Attention in Rodents: Pharmacology and the Role of the

School of Health Sciences

Attention in Rodents:

Pharmacology and the Role of the

Frontoparietal Network

A thesis submitted to The University of Manchester for the

degree of Doctor of Philosophy in the Faculty of Biology,

Medicine and Health

Andrew James Hayward

2017

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Table of Contents List of Figures ....................................................................................................................................... 5

List of Tables ........................................................................................................................................ 6

Abbreviations ....................................................................................................................................... 7

Thesis Abstract ..................................................................................................................................... 9

Declaration ........................................................................................................................................ 10

Copyright ........................................................................................................................................... 10

Acknowledgements ............................................................................................................................ 11

1 GENERAL INTRODUCTION .................................................................................................................. 12

1.1 CONTEXT OF THESIS ......................................................................................................................... 12

1.2 ATTENTION .................................................................................................................................... 12

1.2.1 History of attention ............................................................................................................. 12

1.2.2 How attention is used.......................................................................................................... 13

1.2.3 Neurobiology of attention ................................................................................................... 14

1.2.4 Neurotransmitters of attention ........................................................................................... 15

1.2.5 Pharmacology of attention .................................................................................................. 16

1.2.6 Neural Oscillations .............................................................................................................. 18

1.3 ANIMAL MODELLING ....................................................................................................................... 19

1.3.1 Abstract .............................................................................................................................. 21

1.3.2 Introduction ........................................................................................................................ 22 1.3.2.1 Value of Animal Models and the Need for Improved Translation..................................................... 24 1.3.2.2 Measuring Attention and Impulsive Action in Rodents .................................................................... 25

1.3.3 Methods of Separation ........................................................................................................ 26 1.3.3.1 Models of High Impulsive Action .................................................................................................... 26 1.3.3.2 Models of Low Attention ............................................................................................................... 28 1.3.3.3 Models Most Relevant to ADHD Symptomatology .......................................................................... 29

1.3.4 Neurobiology Identified Using Separation Methods ............................................................. 33 1.3.4.1 Using Impulsive Models ................................................................................................................. 33

1.3.4.1.1 Behaviour ................................................................................................................................ 33 1.3.4.2 Pharmacology/ Neurobiology......................................................................................................... 34 1.3.4.3 Using Models of Attention ............................................................................................................. 36 1.3.4.4 Using Models of ADHD................................................................................................................... 37 1.3.4.5 Using Models developed in 5C-CPT ................................................................................................ 37

1.3.5 Selection of the most appropriate method of separation ...................................................... 44

1.3.6 Conclusion .......................................................................................................................... 45

1.4 GENERAL AIMS OF THESIS ................................................................................................................. 46

2 GENERAL METHODS .......................................................................................................................... 48

2.1.1 Animals ............................................................................................................................... 48

2.1.2 Animal Usage ...................................................................................................................... 48

2.1.3 Apparatus ........................................................................................................................... 49

2.1.4 5C-CPT Task Structure ......................................................................................................... 50

2.1.5 5C-CPT Training................................................................................................................... 51

2.1.1 Signal Detection Theory....................................................................................................... 53

2.1.2 Variable Stimulus Duration .................................................................................................. 55

2.1.3 Division into subgroups ....................................................................................................... 56

3 PHARMACOLOGICAL VALIDATION OF A REFINED LOW ATTENTIVE MODEL USING METHYLPHENIDATE

AND ATOMOXETINE .................................................................................................................................. 57

3.1 CONTRIBUTIONS ............................................................................................................................. 57

3.2 CONTEXT ...................................................................................................................................... 58

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3.3 ABSTRACT ..................................................................................................................................... 58

3.4 INTRODUCTION ............................................................................................................................... 59

3.5 METHODS ..................................................................................................................................... 60

3.5.1 Animals ............................................................................................................................... 60

3.5.2 Apparatus ........................................................................................................................... 61

3.5.3 5C-CPT Training Procedure .................................................................................................. 61

3.5.4 Testing Procedure ............................................................................................................... 61

3.5.5 Division into Subgroups ....................................................................................................... 61

3.5.6 Statistical Analysis............................................................................................................... 62

3.6 RESULTS ....................................................................................................................................... 63

3.7 METHYLPHENIDATE ......................................................................................................................... 63

3.7.1 Group Performance ............................................................................................................. 63

3.7.2 High/ Low Comparison ........................................................................................................ 64

3.8 ATOMOXETINE ............................................................................................................................... 67



3.9 DISCUSSION ................................................................................................................................... 71

4 NICOTINE AND CAFFEINE IN THE 5 CHOICE CONTINUOUS PERFORMANCE TASK ............................... 73

4.1 CONTRIBUTIONS ............................................................................................................................. 73

4.2 CONTEXT ...................................................................................................................................... 74

4.3 ABSTRACT ..................................................................................................................................... 74

4.4 INTRODUCTION ............................................................................................................................... 75

4.5 EXPERIMENTAL PROCEDURES ............................................................................................................. 76

4.6 ANIMALS....................................................................................................................................... 76

4.6.1 Apparatus ........................................................................................................................... 76





4.7 RESULTS ....................................................................................................................................... 78

4.7.1 Whole Group Baseline ......................................................................................................... 78

4.7.2 High/Low Comparison ......................................................................................................... 80

4.8 DISCUSSION ................................................................................................................................... 85

5 PARTIAL AGONISM AT THE Α7 NICOTINIC ACETYLCHOLINE RECEPTOR IMPROVES ATTENTION,

IMPULSIVE ACTION AND VIGILANCE IN LOW ATTENTIVE RATS ................................................................. 88

5.1 CONTRIBUTIONS ............................................................................................................................. 88

5.2 CONTEXT ...................................................................................................................................... 89

5.3 ABSTRACT ..................................................................................................................................... 89

5.4 INTRODUCTION ............................................................................................................................... 90

5.5 EXPERIMENTAL PROCEDURES ............................................................................................................. 92

5.5.1 Animals ............................................................................................................................... 92

5.5.2 Apparatus ........................................................................................................................... 92





5.6 RESULTS ....................................................................................................................................... 94



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5.7 DISCUSSION ................................................................................................................................. 100

6 FRONTOPARIETAL NEURAL ACTIVATION CORRELATES WITH VIGILANCE IN THE 5C-CPT .................. 104

6.1 CONTRIBUTIONS ........................................................................................................................... 104

6.2 CONTEXT .................................................................................................................................... 105

6.3 ABSTRACT ................................................................................................................................... 105

6.4 INTRODUCTION ............................................................................................................................. 106

6.5 METHODS ................................................................................................................................... 107

6.5.1 Animals ............................................................................................................................. 107

6.5.2 5C-CPT Apparatus, Training and Testing ............................................................................ 107

6.5.3 Tissue Extraction ............................................................................................................... 108

6.5.4 Immunohistochemistry ...................................................................................................... 108

6.5.5 Quantification ................................................................................................................... 110

6.5.6 Statistical Analysis............................................................................................................. 110

6.6 RESULTS ..................................................................................................................................... 112

6.7 DISCUSSION ................................................................................................................................. 113

7 OSCILLATIONS IN COGNITION: AN ATTENTION-PROMOTING DOSE OF METHYLPHENIDATE

AUGMENTS ALPHA RHYTHM IN THE DORSAL ATTENTION NETWORK ..................................................... 116

7.1 CONTRIBUTIONS ........................................................................................................................... 116

7.2 CONTEXT .................................................................................................................................... 117

7.3 ABSTRACT ................................................................................................................................... 117

7.4 INTRODUCTION ............................................................................................................................. 118

7.5 METHODS ................................................................................................................................... 119

7.5.1 Animals ............................................................................................................................. 119

7.5.2 Surgery ............................................................................................................................. 119

7.5.3 Recording.......................................................................................................................... 119

7.5.4 Histology........................................................................................................................... 120

7.5.5 Analysis ............................................................................................................................ 120

7.6 RESULTS ..................................................................................................................................... 120

7.7 DISCUSSION ................................................................................................................................. 123

8 GENERAL DISCUSSION ..................................................................................................................... 126

8.1 AIM 1: TO FURTHER DEVELOP A METHOD OF MODELLING ATTENTION DEFICITS IN RATS. .................................. 126

8.2 AIM 2: TO USE THE HIGH AND LOW ATTENTIVE MODEL TO STUDY THE PHARMACOLOGY OF ATTENTION ................ 127

8.3 AIM 3: TO EXAMINE THE INVOLVEMENT OF THE FRONTOPARIETAL NETWORK IN PERFORMANCE OF THE 5C-CPT AND

EFFECTS OF ATTENTION PROMOTING AGENTS ................................................................................................... 128

8.4 LIMITATIONS AND FUTURE RESEARCH ................................................................................................. 129

8.5 CONCLUSION................................................................................................................................ 132

9 REFERENCES .................................................................................................................................... 133

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Word Count: 39,692

List of Figures

Figure 1.1: Brain regions involved in alerting, orienting and executive networks. .......................... 15 Figure 1.2: The 5 Choice Serial Reaction Time Task vs 5 Choice Continuous Performance Task 26 Figure 1.3: Baseline separation of HI-filled circles/LI-empty circle animals using the Dalley et al.

(2007) method. ............................................................................................................................ 27 Figure 1.4: a dimensional analysis method of performance separation ......................................... 30 Figure 2.1: Animals used for 5C-CPT experiments and the order of use ...................................... 49 Figure 2.2: 5C-CPT Apparatus for training and pharmacological testing ....................................... 49 Figure 2.3: 5C-CPT Task Structure. ............................................................................................. 50 Figure 2.4: Representative progression of training. ...................................................................... 53 Figure 3.1: Methylphenidate increased accuracy at 0.5 and 1 mg/kg in LA animals. ..................... 65 Figure 3.2: Vigilance is increased by 0.5 and 1 mg/kg in LA animals. ........................................... 66 Figure 3.3: No significant differences were seen on the latency measures due to methylphenidate.

(A) No significant differences in correct latency due to methylphenidate. ...................................... 67 Figure 3.4: In LA animals Accuracy is increased and premature responses reduced by

atomoxetine. ............................................................................................................................... 69 Figure 3.5: Atomoxetine reduced d′ in HA animals, but increased it in LA animals ........................ 70 Figure 3.6: Atomoxetine had no significant effects on the latency measures in HA or LA animals. 71 Figure 4.1: Comparison of accuracy and d′ after vehicle treatment in HA and LA groups when

grouped by these parameters ...................................................................................................... 81 Figure 4.2: Performance measures of the go trials in 5C-CPT when treated with saline, nicotine

(0.1 and 0.2 mg/kg) and caffeine (2.5 and 5 mg/kg) ..................................................................... 82 Figure 4.3: Signal Detection Theory related measures of the 5C-CPT .......................................... 83 Figure 4.4: Latency measures of the 5C-CPT following treatment with vehicle, nicotine (0.1 and 0.2

mg/kg) or caffeine (2.5 and 5 mg/kg). .......................................................................................... 84 Figure 4.5: Number of trials completed per 20 minutes of the 5C-CPT. ........................................ 84 Figure 5.1: Group differences between high and low attentive groups (HA/LA) at increasing SDs. 97 Figure 5.2: Encenicline improved selective attention and vigilance in low attentive (LA) animals

only. ............................................................................................................................................ 97 Figure 5.3: Encenecline reduces impulsivity in LA animals at 0.75 s SD. In HA animals pFA (A)

trended to significance for 0.75 s SD and 0.1 mg/kg (p=0.081). ................................................... 99 Figure 5.4: Latency measures of the 5C-CPT showed no significant TREATMENT*GROUP

interaction. ................................................................................................................................ 100 Figure 6.1: Regions analysed for C-Fos quantification ............................................................... 109 Figure 6.2: Correlation between count order and the number of identified c-Fos-positive neurones.

................................................................................................................................................. 111 Figure 6.3: C-Fos-positive cell count was higher in PPC compared to PFC. ............................... 112 Figure 6.4: Vigilance (d′) correlates positively with the number of c-Fos-positive cells in mPFC and

PPC .......................................................................................................................................... 113 Figure 7.1: representative power spectral densitiy for saline recording. ...................................... 121 Figure 7.2: Methylphenidate takes 20-40 minutes to cause changes in the neural oscillations .... 122 Figure 7.3: Methylphenidate significantly increases power in the alpha band, particularly in the

mPFC, while reducing power at lower frequencies. .................................................................... 123

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List of Tables

Table 1.1: Summary of published methods for modelling impulsivity, attention and combined

symptom subtypes using performance based separation techniques. .......................................... 31 Table 1.2: Summary of pharmacological effects in models based on behavioural separation........ 39 Table 2.1: Training stages for 5C-CPT. ........................................................................................ 52 Table 2.2: Summary of measures in the 5C-CPT ......................................................................... 54 Table 3.1: Group vehicle treated baseline before HA/LA grouping for methylphenidate ................ 63 Table 3.2: Group vehicle treated baseline before HA/LA grouping for atomoxetine....................... 67 Table 4.1: Whole cohort 5C-CPT performance following vehicle compared to nicotine and caffeine

................................................................................................................................................... 79 Table 5.1: Effect of encenicline on the whole cohort..................................................................... 95

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Abbreviations

Abbreviation Meaning

5C-CPT 5 Choice Continuous Performance

5C-SRTT 5 Choice Serial Reaction Time Task

5-HT 5-Hydroxytriptamine

Acc Accuracy

ADHD Attention Deficit/Hyperactivity Disorder

ADHD-C Attention Deficit/Hyperactivity Disorder Combined Subtype

ADHD-HI Attention Deficit/Hyperactivity Disorder Hyperactive/Impulsive Subtype

ADHD-I Attention Deficit/Hyperactivity Disorder Inattentive Subtype

ANOVA Analysis of Covariance

BOLD Blood Oxygen Level Dependent

BSA Bovine Serum Albumin

Cg1 Cingulate Cortex 1

COMT Catechol-O-Methyl Transferase

CPT Continuous Performance Task

d′ D Prime

DAT Dopamine Transporter

DRD4 Dopamine D4 Receptor

E Efficent Performers

EEG Electroencephalography

fMRI Functional Magentic Resonance Imaging

GABA Gamma Aminobutyric Acid

HA High Attentive

HI High Impulsive

HP High Performance

IA Inattentive

IA-I Inattentive-Inpulsive

ITI Inter-Trial Interval

LA Low Attentive

LC Locus Coeruleus

LFP Local Field Potential

LH Limited Hold

LSD Lest Squared Difference

M Moderate Performers

MANCOVA Multivariate Analysis of Covariance

mPFC Medial Prefrontal Cortex

MPH Methylphenidate

MPtA Medial Parietal Associative Cortex

NAc Nucleus Accumbens

NAcC Nucleus Accumbens Core

nAChR Nicotinic Acetylcholine Receptor

NAcS Nucleus Accumbens Shell

NAT Noradrenaline Transporter

NICE National Institute of Clinical Excellence

OFC Orbital Frontal Cortex

PBS Phosphate Buffered Saline

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PBS-T Phosphate Buffered Saline with Triton

pFA Probability of False Alarms

PFC Prefrontal Cortex

pHR Probability Hit Rate

PPC Posterior Parietal Cortex

PR Premature Responses

PrL Prelimbic Cortex

RDoC Research Domain Criteria

RI Responsivity Index

SD Stimulus Duration

SI Sensitivity Index

vITI Variable Inter-Trial Interval

vSD Variable Stimulus Duration

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Thesis Abstract

The recent high profile Phase III failures of new drugs to treat psychiatric disorders demonstrated that there is a clear need to improve translation of pre-clinical findings. The Research Domain Criteria (RDoC) proposes that this can be achieved by focusing on trans-diagnostic criteria. A good example of this is inattention, which is prevalent in conditions such as schizophrenia, Alzheimer‟s disease and attention deficit hyperactivity disorder (ADHD). Validating and refining novel and existing models of inattention will facilitate development of new therapeutic strategies. The work presented in thesis refines a rat model of low attention to increase its translational potential for investigating attention promoting compounds. RDoC also suggests that research at multiple levels of analysis is essential. Therefore, this work also investigates involvement of the frontoparietal network in attention using electrophysiology and cFos techniques ex vivo. This thesis first refines a 5 choice-continuous performance task (5C-CPT) method to group rats based on attentional performance by using a more translational calculation of vigilance (d′) alongside stricter separation criteria. This method demonstrated pharmacological validity by replicating previous effects of methylphenidate and atomoxetine in low attentive (LA) rats. It also showed enhanced translational potential by revealing reduced omissions (increased sustained attention) in LA rats, which is an important clinical symptom that the previous method failed to uncover. Nicotine and caffeine, which improve sustained attention in humans, were assessed in high and low attentive animals. Both compounds increased impulsivity (probability of false alarms (pFA)), irrespective of attentive grouping. However, these impulsive symptoms may have been caused by off-target effects. This was controlled for by testing the selective α7 nicotinic acetylcholine receptor partial agonist encenicline. The rationale for this was that knockout of the α7 nicotinic acetylcholine receptor promotes an inattentive phenotype in mice; therefore, encenicline was studied for its potential to improve attention. Encenicline improved accuracy, d′ and pFA, in LA rats, showing the potential of this target to improve attention, vigilance and impulsivity, respectively. Therefore, it is proposed that partial agonists at the α7 nicotinic acetylcholine receptor warrant further investigation for inattention symptoms. Connections between the prefrontal cortex (PFC) and the posterior parietal cortex (PPC) form the frontoparietal network, activated in humans during tasks that require attention. Subsequent studies in this thesis investigated the involvement of this network in 5C-CPT performance by using the immediate early gene c-Fos, a marker for neuronal activation. The number of c-Fos-positive neurones in PFC and PPC positively correlated with task engagement/vigilance (d′). This finding further supports involvement of these regions in sustained attention, as well as the utility of the 5C-CPT as a highly translatable task. Electroencephalography in patients has shown that the attention-promoting dopamine re-uptake inhibitor methylphenidate (Ritalin), increases alpha oscillations in frontoparietal regions as well as increasing beta in frontal regions, which correlates with improved performance in attentive tasks. In the final study, local changes in low-frequency electrical oscillations of the PFC and PPC of the rat following an acute attention-promoting dose (1 mg/kg) of methylphenidate under urethane anaesthesia were analysed. Methylphenidate promoted alpha oscillations (8-12 Hz) in the PFC and high gamma oscillations (55-90 Hz) in the PPC and reduced delta (1-4 Hz), theta (4-8 Hz) and high gamma in the PFC. Alpha is linked to the suppression of irrelevant and selection of salient stimuli, which may be a mechanism by which methylphenidate promotes an attentive state. Clinical studies have positively correlated frontal theta to ADHD symptom severity and it is thought to be linked to underutilisation of attention. Therefore, reductions of theta in this experiment may also be associated with methylphenidate‟s ability to promote attention in the 5C-CPT. In conclusion, these studies support the use of a refined 5C-CPT as a translational task of relevance to conditions with inattention as a symptom, such as ADHD, schizophrenia and Alzheimer‟s disease. This thesis offers guidance for future work into how high and low attention in rodents interacts with pharmacological agents and the effects of encenicline show the potential of selective nicotinic α7 agonists to improve attention. The presented work also shows that methylphenidate improves inattention and promotes alpha band oscillations in the PFC, which is a biomarker in need of further study. Additional research is needed to link these oscillations in anaesthetized animals to specific behaviours, but the work outlined here provides important progress towards understanding the interaction between inattention, pharmacology and the frontoparietal network.

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Declaration

No portion of the work referred to in the thesis has been submitted in support of an application for

another degree or qualification of this or any other university or other institute of learning

Copyright

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thesis) owns certain copyright or related rights in it (the “Copyright”) and s/he has

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including for administrative purposes.

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iii. The ownership of certain Copyright, patents, designs, trademarks and other

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copyright works in the thesis, for example graphs and tables (“Reproductions”),

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owner(s) of the relevant Intellectual Property and/or Reproductions.

iv. Further information on the conditions under which disclosure, publication and

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(see http://documents.manchester.ac.uk/DocuInfo.aspx?DocID=24420), in any

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on Presentation of Theses

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Acknowledgements

Firstly I would like to thank my supervisors Joanna C. Neill, John Gigg and Michael Harte their

support and guidence has been invaluable throughout my PhD.

I would also like to thank members of Jo Neill, John Gigg and Michael Harte‟s labs including: Lisa

Adamson, Anneka Tomlinson, Chloe Piercy, Nazanin Doostdar, Ben Grayson, Sam Marsh,

Marianne Leger, Katie Murry, Giovanni Podda, Lisa Heany, William Watremez, Joshua Jackson,

Joanna Oladipo, Daniel Squirrel, Sander Tanni, Maria Constantinou and Ines Das Neves for their

support and assistance. This work would not have been possible without them and the support of

the biological services facility (BSF) and the University of Manchester more broadly. Additionally, I

would like to thank Rayna Samuels of Prof. Hugh Piggins‟ lab for her advice during the C-Fos

project and Dr Jaleel Miyan for his advice and support while writing my thesis.

More personally I thank my friends for their emotional support throughout the entire process,

particularly Oktawia Borecka who has been a tireless proof reader and emotional support

throughout.

I would not have gotten to this stage without the lifelong support of my parents, sister and wider

family. Thank you all for your help and support.

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1 General Introduction

1.1 Context of Thesis

The failure of projects after many years in development has cost pharmaceutical companies

millions and moved many drug companies‟ priorities for drug discovery away from psychiatric

research (Insel and Sahakian, 2012, Nutt and Goodwin, 2011, Neill and Hendrie, 2012). This is

particularly problematic for psychiatric conditions as they are difficult to treat, leading to patients‟

needs not being fully met. For these reasons, the National Institute of Mental Health has produced

the Research Domain Criteria (RDoC) to identify transdiagnostic domains of mental health (Young

et al., 2016). Through a multilevel analysis of cells, circuitry and genes this allows research to

move away from grouping by a collection of symptoms and instead enables analysis of the

common mechanisms involved. Attention is one such domain outlined by RDoC, where the study of

attention deficits can be applied to multiple conditions such as schizophrenia (Luck and Gold, 2008,

Orzack and Kornetsky, 1966, Phillips et al., 2015), attention deficit hyperactivity disorder (ADHD)

(Bernfort et al., 2008, Epstein et al., 2003, Kooij et al., 2010), anorexia nervosa (Dalmaso et al.,

2015) and neurodegenerative disorders (Sharma et al., 2016) including Alzheimer‟s disease (Foldi

et al., 2002, Perry et al., 2000, Vasquez et al., 2011). This thesis, therefore, focuses on developing

a translatable model of inattention in rodents and the application of this to pharmacological studies.

The work then progresses to studying the functional neurobiology of attention and applies this to

study the effects of putative pro-attentive compounds.

1.2 Attention

To survive, organisms have developed many ways to detect changes in the environment. Due to

limited neural resources, this information cannot all enter conscious awareness. Attention is the

filter used to prioritise information adaptively (Petersen and Posner, 2012). How this filter works is a

fundamental issue to understand, firstly due to its role in shaping our perception of the world and

secondly due to the involvement of inattention in many neuropsychiatric conditions such as

schizophrenia, Alzheimer‟s disease and ADHD.

1.2.1 History of attention

Early research into visual attention by Broadbent (1958) proposed a dual-mechanism hypothesis in

humans with two stages; spatial and feature-based processing (Mazer, 2011). Spatial processing

extracts low-level detail such as colour, luminescence and orientation and is fairly effortless.

However, to extract high-level details such as facial features, a more focused approach is needed.

Feature-based processing allows small details to be picked out from a cluttered environment.

Feature-based attention was later developed by Treisman and Gelade (1980) to the “attention

spotlight theory”. This theory proposed a restricted zone of heightened visual processing which

could move independently of eye movement (Mazer, 2011). The spotlight could also be moved by

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conscious (top-down) control to build an awareness of an environment. In contrast, external

warning signals can cause a bottom-up control system to widen the spotlight.

One important distinction between the models of Broadbent (1958) and Treisman and Gelade

(1980) is the point at which information is filtered. Broadbent (1958) suggested that, as perception

is limited, low-level details are essential to filter at the early stages in the process and suppression

of stimuli is achieved by not perceiving them initially (Lachter et al., 2004). However, the Treisman

and Gelade (1980) model argues that perception is not the “bottleneck” and that large amounts of

data is perceived, but does not enter conscious awareness due to higher level cognitive filters. By

cognitive filtering they allow for a change in the size of the attention spotlight, which happens in

periods of heightened alertness. A wealth of studies now show that it is, in fact, a combination of

the two models in which attention can be filtered early or late depending on the stimulus (Murphy et

al., 2016). Stimuli that are either complex or similar to distractors are said to have a high perceptual

load. In low perceptual load, moving of the spotlight is sufficient, but if a high perceptual load is

encountered then cognitive filters have to be used to alter the size of the spotlight by cognitively

filtering out the irrelevant information.

Petersen and Posner (2012) bring together these ideas into a three-part process. Firstly, an

alerting signal controls arousal of attention to a state prepared to detect an upcoming signal.

Alerting is particularly evident when a warning signal is used and increases the reaction time and

accuracy of the participant and can be thought of as a temporary increase in the limit of perceptual

load. Secondly, orienting is the process of using internal (top-down) and external (bottom-up)

information to orient attention to the required stimulus. Shifts of the spotlight can occur either with

(overt) or without (covert) corresponding eye movement. Finally, the executive stage is where the

attended information can be cognitively filtered before entering conscious awareness. Executive

networks are also the site of top-down control and conflict monitoring for the other two stages.

While the three stages are presented as separate, there is evidence that interaction between them

is essential for goal-directed behaviours (Xuan et al., 2016).

1.2.2 How attention is used

The widely used terms of selective, sustained and divided attention, as well as vigilance, refer to

the pattern of use of the attentional spotlight. Focusing the brain‟s resources to a single stimulus

can be advantageous to goal-directed behaviour (Robbins, 2002). Selective attention allows us to

ignore task-irrelevant and focus on task-relevant stimuli. An example of this is managing to ignore a

noisy surrounding and focus on and listen to an individual‟s voice in a crowd (the cocktail party

effect; Treisman and Gelade (1980)). Selectivity is achieved by a dual mechanism of enhancing the

representation of relevant stimuli and inhibiting irrelevant stimuli (Chelazzi et al., 2013, Shioiri et al.,

2016, Klimesch, 2012).

The environment also often requires prolonged periods of attention. This is accounted for by two

forms of attention adaptation (Egeland et al., 2009). Firstly, sustained attention allows a focus to be

maintained for long periods, for example when reading a book (Carter et al., 2013). The second

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form is vigilance, which refers to the ability to remain alert toward incoming unpredictable stimuli

(Egeland et al., 2009). This state of arousal can be induced by an initial unexpected stimulus,

termed a warning signal (Petersen and Posner, 2012). An example could be, if a deer sees

movement in the bushes, it would enter a vigilant state to be prepared for further sensory input that

would warn of danger from a predator. Vigilance is also used to search for rare but important

events such as in the classic studies of Mackworth (1948) assessing the performance of World

War II radar operators.

Finally divided attention, also commonly called multi-tasking, is the ability to pay attention to

multiple stimuli at once (Finley et al., 2014). An example of this is, being able to walk and hold a

conversation. However, as attention is limited, this involves rapidly reorienting attention between

the stimuli and severely decreases performance compared to selective attention. A prominent

example of this is using a mobile phone while driving, which significantly increases reaction times

and chances of accident even if used in a hands-free mode (Horrey and Wickens, 2006).

1.2.3 Neurobiology of attention

The locus coeruleus (LC) is a key region in the alerting of attention (Howells et al., 2012, Aston-

Jones and Cohen, 2005, Aston-Jones et al., 1997). Neurones of this region usually fire tonically at

a rate of around 1-3 Hz. When a warning signal is detected, they enter a phasic state of firing which

can promote a prolonged fixed focus (sustained attention). This is possibly due to the broad

noradrenergic innervation to much of the neocortex, particularly the prefrontal (PFC) and posterior

parietal (PPC) cortices (Aston-Jones and Cohen, 2005). It is by balancing tonic and phasic states

that the locus coeruleus is thought to actively control attention by adjusting the synaptic gain across

the cortex (Howells et al., 2012). This optimisation can be modelled as an inverted U-shaped

relationship between locus coeruleus activity and behavioural performance (Aston-Jones and

Cohen, 2005). There is evidence that the error monitoring processes of the anterior cingulate

cortex can modulate LC neuronal firing mode allowing continuous adaptive optimisation of arousal

(Petersen and Posner, 2012, Aston-Jones and Cohen, 2005, Cohen et al., 2000).

Stimuli can be split into two categories, top down/internal cues (e.g. memory) and bottom

up/external cues (e.g, sudden movement). Attention is oriented differently for these two categories.

Corbetta and Shulman (2002) used functional magnetic resonance imaging (fMRI) to show that

there are two orienting systems (Petersen and Posner, 2012). The ventral system includes the

ventral frontal cortex and the temporoparietal junction and is activated by external cues. For

memory related internal cues, Corbetta and Shulman (2002) found the intraparietal sulcus, superior

parietal lobe and the frontal eye fields were activated and termed this the dorsal attention network.

There are two current theories about the anatomy of the executive control in attention (Petersen

and Posner, 2012). The first suggests that the anterior cingulate cortex monitors conflict and the

lateral frontal areas resolve conflict when found (Botvinick et al., 2001). The alternate theory argues

15

that there are two top-down networks controlling executive function (Dosenbach et al., 2007,

Dosenbach et al., 2008). Top down networks include the frontoparietal network made up of the

dorsolateral prefrontal cortex and posterior parietal cortex. This network shows activation in start-

cue and error-related activity and may initiate and adapt control on a trial-by-trial basis. The second

is the cingulo-opercular, including the dorsal anterior cingulate/medial superior frontal cortex,

anterior insula/frontal operculum, and anterior prefrontal cortex. The network seems to show

activation in stabilising and maintenance of focus.

Figure 1.1: Brain regions involved in (a) alerting, (b) orienting and (c) executive networks. (a) Projections of the locus coeruleus in the macaque. Dense projections are sent to the frontal and parietal cortices. (b) The intraparietal sulcus/superior parietal lobe (IPS/SPL) and frontal eye fields (FEF) comprise the dorsal orienting network. The ventral orienting system consists of regions in the temporoparietal junction (TPJ) and the ventral frontal cortex (VFC). (c) In yellow are regions of the frontoparietal network: dorsolateral and anterior prefrontal cortex (dlPFC and aPFC), the dorsal frontal cortex (dFC), the IPS and the intraparietal lobe (IPL). In black are the regions of the cingulo-opercular system: dorsal anterior cingulate cortex (dACC), thalamus and anterior PFC (aPFC). Figure is taken from Petersen and Posner (2012)

1.2.4 Neurotransmitters of attention

The alerting signal derives from the locus coeruleus, which is the main course for noradrenergic

innervation in the brain (Aston-Jones and Cohen, 2005). In an awake state LC neurones fire action

potentials tonically, but to promote sustained attention they switch to phasic firing. Phasic firing

releases more noradrenaline throughout the cortex than tonic firing, particularly the prefrontal and

parietal cortices. It has been proposed that noradrenaline release increases the signal to noise

ratio of the attention system allowing rapid orienting, then sustained attention (Aston-Jones and

Cohen, 2005, Sara and Bouret, 2012). There is also a relationship between task performance and

levels of tonic activity in the LC (Aston-Jones and Cohen, 2005). Low levels of tonic activity

promote inattention, increasing tonic activity improves performance in attentive tasks by promoting

task engagement to an optimal point, beyond the optimal point increases of tonic activity promote a

distractible state that reduces performance. It is also essential to have a balance between phasic

and tonic firing for optimal performance (Howells et al., 2012).

Dopamine neurotransmission has a well-established role in reward and control of motivation

(Bromberg-Martin et al., 2010). In this role, dopamine release enhances synaptic plasticity to

reinforce firing patterns that result in positive outcomes (e.g. food). However, it has more recently

been suggested that this role in synaptic plasticity can also be used in response to non-rewarding

16

and aversive stimuli (Ventura et al., 2007, Bromberg-Martin et al., 2010). This would suggest that

rather than being a reward signal, dopamine signals motivational salience. In agreement with this,

different populations of dopamine neurones have been found to respond to positive or both positive

and negative events (Brischoux et al., 2009, Bromberg-Martin et al., 2010, Matsumoto and

Hikosaka, 2009). These can be considered as value-coding dopamine neurones and salience-

coding dopamine neurones, respectively. These signals for motivational salience are involved in

orienting attention, particularly if rewards are expected (Anderson et al., 2016).

Increases of serotonin (5-Hydroxytryptamine; 5-HT) have been linked to the reduction of vigilance.

Selective serotonin reuptake inhibitors are commonly used for the treatment of depression, but

have also been shown to reduce vigilance in humans performing the Mackworth clock task. An

functional magnetic resonance imaging (fMRI) study found that these compounds act on the frontal

cortex, which is a key area for attention. The exact mechanism by which serotonin modulates

attention is not clear, but it may be through interaction with the 5-HT2 receptors (e.g., 5-HT2a),

which causes indirect changes in dopamine neurotransmission (Wingen et al., 2007).

Acetylcholine is also important in attention; this is shown by lesions of cholinergic input from the

basal forebrain to the prefrontal cortex promoting distractibility and reducing performance under

high attentional demand in rodents (Dalley et al., 2004b, Newman and McGaughy, 2008). This is

further reinforced by detection of changes in acetylcholine levels during tasks of attention (Parikh et

al., 2007). As these changes can be detected broadly and transiently, acetylcholine neurones may

also fire either tonically and phasically, but further research is needed.

Local injection of GABAA agonists, antagonists and inverse agonists into the PFC impairs accuracy

and increases omissions in rats performing the 5 choice-serial reaction time task (5C-SRTT)

(Murphy et al., 2012, Paine et al., 2011, Pehrson et al., 2013, Pezze et al., 2014). This shows that

Gamma aminobutyric acid (GABA) neurotransmission is involved in attention and that, like

dopamine and noradrenaline, it may operate as an inverted U-shaped relationship with attention.

The mechanism of GABA neurotransmission in attention is not yet fully understood but may be due

to dysregulation of the dopaminergic system, (Pezze et al., 2014).

1.2.5 Pharmacology of attention

In ADHD, inattention is a core symptom and, therefore, the treatments for this condition are largely

aimed at improving attention. Methylphenidate and amphetamine salts are first and third line

treatments in the National Institute of Clinical Excellence (NICE) guidelines, respectively (Palanivel

et al., 2009). There is a large body of evidence that these stimulants are very effective in children

with ADHD with an improvement of inattentive symptoms in around 75% of patients (Faraone and

Antshel, 2008, Leucht et al., 2012, Pietrzak et al., 2006, Schachter et al., 2001).

Amphetamine salts such as d-amphetamine (dextroamphetamine) and lisdexamphetamine are

efficacious but are used less due to adverse effects and abuse liability (Heal et al., 2009, Heal et

al., 2013). The main side effects encountered include loss of appetite, insomnia, and abdominal

17

pain (Punja et al., 2016). D-amphetamine inhibits dopamine and noradrenaline transporters, but

also increases the release of these neurotransmitters and inhibits monoamine oxidase, which

degrades them. Methylphenidate selectively inhibits the dopamine and noradrenaline reuptake

transporters to increases the length of time that these neurotransmitters are present at synapses

after release (Heal et al., 2009). Through stimulating dopamine and noradrenaline

neurotransmission, stimulants can promote resting state connectivity of attention-related networks,

such as the frontoparietal network and connections from the visual cortex to the frontoparietal and

executive networks (Mueller et al., 2014, An et al., 2013). Stimulants also increase blood flow to the

frontoparietal network when performing attention and working memory-based tasks (Tomasi et al.,

2011).

The second-line treatment for ADHD is the non-stimulant, atomoxetine, which alleviates impulsive

measures in the continuous performance, stop signal and delay discounting tasks in humans (Barry

et al., 2009, Chamberlain et al., 2006). However, for attentive measures, response rates and effect

sizes are lower than for stimulants at around 64% of ADHD patients (Heal et al., 2009, Cunill et al.,

2013). Atomoxetine is a selective inhibitor of the noradrenaline reuptake transporter. As the main

source of noradrenaline in the brain, the LC is an important site of action for atomoxetine, as well

as the previously mentioned compounds. Via action at this site, noradrenaline can promote both

spontaneous and sensory-evoked discharges of the LC neurones in the rat, thus, increasing phasic

firing and reducing tonic firing (Bari and Aston-Jones, 2013). This causes arousal of attention and

an increase in selective attention seen in many studies using this compound (Tomlinson et al.,

2014, Blondeau and Dellu-Hagedorn, 2007, Cunill et al., 2013, Heal et al., 2009). However, this is

not the entire picture, as atomoxetine administered locally to the PFC still promotes noradrenaline

and dopamine release, suggesting that it directly and indirectly alters neurotransmission in the PFC

(Bymaster et al., 2002).

The α2 adrenoceptor is abundant in the LC and the PFC and blockade of this receptor results in

increased distractibility and prevents the improvement of attention by atomoxetine (Arnsten et al.,

2007). Guanfacine and clonidine are α2 adrenoceptor agonists that can improve inattentive and

impulsive symptoms in ADHD (Bolea-Alamanac et al., 2014, Huss et al., 2016). Less commonly

used treatments include modafinil, desipramine and bupropion (Minzenberg, 2012, Palanivel et al.,

2009). These drugs are not recommended by NICE due to a poor understanding of how they

improve attention.

In schizophrenia, the majority of treatments focus on the positive symptoms with the cognitive

symptoms being poorly treated to date. However, the atypical antipsychotic clozapine is also able

to improve cognitive symptoms including inattention. The improvement in attention is due to the

increase in the ability to orient attention (Spagna et al., 2015). Clozapine has a complex

pharmacology with binding affinity to dopamine (D1, D2 and D4), serotonin (5-HT2a and 2C),

noradrenaline (α1) and cholinergic (M1 and M5) receptors (Baviera et al., 2008). It is therefore not

clear whether modulation of single or multiple transmission systems/ receptors mediate this

improvement in the orienting of attention (Spagna et al., 2015).

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Donepezil, galantamine and rivastigmine are inhibitors of cholinesterase. As cholinesterase is an

enzyme that degrades acetylcholine, its inhibition increases acetylcholine availability (Sharma et

al., 2016). Cholinesterase inhibitors are regularly used for Alzheimer's disease to reduce symptoms

of memory loss and inattention (Lee et al., 2015). It is effective at reducing cognitive decline across

the spectrum of Alzheimer‟s disease progression, but has a limited duration of effectiveness (Lee et

al., 2015).

1.2.6 Neural Oscillations

As attention is a rapid and transient process, it is challenging to study with some imaging methods

such as fMRI/ positron emission tomography (PET) due to their low temporal resolution. As the

brain communicates using instantaneous changes in electrical potential, detection of changes in

electrical activity is the most direct way to measure changes in brain activity. Also, as electricity

propagates at the speed of light, detection of these changes offers insight into patterns and timing

of neuronal firing. The net potential difference of an area of neuronal firing is a local field potential,

which can propagate through the brain, skull and scalp. In electroencephalography (EEG) an array

of scalp-fixed electrodes can record these changes. An event-related potential is an electrical

signal detected by scalp electrodes, which corresponds to an event within a task. However, due to

the various tissues the signal has travelled through it is not possible to locate its source with high

certainty, which is called the „inverse problem‟. There are methods that attempt to overcome this

issue using computationally intense approaches (Castano-Candamil et al., 2015, Grech et al.,

2008). However, these methods often have limitations in spatial or temporal dimensions or may be

insensitive or distorted by non-stationary signal components (Castano-Candamil et al., 2015). The

latter is a particular issue as non-stationary signals are fundamental to neurophysiological

recordings (Woolrich et al., 2013). The computations also rely on estimating the number of

variables such as the number of current dipoles, but this is impossible to know, and the estimations

cannot be validated fully (Grech et al., 2008). Therefore, to study the function of a region,

electrodes need to be inserted into it to record the local field potential (LFP).

As described so far, regions involved in the process of attention are dispersed across the brain.

This raises the question of how they can be dynamically entrained. Due to the rapid timescale of

attention, anatomical changes would be impossible. However, the theory of communication through

coherence suggests that, by synchronising firing of large groups of neurones to form neural

oscillations, two distant regions can transfer information selectively (Fries, 2015). Due to this

observation a large research effort has tried to link oscillations at particular frequencies to a range

of cognitive processes. For attention, the processes of selection of task-relevant stimuli and

suppression of irrelevant stimuli are essential. For inhibition, alpha has been an area of strong

interest (~8-15 Hz) (Jensen and Mazaheri, 2010). Alpha was originally thought to be generated by

the thalamus, but more recent studies have found isolated brain slice preparations containing

cortical pyramidal neurones capable of generating alpha independently of the thalamus (Bollimunta

et al., 2008, Haegens et al., 2015). Regardless of its origin, evidence shows that the presence of

alpha inhibits the firing of neurones (Jensen and Mazaheri, 2010), possibly via the recruitment of

19

local inhibitory neurones (Lorincz et al., 2009). Through inhibitory mechanisms, well-timed alpha

oscillations in the PFC facilitate suppression and selection of stimuli (Klimesch, 2012, Zanto et al.,

2011). Alpha oscillation timing can also be recorded on electrodes over the parietal and occipital

cortices preceding a stimulus, with event-related synchronisation to the alpha band, which

desynchronises upon stimulus presentation to enable shifting of attention (Schack et al., 2005,

Broussard and Givens, 2010, Klimesch, 2012). In agreement with this, (a) more than 60% of inter-

subject variability in perceptual learning can be explained by the relative engagement of alpha

rhythm (Freyer et al., 2013) and (b) children and adults with ADHD show reduced event-related

alpha synchronisation compared to controls (Hasler et al., 2016, Klimesch, 2012). However, as

attention is a complex behaviour, it is unlikely that it is controlled by a single oscillation. Therefore,

the interaction between different frequencies needs to be analysed. Gamma oscillations are one

such consideration (30-90 Hz). This is because alpha power increases during anticipation of a

stimulus, upon stimulus presentation gamma oscillations increase in power as alpha reduces (Fries

et al., 2001, Muller et al., 2000). The increase in gamma power is predictive of task performance

(Siegel et al., 2008). Magnetoencephalography in primate subjects has proven a useful tool in

elucidating the nature of this interaction. A study by Siegel et al. (2008) showed firstly that visual

attention modulates the oscillations found between frontal eye fields, intraparietal sulcus and visual

cortex and secondly that the intraparietal sulcus is the main source of alpha pre-stimulus and

gamma post-stimulus.

1.3 Animal Modelling

The work in this thesis focuses on the modelling of low attention in rodents, and so the following

review paper contextualises the need for animal models, some approaches that have been taken

and how the area can progress in the future.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3441927/

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Low attentive and high impulsive rats: a translational animal model of ADHD and disorders of attention and impulse control

Authors: Andrew Hayward, Anneka Tomlinson and Joanna C Neill

[Published in Pharmacology and Therapetics:

HAYWARD, A., TOMLINSON, A. & NEILL, J. C. 2016. Low attentive and high impulsive rats: A

translational animal model of ADHD and disorders of attention and impulse control. Pharmacol

Ther, 158, 41-51.]

21

1.3.1 Abstract

Many human conditions such as attention deficit hyperactivity disorder (ADHD), schizophrenia and

drug abuse are characterised by deficits in attention and impulse control. Carefully validated animal

models are required to enhance our understanding of the pathophysiology of these disorders,

enabling the development of improved pharmacotherapy. Recent models have attempted to

recreate the psychopathology of these conditions using chemical lesions or genetic manipulations.

In a diverse population, where the aetiology is not fully understood and is multifactorial, these

methods are restricted in their ability to identify novel targets for drug discovery. Two tasks of visual

attention and impulsive action typically used in rodents and based on the human continuous

performance task (CPT) include, the well-established 5 choice serial reaction time task (5C-SRTT)

and the more recently validated, 5 choice continuous performance task (5C-CPT) which provides

enhanced translational value. We suggest that separating animals by behavioural performance into

high and low attention and impulsivity cohorts using established parameters in these tasks offers a

model with enhanced translational value. In this review, methods to separate animals are

compared and the results discussed to highlight advantages over more constrained models, in

addition to potential future directions for enhanced validation. Advantages include reliability,

flexibility and enhanced translation to clinical conditions, all important considerations in modelling

ADHD, schizophrenia and drug abuse, conditions with multifactorial aetiology. Based on the

existing evidence, we suggest that future studies should incorporate an element of behavioural

separation when studying the constructs of visual attention and impulsive action of relevance to

human disorders.

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1.3.2 Introduction

The focus of this review is attention deficit hyperactivity disorder (ADHD) an illness clearly

characterised by abnormal hyperactivity in addition to deficits in attention and impulsivity, although

many of the constructs we discuss are relevant for other disorders such as drug abuse and

schizophrenia.

Psychiatric disorders remain poorly managed and present a large economic burden. Brain

disorders cost €141 billion per annum in the UK, with a total 2010 cost (in million € purchasing

power parity) of €134 (Fineberg et al., 2013). ADHD is often confounded by other conditions such

as generalised anxiety disorder, oppositional defiant disorder and learning difficulties (Larsson et

al., 2011). The estimated direct and indirect costs per child or adolescent patient are estimated to

be €9,860 to €14,483 per year (Le et al., 2014). Of this, between 8-25% are estimated to be direct

healthcare costs, showing that a large proportion of the economic cost is due to indirect costs such

as education and social services. When the condition persists into adulthood, symptoms begin to

include poor occupational performance (Kuriyan et al., 2013) and higher risk taking behaviour

(Flory et al., 2006, Groen et al., 2013) resulting in even poorer financial and societal outcomes

(Doshi et al., 2012). Factors such as these contribute to a reduced quality of life for ADHD patients,

which often persist through middle age into old age if left untreated (Brod et al., 2012).

ADHD affects 5-8% of school aged children and can persist to adulthood in 50-80% of cases

(Faraone et al., 2000, Barkley et al., 1990, Fayyad et al., 2007, Bari et al., 2008). It is recognised

as a heterogeneous disorder characterised by three main symptoms (inattention, hyperactivity and

impulsivity), which forms the basis of the current psychiatric classification system. However a

recent review describes the many other different methods of characterising ADHD, including; types

and traits, age, gender, treatment, duration and variations in neuroanatomical structure and

function (Heidbreder, 2015). ADHD is most commonly sub-classified as; 1. Predominantly

inattentive (ADHD-I), 2. Predominantly hyperactive/impulsive (ADHD-HI) 3. Combined type

presentation (ADHD-C). In a large meta-analysis investigating the validity of the subtypes and

symptom dimensions, the authors concluded that separation into subtypes is a useful, convenient

way to describe the functional and behavioural aspects of ADHD, however care must be taken

when defining the subtypes as discrete distinct forms of the disorder (Willcutt et al., 2012).

There is a large evidence-base confirming the heterogeneity between subtypes in ADHD. This

evidence suggests the existence of neurocognitive, gender, neural correlate and behavioural

differences (different phenotypes) between subtypes. The gender differences reported show that

women are more likely than men to be diagnosed with the inattentive subtype of ADHD (Biederman

et al., 2002) and men are more likely to be diagnosed with the combined subtype (Ramtekkar et al.,

2010b). Interestingly, the male to female ratio differs between children and adults. In children,

ratio estimates differ depending on the sample; in community surveys male to female ratio is higher

(as high as 10:1) than in a clinical sample (as low as 3:1). However in adults, this ratio is reduced

(1:1 to 2:1) (Williamson and Johnston, 2015a). The importance of recognising gender differences

in research is highlighted in a recent comprehensive review, which recommends adequate

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representation of both genders in clinical samples (Williamson and Johnston, 2015a). It is vital that

both male and female subjects are included in future studies, including in animal studies to

represent the true clinical picture of ADHD and to more carefully assess the contribution of gender

to the pathophysiology of the disease.

The ADHD-200 Consortium investigating heterogeneity in ADHD have shown a number of specific

neural correlates within the subtypes of ADHD (ADHD-200-Consortium, 2012). The ADHD-I

subtype was found to have variations in specific brain regions including mainly dorsolateral

prefrontal and cerebellar regions, known to be associated with control systems. ADHD-C subtype

patients were noted to have atypical connectivity in midline default network components and the

insular cortex. These results support the hypothesis that differential connectivity disturbances

underpin each ADHD subtype (Fair et al., 2013). It has also been shown that an abnormality in the

motor circuit represents the major difference between ADHD-C and ADHD-I subtypes (Lei et al.,

2014).

Existing pharmacotherapy is not designed for the diversity of symptom presentations, and therefore

lacks efficacy and also has a large side effect burden. Critical to the development of improved

therapy is improved understanding of the aetiology and neurobiology of these disorders, achieved

through carefully validated animal models, which are currently lacking.

Stimulant medication such as methylphenidate and amphetamine salts are currently first and third

line treatment in the National Institute of Clinical Excellence (NICE) guidelines respectively

(Palanivel et al., 2009). They control attentive symptoms in approximately 75% of patients, but do

not adequately control impulsive symptoms and, at high doses and in certain patient groups, may

even exacerbate them (Winstanley, 2011, Faraone and Antshel, 2008). They are also

contraindicated for patients with a history of drug abuse due to their abuse liability. The second line

treatment is the non-stimulant, atomoxetine which alleviates impulsive measures in the continuous

performance, stop signal, and delay discounting tasks in humans (Barry et al., 2009, Chamberlain

et al., 2006), however, for attentive measures response rates and effect sizes are lower than for

stimulants (Heal et al., 2009, Cunill et al., 2013). A recent meta-analysis of atomoxetine treatment

concluded that the risk-benefit ratio for atomoxetine is even lower in an adult ADHD population as it

has only minimal effect on clinically meaningful endpoints (e.g. job performance or sociability)

(Cunill et al., 2013). Other non-stimulant treatments, guanfacine and clonidine are only

recommended in North America for ADHD (Bolea-Alamanac et al., 2014). Their omission from

NICE and other European guidelines is likely due to the need for clearer evidence of their efficacy

across patient groups, particularly in adulthood. With adult ADHD being increasingly recognised to

have a significant socioeconomic burden, it is important that we find new pharmacological

treatments with efficacy, safety and tolerability in the adult ADHD population, or enhance the use of

available medication by delivering the most appropriate treatment to each subtype of patient (Kooij

et al., 2010). Less commonly used treatments include modafinil, desipramine, and buproprion

(Minzenberg, 2012, Palanivel et al., 2009). These drugs are not recommended by NICE as their

mechanism of action is not yet fully understood. Taking all this into account, it is important to

24

conduct further research using animal models in order to define patient populations with symptom

profiles who may benefit most from the different types of medication.

1.3.2.1 Value of Animal Models and the Need for Improved Translation

Animal models offer the ability to control and manipulate experimental parameters more precisely

and rapidly than in human subjects (Markou et al., 2009, Moore, 2010). This level of manipulation

supports hypothesis driven research, with the overall aim of improving understanding of

neuropathology, and developing better treatments for clinical conditions. The high attrition rate of

drug development programmes and the withdrawal of the pharmaceutical industry from psychiatric

drug discovery has been attributed to poor translation from the animal models into the clinic (Neill

and Hendrie, 2012, Nutt and Goodwin, 2011, Insel and Sahakian, 2012). However, a more likely

explanation is that not enough time and resources have been provided to improve validity of the

animal models and appropriateness of the tests employed, a situation currently being addressed

(eg see Neill et al., 2014, Stuart et al., 2013, and Tomlinson et al., 2014 for ADHD). Progress in

this area is essential to enable much needed progression in psychiatric drug development. This

section of our review aims to summarise recent efforts to enhance translation from rodents to the

clinic in ADHD, using the separation of rodents into high and low performing groups based on

attentive and impulsive measures. The tests used for this include the standard 5- choice serial

reaction time task (5C-SRTT) and a task with enhanced translation, the 5- choice continuous

performance task (5C-CPT).

There are three factors suggested by Willner (1986) to consider when reviewing an animal model:

face, construct and predictive validity. Firstly, face validity requires the model to produce the same

phenotype as the human condition. In the case of ADHD, deficits in measures designed to translate

to human attention and impulsivity are key to producing the model. Furthermore, models designed

to align with clinical subtypes further increase this translational power. Secondly, construct validity

is required to mimic aetiology and neurobiology of the human condition in the animal. ADHD as an

example is considered highly heritable, with estimates of 80% or greater heritability (Biederman et

al., 1990, Kieling et al., 2008, Langner et al., 2013). Multiple genetic factors have been linked to

this heritability such as a dopamine D4 receptor (DRD4) seven transmembrane repeat (Woolley et

al., 2008, Faraone et al., 2001, Li et al., 2006, Gornick et al., 2007) and the dopamine transporter

type 1 (Lim et al., 2006). This suggests that several genetic factors combine to produce the

observed phenotype, or that other factors may play a role including environmental factors and

epigenetics (Maher, 2008). Due to the multifactorial and diverse nature of the aetiology of ADHD

and many other neuropsychiatric conditions, aetiological genetic modelling would be a restrictive

method preventing exploration of alternative mechanisms (Bari and Robbins, 2011, Nestler and

Hyman, 2010). One alternative is lesion modelling, but this suffers the same issue of restriction of

mechanism, and lesion or manipulation of brain regions with neurotoxins only mimics a small group

of ADHD patients who encounter toxins such as heavy metals. Ultimately these methods

investigate the effect of the manipulation rather than attempting to replicate the core symptoms of

ADHD (Jupp et al., 2013).

25

One theory of ADHD, and indeed other disorders, is that it represents the extreme of a continuum

present within the general population (Bari and Robbins, 2011). As rats tested in the 5C-SRTT and

5C-CPT show a continuum of performance, a method of modelling with stronger validity and the

potential for translation is behavioural separation based upon differences in performance, as it

avoids assumption of mechanisms (Puumala et al., 1996, Dalley et al., 2007, Tomlinson et al.,

2014). A variety of separation methods have been used to produce differing models based on the

condition under investigation or hypothesis studied. Taking into account the important differences

between the subtypes identified by behavioural separation, optimum animal models need to

characterize specific symptoms (i.e. impulsivity) or model the individual subtypes of ADHD. We

review the main methods used to date and assess their translational value in the following sections.

1.3.2.2 Measuring Attention and Impulsive Action in Rodents

In humans, the CPT is the most widely utilised task used to measure sustained attention (errors of

omission and accuracy of response), impulsive action (errors of commission) and vigilance (d

prime-d′), the ability to remain aware of changing situations in order to respond correctly (Epstein et

al., 2003, Riccio et al., 2002). All of these are readily measured in animals. The most widely used

test is the rodent 5C-SRTT (Carli et al., 1983, Robbins, 2002, Bari et al., 2008). This test has

recently been further validated by reverse-translation to humans, as the four choice serial reaction

time task (Voon et al., 2014). For full details of the methods for 5C-SRTT see Bari et al. (2008), in

brief a rodent, usually a rat, is placed into a standard five-hole operant chamber (Fig 1). During the

task a light stimulus is lit briefly in the back of a randomly selected aperture. The animal‟s ability to

detect and respond, usually via a nose poke response, to the correct aperture is recorded as a

measure of accuracy, which is considered an analogue of selective attention (Robbins, 2002). A

second measure provided by this task is premature responding during the waiting period, before

the next stimulus presentation. This is considered a measure of impulsive action and will be termed

waiting impulsivity for the purposes of this review. This task has been widely adopted as a

preclinical task of attention and waiting impulsivity (Robbins, 2002, Winstanley, 2011).

A variation of this task is the 5 choice continuous performance task (5C-CPT) first described in

mice (Young et al., 2009), and recently validated in our laboratory for rats (Barnes et al., 2012a,

Barnes et al., 2012b). It has also been reverse translated to humans, demonstrating translational

validity (McKenna et al., 2013). In the CPT, the same response to a single light stimulus is required

during most trials, but infrequent non-target trials add an additional challenge. In non-target trials,

the pre-potent signal of all five lights requires no response by the animal in order to gain a food

reward (i.e. the animal is rewarded for withholding its impulse to respond). Responding during this

trial, rather than withholding responding, is termed a false alarm, and interpreted as response

disinhibition (Fig 1). Response disinhibition closely corresponds with human errors of commission

in the CPT (Riccio et al., 2002). Response disinhibition is a form of impulsive action, but is

genetically and pharmacologically distinct from waiting impulsivity as measured by premature

responses in the 5C-SRTT and 5C-CPT (Young et al., 2011). When DRD4 expression is reduced,

response disinhibition increases without affecting the premature responses. Pharmacologically,

SD242084 (a 5-HT2C receptor antagonist) increases premature responses without affecting false

26

alarms. The addition of non-target trials also enables the assessment of vigilance (measured as

sensitivity index-SI) by robust statistical analyses using signal detection theory in the same way as

in the human CPT (discussed in detail in Young et al. (2009). The 5C-CPT in rodents offers

enhanced translation to humans because of its close resemblance to the human CPT. Due to this

enhanced translational value and lengthy training procedure, it has been suggested that the 5C-

CPT is best used at later stages of the drug development process and that the 5C-SRTT is more

suited to earlier stages, due to its higher throughput (Lustig et al., 2013).

Figure 1.2: The 5 Choice Serial Reaction Time Task, left panel (5C-SRTT) only uses trials where a reward is earned by responding in the hole in which a light stimulus appears, either during the stimulus duration, or immediately afterwards, in the remaining limited hold period. The 5 Choice Continuous performance task (5C-CPT) uses these target trials and adds infrequent non target trials where all five lights are lit and no response is required to earn a food reward, no go trials are shown in the right panel. Adapted with permission from Tomlinson et al. (2015).

1.3.3 Methods of Separation

1.3.3.1 Models of High Impulsive Action

The most commonly used method was first described by Dalley et al. (2007) and has been used in

several studies since, demonstrating its reproducibility. This method involves separating rats based

on premature responses in the 5C-SRTT, as an analogue of impulsive action. Dalley and

colleagues use adult male Lister Hooded rats in this model. In order to separate the animals, they

were trained to standard parameters of the task (as above) using a 5 seconds (s) fixed inter-trial

interval (ITI), they were then cycled through 3 weeks of daily testing with two days at a 5 s fixed ITI,

one day at a 7 s fixed ITI and then two days at a 5 s fixed ITI again (Fig 2). The use of two ITIs

demonstrates the rats‟ performance at baseline (5 s) and when challenged (7 s) (Dalley et al.,

2007). HI animals were originally shown to have twice as many premature responses as LI animals

at 7 s ITI (Dalley et al., 2007). They have also been characterised as having 50% premature

responses across the three weeks at 7 s ITI with Low Impulsive (LI) animals having less than 30%

premature responses (Caprioli et al., 2013, Caprioli et al., 2014) or as being in the top 15th

27

percentile for impulsivity (Isherwood et al., 2015). This HI model was originally used to model

aspects of drug taking behaviour, such as abstinence and relapse in addition to investigation of the

neuroanatomical basis of impulsive behaviour, more specifically, the role of prefrontal cortex and

ventral striatum circuitry in impulsive behaviour (Jupp et al., 2013). This method results in

approximately 7% of the animals being classified as high impulsive, which is then a stable trait

when retested over time (Dalley et al., 2007).

Another method of separation for impulsivity is described by Diergaarde et al. (2008) who

separated a group of 32 male Wistar rats based on premature responding by upper and lower

quartiles using five stable training sessions. This is a shorter baseline period than that used by

Dalley et al. (2007). Training sessions used a standard 5 s fixed ITI but a 1 s stimulus duration

(SD), which is longer than the standard SD (Bari et al., 2008). The primary effect of altered SD is

on accuracy and, as the limited hold is 1 s in both cases, there would be an expected minimal

effect on premature responses. This method produced 8 HI and 8 LI animals, removing 16

moderately performing animals from the comparison. This model has been used to study nicotine

dependence and non-drug related addiction by sucrose self-administration (discussed later). The

advantage of this method is that it offers a simple means of separation with a substantial difference

in performance between the subgroups. While use of cohort characteristics such as upper and

lower quartile makes comparison within a cohort robust, it also means that whole cohort

performance is an important factor when comparing between studies.

Winstanley et al. (2010) used a similar method, 41 male Long-Evans rats were trained to a stable

five-day baseline in the 5C-SRTT. They were then separated according to the median premature

responses. Using the median reduces the effect of extreme values and allows re-establishment of

a baseline between experiments. However, it also produces an unequal HI:LI ratio. The authors

state that this method was not designed to rigorously separate animals into extreme groups but to

offer a simple separation method and also has the ethical benefit of utilising all members of a

Figure 1.3: Baseline separation of HI-filled circles/LI-empty

circle animals using the Dalley et al. (2007) method. This

establishes a baseline of 5 s ITI and then challenges the

animals with 7 s long ITI. Under this challenge, HI animals can

be separated. The 5 day schedule is repeated for three

consecutive weeks for reliability. Reproduced with permission

from Dalley et al. (2007).

28

cohort (Winstanley et al., 2010). This model has been used to study obsessive compulsive disorder

and bipolar disorder.

Tomlinson et al. (2014) recently proposed a model to account for impulsive symptoms by using the

parameters of premature responses (waiting impulsivity) and probability of false alarms (pFA;

response inhibition) offering a unique modelling method using the highly translatable 5C-CPT in

female Lister Hooded rats. For this model, the parameters were premature responses above or

below 10 and probability of false alarms above or below 0.5 for high and low impulsive groups

respectively. As response inhibition is a critical factor of ADHD impulsive and combined subtype

presentation in people (Epstein et al., 2001) this model is designed for enhanced translation to the

clinical CPT (Young et al., 2009, Lustig et al., 2013, Tomlinson et al., 2014, Tomlinson et al., 2015).

1.3.3.2 Models of Low Attention

To separate animals for attentive performance, Granon et al. (2000) used the measure of accuracy

and selected animals performing above or below 75% accuracy to produce high and low baseline

performance groups respectively. Male Lister Hooded rats were trained to 5 s SD as in the

standard 5C-SRTT protocol (Bari et al., 2008). Once rats had been trained, stability of performance

was measured over a five day baseline. Animals in which performance deviated <5% from the

mean were progressed to a reduced SD (not lower than 0.25 s). Before testing, animals performed

stably between 0.5-0.25 s SD, this varied between animals so is a confounding factor for analysis,

but high attentive (HA) and low attentive (LA) groups were well balanced in SD. Ensuring stability is

important for accurate interpretation of the results, but testing the animals based on different SDs

alters the nature of the separation as the ability to respond at shorter SDs is seen as an attentive

trait which directly affects accuracy (Robbins, 2002, Young et al., 2013).

Paterson et al. (2011) used male Long Evans rats to produce a sub-optimally performing group.

This, as with the previous model, was based on less than 75% accuracy across a 5 day baseline,

but this study only looked at the low performing group. To control for lack of trials completed they

also stipulated that at least 50 trials had to be completed in each of five consecutive sessions. They

state that 25% of the whole group of rats met these criteria making it similar to a lower quartile

separation.

Grottick & Higgins (2000) produced a model to test the hypothesis that nicotine has a pro-attentive

effect that cannot be measured in normal animals using the 5C-SRTT. They chose male Lister

Hooded rats with < 80% accuracy and > 20% omissions. They are the only group to separate using

omissions as a measure of ability to sustain attention. Although it is debated whether omissions

represent sustained attention or motivation in this task (Robbins, 2002), this demonstrates the

flexibility to separate by a particular parameter of interest.

The 5C-CPT has also been used recently to model the attentive deficits in ADHD, which are known

to persist into adulthood more consistently than impulsive symptoms (Ingram et al., 1999, Wilens et

al., 2004, Kooij et al., 2010). Tomlinson et al. (2014) produced a model of the inattentive subtype

29

of ADHD using the 5C-CPT by separating female Lister Hooded rats using parameters of above or

below 90% accuracy with a SI of above or below 0.3, for high attentive and low attentive groups

respectively across 5 baseline training sessions. The SI adds an important factor to the model by

accounting for vigilance, which has been shown to be a significant factor in all ADHD symptom

subtypes (Collings, 2003). This therefore produces a model closer to the clinical situation, with the

aim of improved assessment of compounds with differential efficacy in certain ADHD impulsive

subtype patients.

1.3.3.3 Models Most Relevant to ADHD Symptomatology

The earliest example of behavioural separation using the 5C-SRTT is by Puumala et al. (1996).

Their method translates well to combined subtype of ADHD. They produced the model by

separating male Han:Wistar rats into an ADHD and a control group. To do this they used

percentage correct responses and percentage premature responses. An important feature of their

method is that they used two parameters to define the model, correctly recognising that ADHD

commonly presents with both inattention and impulsivity, particularly in combined subtype patients

(Epstein et al., 2003).

In an attempt to represent the spectrum of ADHD symptoms, Blondeau and Dellu-Hagedorn (2007)

used a cluster analysis method to isolate an array of phenotypes (Fig 3). This method produced

the efficient performing (E) animals (high percentage correct, and low percentage premature), the

moderate performing (M) group (with average percentage correct and percentage premature), the

inattentive (IA) group (low percentage correct, but average percentage premature) and finally the

inattentive and impulsive (IA-I) group (low percentage correct and high percentage premature).

These groups are similar to those found in a clinical and non-clinical population when people were

tested with a self-report questionnaire based on disease criteria (Marsh and Williams, 2004).

Tomlinson et al. (2015) also produced a model of adult ADHD combined subtype, this model used

accuracy to separate by attentiveness, SI to separate by vigilance and pFA to separate by

impulsive action. In ADHD-C type patients these three symptoms combine to produce the most

persistent and difficult to treat subgroup (Molina et al., 2009, Kooij et al., 2010). Therefore an

animal model of this subtype is particularly valuable to enhance our understanding of the

neurobiological basis of this subtype and so improve treatment strategies.

30

Figure 1.4: a dimensional analysis

method of performance separation.

This method is particularly interesting

as it produces multiple groups which

correspond to clinical ADHD

subgroups. Reproduced with

permission, from Blondeau and Dellu-

Hagedorn (2007).

31

Table 1.1: Summary of published methods for modelling impulsivity, attention and combined symptom subtypes using performance based separation techniques, as discussed above.

Abbreviations: 5 choice-serial reaction time task (5C-SRTT), 5 choice continuous performance task (5C-CPT), high/ low impulsive (HI/LI), high/ low performance (HP/LP),

attention deficit hyperactivity disorder (ADHD), ADHD inattentive subtype (ADHD-I), ADHD combined subtype (ADHD-C), inattentive (IA), IA impulsive (IA-I), moderate

performers (M), efficient performers (E), Lister Hooded (LH), premature response (PR), probability of false alarms (p[FA]), accuracy (Acc), sensitivity index (SI).

Developed by Test Model Rat sex & strain Parameter Rule Additional

Notes

Imp

uls

ivit

y

Dalley et al. (2007)

5C

-SR

TT

HI Male LH PR >2 at 7 s ITI Tested over 3

weeks

Diergaarde et al. (2008) HI/ LI Male Wistar PR Upper/ Lower

Quartiles

Winstanley et al. (2010) HI/ LI Male Long-Evans PR Above/ Below

Median

Tomlinson et al. (2014)

5C

-

CP

T

HI/ LI Female LH PR >/ < 10

Mean of 5 tests p[FA] >/ < 0.5

Att

en

tio

n

Granon et al. (2000)

5C

-SR

TT

HP/ LP Male LH Acc >/ < 75%

accuracy

When stable

over 5 sessions

Paterson et al. (2011) LP Male Long-Evans Acc

< 75%

accuracy and >

50 trials

Similar to

Granon et al.

(2000), but only

used LP group

Grottick and Higgins

(2000) LP Male LH

Acc < 75% Stable for 2

weeks. Omissions > 20%

32


5C

-CP

T

ADHD-I/ Control Female LH

Acc >/ < 90%

Mean of 5 tests

SI >/ < 0.3

Att

en

tio

n &

Im

pu

lsiv

ity

Puumala et al. (1996)

5C

-SR

TT

ADHD/ Control Male Han:Wistar Acc < 60%/ > 75%

PR > 40%/ < 30%

Blondeau and Dellu-

Hagedorn (2007)

IA-I/ IA/ M/ E Male Sprague-

Dawley

Acc

Cluster analysis

Control and

three subtypes

of ADHD PR


5C

-CP

T

ADHD-C/

Control Female LH

Acc </ > 90% Attention,

Vigilance and

Impulsive action

SI </ > 0.3

p[FA] >/ < 0.5

33

1.3.4 Neurobiology Identified Using Separation Methods

1.3.4.1 Using Impulsive Models

1.3.4.1.1 Behaviour

Dalley et al. (2007) produced the most widely used behavioural separation model using the 5C-

SRTT. It was proposed that differences in neurobiology between the animals in each group could

drive escalated cocaine self-administration. Drug naive HI animals showed reduced binding

potential for [18

F]fallypride using positron emission tomography imaging, suggesting that HI animals

had a reduced number of D2 and/or D3 receptors in the ventral striatum, showing an

endophenotype that may contribute to the behavioural differences. High impulsivity has been linked

to reduced D2/D3 availability in the striatum of ADHD patients, which adds construct validity to the

model (Ghahremani et al., 2012, Buckholtz et al., 2010). This difference was also observed

following cocaine self-administration, which was enhanced in this group and correlated with

reduced dopamine release in the nucleus accumbens core (NacC) of HI animals. Diergaarde et al.

(2008) used their model to show that high impulsive action predicted greater overall nicotine self-

administration and longer maintenance compared with animals with lower impulsive action. They

also showed that HI animals show escalated sucrose self-administration (Diergaarde et al., 2009).

This shows that high impulsive animals are more responsive to reinforcement which is not limited

to drugs of abuse. Dalley et al. (2007) showed that the HI phenotype is linked to increased cocaine

self-administration. One caveat of previous findings is that in humans it was not possible to

determine whether this high impulsivity is due to the drug taking itself or a predefined trait that led

to escalating drug taking behaviour (Perry and Carroll, 2008). As impulsivity is screened before

drug administration these findings support the latter theory. Belin et al. (2008) examined how HI

animals differed in attempts to stop compulsive drug taking using the negative stimulus of a foot

shock. They found that HI animals took more negative stimuli to stop drug taking behaviour

showing that impulsivity is also linked to compulsive behaviour.

The delay discounting task challenges animals to decide how long they are willing to wait for a

larger reward compared with a smaller and more immediate reward (Winstanley et al., 2003). This

shows how impulsive the animals are, but is distinct from waiting impulsivity already discussed.

This type of reward sensitivity is called impulsive choice, and is the ability to delay gratification for a

better long term solution when faced with an immediate payoff. When HI animals in the Dalley et al.

(2007) study were tested in the delay discounting task they showed a steeper discounting curve,

showing that they are less willing to wait for a larger delayed reward as the delay increased

(Robinson et al., 2009). However these rats showed no deficit in the stop signal reaction time task.

This measures the speed at which an action can be cancelled. This highlights the separable nature

of the different forms of impulsivity, which has previously been demonstrated in lesion studies

(Cardinal et al., 2001, Christakou et al., 2004, Eagle and Robbins, 2003). Separation by

performance using the delay discounting task has also been studied and is discussed in Jupp et al.

(2013); here we focus on impulsive action in 5C-SRTT and 5C-CPT. Robinson et al. (2009) also

34

demonstrated the ability of atomoxetine to reduce premature responses (waiting impulsivity) and

flatten the delay discounting curve (impulsive choice).

1.3.4.2 Pharmacology/ Neurobiology

High waiting impulsivity in the Dalley et al. (2007) HI model can be modulated by dopaminergic and

noradrenergic pharmacological agents such as quinpirole and sumanirole (both D2/3 receptor

agonists), which both reduce premature responses (Fernando et al., 2012). In that study,

atomoxetine (a NA reuptake inhibitor) and guanfacine (an α2 adrenoceptor agonist) dose

dependently reduced premature responses in HI animals. GBR-12909 (a DA reuptake inhibitor)

increased impulsivity in HI and LI animals, whereas the DA and NA reuptake inhibitor

methylphenidate had no effect on impulsive responses. These results show that both NA and DA

systems are involved in impulsive behaviour and shows that activation of D2/3 or α2 adrenoceptors

are involved in impulse control. The modulation of impulsivity by these pharmacological agents is

very similar to effects seen in humans with impulse control disorders and ADHD demonstrating

predictive validity of the model (Cunill et al., 2013, Wood et al., 2014). Glutamate is the most

abundant excitatory neurotransmitter in the brain and shows involvement in a number of conditions

which express high levels of impulsivity (Isherwood et al., 2015). Modulation of metabotropic

glutamate receptors is therefore a target for investigation. Recent work examined the effect of

metabotropic glutamate receptor 5 negative allosteric modulators RO4917523 and 3-[(2-methyl-

1,3-thiazol-4-yl)ethynyl]pyridine (MTEP) and the positive allosteric modulator ADX47273 on trait

impulsivity (Isherwood et al., 2015). The authors demonstrated that RO4917523 and MTEP

reduced premature responses but at the same time increased omissions and response latencies,

suggesting a non-specific motor effect rather than a true reduction in waiting impulsivity. ADX47273

reduced premature responses in the HI group, where the effect was larger when animals were

challenged with a 7 s rather than 5 s ITI. While in this group, omissions were not affected, correct

latency was significantly increased at the same doses that reduced premature responses. A

locomotor activity assessment was simultaneously conducted. The only dose of drug not to show

any effect on locomotor activity was ADX47273 at 80 mg/kg, which also had a highly significant

effect on correct latency. Due to these locomotor effects of the metabotropic glutamate receptor 5

modulators, it is challenging to separate effects on impulsivity from general effects on motor

behaviour. Future work could investigate region specific infusion and compounds with affinity for

other metabotropic receptors to analyse the role of glutamate in impulsivity without the confounding

motor effects.

Based on the initial [18

F]fallypride PET study, the NAc, as part of the ventral striatum was found to

be a key target, in particular, the D2/3 system (Dalley et al., 2007). The left NAc was found to have

reduced grey matter density in HI animals (Caprioli et al., 2014). Infusion of aripiprazole, a D2/3

receptor partial agonist and atypical antipsychotic, and nafadotride, a D2/3 receptor antagonist, into

the NAcC and nucleus accumbens shell (NAcS) separately showed a functional opposition within

the NAc (Besson et al., 2010). For HI animals, nafadotride increased premature responses when

infused into the NAcS, but reduced them when infused into the NAcC while aripiprazole had no

35

effect when directly infused into either region. This contrasting effect may explain why systemic

administration of nafadotride had no effect on premature responses. This has been further

investigated using quinpirole, a D2/3 receptor full agonist, which was found to increase premature

responses when infused into the NAcC, in HI animals only but increased locomotor activity in both

HI and LI animals (Moreno et al., 2013). In contrast, infusion of quinpirole into the NAcS promoted

an increase in locomotor activity only in HI and not in LI animals. Nafadotride did not attenuate the

increase in impulsivity caused by quinpirole infused into the NAcC, however it did attenuate the

locomotor stimulant effect of NAcS infusion in HI animals. The inhibitory neurotransmitter γ amino

butyric acid (GABA) has recently been found to play a role in impulsivity by modulation in the

NAcC. This was shown by a reduction of the enzymes needed to produce GABA, glutamate

decarboxylase GAD 65/67, as well as reduced markers of dendritic spines and microtubules in HI

compared to LI animals which was further strengthened by LI animals becoming more impulsive

when GAD65/67 gene expression was reduced by infusion of antisense GAD67 and GAD65

oligonucleotides into the NAcC (Caprioli et al., 2014). This reveals a novel mechanism of

impulsivity in rats and warrants further investigation.

Patients with damage to the orbital frontal cortex (OFC) have high scores on impulsiveness

questionnaires (Berlin et al., 2005) and so Winstanley et al. (2010) investigated whether this region

could be mediating behaviour in HI animals. OFC infusion of the dopamine D1 receptor antagonist,

SCH23390 decreased impulsive responding in HI animals, but not in controls. When infused into

the OFC, quinpirole only affected non-impulsive parameters, while a D1 receptor agonist, SKF-

81297 had no effect. This shows that D1 receptors in the OFC do not fully mediate effects on

impulsivity suggesting that brain regions outside the OFC are important, and that D2 receptors in

the OFC are not involved in impulsivity. This study again demonstrated that baseline performance

of the individual animal alters the effect of dopaminergic compounds.

Electrical signalling of clusters of neurons has also been linked to high and low impulsive behaviour

(Donnelly et al., 2014). Silicone multielectrode arrays were implanted into the NAcC or NAcS and

the infralimbic and prelimbic regions within the prefrontal cortex of HI and LI male Lister Hooded

rats and low frequency (<200 Hz) activity recorded (Donnelly et al., 2014). By correlating electrical

changes in the low frequency oscillations or local field potential with operant responses made in the

5C-SRTT, a new level of understanding was achieved. The authors discovered firstly that, across

the NAc and prefrontal cortex, gamma (50-60 Hz) and theta (7-9 Hz) frequency oscillations

increased during visual search in anticipation of stimulus presentation and again following a correct

response. Secondly they found that gamma and delta activity coupling strengthened following error

responses. Thirdly they found that these differed significantly when a premature response was

about to be made. Finally they found that in HI animals error responses were stronger, and

responses linked to correct responses were weaker. This elegant work shows that both the medial

prefrontal cortex and the NAc are important regions for attention when scanning for a target and

are linked to impulsive responses by recognition of error response. This series of experiments

shows that, not only can behavioural separation offer a platform to test and compare future

36

treatments, but enable research into new mechanisms which cause behavioural deficits. Other

models such as dopaminergic lesion would not have allowed insight into the GABAergic or

electrical activity within discrete brain areas which are becoming of increasing relevance in the

search for alternative treatment mechanisms (Hayes et al., 2014, Jupp et al., 2013, Donnelly et al.,

2014).

1.3.4.3 Using Models of Attention

Granon et al. (2000) used the parameters of above or below 75% accuracy to analyse the effect of

three dopaminergic compounds via bilateral microinfusion into the medial prefrontal cortex. They

found that, while sulpiride (a D2 receptor antagonist) had no effect on task performance, SKF-

38393 (a D1 receptor agonist) enhanced accuracy selectively in the low performing animals and

SCH 23390 (a D1 receptor antagonist) had the opposite effect to reduce accuracy selectively in the

high performing animals. Although SKF-38393 had no effect here in unseparated animals, it may

be due to the task used, as it has an effect on vigilance in 5C-CPT (Barnes et al., 2012a). The

selectivity of effect suggests that there could be differences in dopamine system engagement in

high and low performing animals, where low performing animals have reduced dopamine activity in

the prefrontal cortex making them susceptible to receptor activation and high performing animals

have increased levels of dopamine and are thus less susceptible to receptor activation, but more

susceptible to antagonism.

Work by Paterson et al. (2011) expanded this by also analysing effects of d-amphetamine,

methylphenidate and atomoxetine in poor performing animals. They used similar methods of

separation to Granon et al. (2000), but only studied poor performing animals. They also compared

the effect of these clinically efficacious compounds to tolcapone (a Catechol O Methyl Transferase

(COMT) inhibitor). They found that a systemically administered acute dose of d-amphetamine

increased premature responding in the poor performing rats. Atomoxetine reduced the number of

premature responses. Methylphenidate reduced percentage omissions at low doses (0.1 and 0.5

mg/kg). One issue with this study, however, is the use of only the poor performing animals and not

both poor and high performing animals which would allow a more robust comparison. In ADHD

(discussed later) an oppositional effect is seen where ADHD-like animals show improved

performance and good performers show reduced performance in response to methylphenidate.

Using low performing animals, Grottick and Higgins (2000) found that sub-chronic dosing of

nicotine and SIB 1765F, an α4β2 nicotinic acetylcholine receptor agonist, improved accuracy and

speed of response, whereas AR-R 17779, an α7 nicotinic receptor agonist, had no effect. Nicotine

and SIB 1765F both increased premature responding during sub-chronic treatment, while nicotine

also reduced omissions. This shows that nicotine and SIB 1765F improve selective attention,

speed of response processing but increase waiting impulsivity, suggesting that the effects of

nicotine are mediated via the α4β2 nicotinic acetylcholine receptor and not the α7 receptor. The

effect of nicotine on omissions may be the result of an effect on motor activity (Grottick et al., 2003,

Young et al., 2013). It has since been found in unseparated mice and in mice with a scopolamine

37

(muscarinic acetylcholine receptor agonist) induced vigilance deficit that nicotine has a primary

effect on vigilance (Young et al., 2013). This would make it an interesting pharmacological agent to

test using animals separated in the 5C-CPT due to the ability to robustly calculate vigilance in that

task.

1.3.4.4 Using Models of ADHD

Lower right frontal cortex DOPAC/DA ratio has been observed in animals with lower accuracy,

whereas in the left frontal cortex serotonin levels were higher in animals with low accuracy

(Puumala and Sirvio, 1998). Furthermore, serotonin utilization in the right frontal cortex was higher

in highly impulsive animals. This suggests that pharmacological agents developed to improve poor

performance should increase dopaminergic neurotransmission and reduce serotonergic

neurotransmission in the frontal cortex. Methylphenidate (MPH) increases DA and NA by blocking

the dopamine transporter (DAT) and noradrenaline transporter (NAT) thus reducing reuptake of DA

and NA leading to increased activation of D1 and α2 receptors respectively. This has a positive

effect on choice accuracy in low attentive animals (Puumala et al., 1996, Blondeau and Dellu-

Hagedorn, 2007). Doses of 100 and 1000 µg/kg MPH were used in the 5C-SRTT, the lower dose

also reduced premature responses in low performing animals. However it has also been found that

MPH can increase premature responding at high doses of 1 mg/kg (Blondeau and Dellu-Hagedorn,

2007). The differential effect of methylphenidate in high and low attentive animals is interesting as it

can be understood using the inverted U shaped hypothesis of optimal arousal, where alertness can

only be increased to a point, beyond which further stimulation of the system promotes negative

effects such as distractibility, impulsiveness and anxiety (Wood et al., 2014). A neurobiological

theory of ADHD has been suggested based on this, where prefrontal cortex catecholamine levels

follow this inverted U shaped relationship (Arnsten, 2009, de Jongh et al., 2008). This also shows

that separation of the animals is essential to determine doses of compounds which may selectively

improve the performance of low and high performing animals. Future work should aim to establish

the mechanisms behind the difference in sensitivity to monoaminergic compounds.

Atomoxetine is a second line treatment for ADHD and acts in a similar way to methylphenidate, but

is selective for NAT, with minimal affinity for DAT, thus activating α2 adrenoceptors in the prefrontal

cortex and minimising striatal psychostimulant effects (Bymaster et al., 2002, Heal et al., 2009). In

animals separated by dimensional analysis into efficient (E), moderate (M), Inattentive (IA) and

Inattentive and Impulsive (IA-I) groups, atomoxetine had no effect on accuracy, but reduced

premature responses in all groups except E, at doses of 0.1, 0.5 and 1 mg/kg. This suggests that

atomoxetine requires a certain threshold of premature responding to have a beneficial effect.

1.3.4.5 Using Models developed in 5C-CPT

A selective effect of MPH has also been shown in the 5C-CPT in female Lister Hooded rats.

Accuracy increased in the LA animals (representing the ADHD-I subtype) at 2 mg/kg, while SI

increased only at 0.5 and 1 mg/kg (Tomlinson et al., 2014). Firstly, this suggests a pharmacological

38

separation between selective attention and vigilance, secondly, the lack of effect on vigilance at 2.0

mg/kg may again be explained by the inverted U shaped hypothesis of optimal arousal (Wood et

al., 2014). Furthermore in LI animals, MPH increased premature responses at the highest dose of 2

mg/kg. In animals with high levels of premature responding and pFA (HI), MPH reduced premature

responses at 0.5 and 1 mg/kg. As no effect on pFA was observed at any dose of MPH, this can be

seen as an effect on waiting impulsivity and not response disinhibition.

In the same study, atomoxetine increased accuracy at 2 mg/kg and SI at 1 and 2 mg/kg in LA

animals, but reduced accuracy in HA animals at 1 mg/kg (Tomlinson et al., 2014). This adds to

(Blondeau and Dellu-Hagedorn, 2007) showing no effect up to 1 mg/kg for accuracy in their model

using the 5C-SRTT and for the first time uses a robust calculation of vigilance which is increased

by atomoxetine. Atomoxetine also reduced premature responding in HI animals at all doses and

reduced pFA in HI (1 and 2 mg/kg) and LI (2 mg/kg) animals. This differential effect of atomoxetine

further emphasises the need to separate animals based on their performance in order to clearly

identify effects of pharmacological agents.

Tomlinson et al. (2015) produced a low attentive, low vigilance and high response disinhibition

model of ADHD-C using female Lister Hooded rats in the 5C-CPT. A-412997, a selective DRD4

receptor agonist improved vigilance and reduced pFA at 0.3 and 1 µmol/kg. No effect was seen on

any parameter in the HA animals, suggesting that targeting the DRD4 could be an effective

treatment for the combined subtype of ADHD. This is a particularly important finding as a genetic

mutation of the 7 transmembrane repeat of DRD4 is linked to adult ADHD, (Franke et al., 2012),

and several D4 receptor antagonists have improved behavioural hyperactivity in an ADHD animal

model (Zhang et al., 2002), highlighting the importance of this receptor system in ADHD

pathophysiology. The COMT inhibitor, tolcapone has previously been shown to be ineffective in the

5C-SRTT (Paterson et al., 2011). The previous method of separation was based only on accuracy

and used the 5C-SRTT, where Tomlinson et al. (2015) used tolcapone in the ADHD-C model, in

the 5C-CPT. In that study, accuracy and SI improved in ADHD-C animals (at 10 and 15 mg/kg), but

decreased in HA animals at the highest dose of 15 mg/kg. There are several possible explanations

for this discrepancy; firstly the 5C-CPT compared to the 5C-SRTT is a more difficult task, whereby

the extra difficulty may have challenged the animals enough to see an effect (Lustig et al., 2013).

Secondly, the Tomlinson et al. (2015) method used a particularly rigorous model of ADHD subtype

ADHD-C, this involved the two extra parameters of SI and pFA and a higher level of accuracy,

resulting in a different model. This highlights the potential flexibility of separation by performance to

produce two very different models depending on the parameters used for separation. It is important

that the most appropriate method of separation is used depending on the experimental question

being investigated. This subject is addressed below.

39

Table 1.2: Summary of pharmacological effects in models based on behavioural separation.

Abbreviations: Correct Latency (CL), noradrenaline (NA), INA transporter (NAT), Premature Responses (PR), dopamine transporter (DAT), catechol-O-methyl transferase (COMT) premature responses (PR), Nucleus accumbens (Na), Na core (NaC), Na shell (NaS), metabotropic glutamate receptor 5 (mGluR5), prelimbic cortex (PrL).

Model Drug Mechanism Administration

(Route) Measure Effect (Dose) Notes Reference

Imp

uls

ivit

y

Dalley et al.

(2007)

HI animals

Quinpirole

D2 Agonist Systemic (s.c.)

PR

↓ (0.01, 0.03, 0.1

mg/kg)

(Fernando et

al., 2012)

Sumanirole ↓ (0.1,0.3,1 mg/kg)

Also Increased

CL at 0.3 and 1

mg/kg

Atomoxetine NAT

Inhibition

Systemic (i.p.)

↓ (1,3 mg/kg) Also Increased

CL at 3 mg/kg

Guanfacine

α2 NA

Receptor

Agonist

↓ (0.3,1 mg/kg)

GBR 12909 DAT

Inhibition ↑ (2.5, 5 mg/kg)

Methylphenidate DAT/NAT

Inhibition - -

40

Aripiprazole D2/3 Partial

Agonist

NaC - -

(Besson et al.,

2010)

NaS - -

Nafadotride D2/3

Antagonist

NaC PR ↓ (0.1 µg)

NaS PR ↑ (0.1 µg)

Quinpirole D2/3 Full

Agonist NaC PR ↑ (0.3, 1 µg)

(Moreno et al.,

2013)

RO4917523

mGluR5

Negative

Allosteric

Modulator

Systemic (p.o.) PR ↓ (0.1,0.3 mg/kg)

Increases in

omissions, correct

latency and

locomotor activity

(Isherwood et

al., 2015) MTEP Systemic (i.p.) PR -

Increases in

omissions, CL

and locomotor

activity, reduction

in accuracy

ADX47273 mGluR5

Positive

Allosteric

Systemic (p.o.) PR ↓ (40, 60, 80, 100

mg/kg) Increases CL

41

Modulator

Tomlinson et al.

(2014) HI

animals


Inhibition

Systemic (i.p.)

PR ↓ (0.5, 1 mg/kg)

(Tomlinson et

al., 2014)

Atomoxetine NAT

Inhibition

PR ↓ (0.5, 1, 2 mg/kg)

pFA ↓ (1, 2 mg/kg)

SI ↑ (1, 2 mg/kg)

Att

en

tio

n

Granon et al.

(2000)

SCH 23390 D1

Antagonist

Bilateral PrL

infusion

- -

(Granon et al.,

2000) SKF 38393 D1 Agonist

Accuracy ↑ (0.06 µg/side)

Omissions ↓ (0.06 µg/side)

Sulpiride D2

Antagonist - -

Paterson et al.

(2011) D-Amphetamine

Catecholami

ne release Systemic (i.p.)

PR

↑ (0.56, 1 mg/kg) (Paterson et

al., 2011) Perservativ

e

Responses

42


Inhibition Omissions ↓ (0.1, 0.5 mg/kg)

Atomoxetine NAT

Inhibition PR ↓ (0.5,1,2 mg/kg)

Tolcapone COMT

Inhibition Omissions ↑ (3,30 mg/kg)

Grottick and

Higgins (2000)

Nicotine Nicotinic

Agonist

Subchronic

Systemic (s.c.)

for 20 days

Accuracy ↑ (0.2 mg/kg/day)

(Grottick and

Higgins, 2000)

Correct

Latency ↓ (0.2 mg/kg/day)

PR ↑ (0.2 mg/kg/day)

SIB1765F Nicotinic

α4β2 agonist

Accuracy ↑ (5 mg/kg/day)

Correct

Latency ↓ (5 mg/kg/day)

PR ↑ (5 mg/kg/day)

AR-R 17779 Nicotinic α7

Agonist - -

43

Tomlinson et al.

(2014) HA

animals


Inhibition

Systemic (i.p.)

Accuracy ↑ (2 mg/kg)

(Tomlinson et

al., 2014)

SI ↑ (0.5, 1 mg/kg)

Atomoxetine NAT

Inhibition Accuracy ↑ (2 mg/kg)

Att

en

tio

n &

Im

pu

lsiv

ity

Puumala et al.

(1996) Methylphenidate

DAT/NAT

Inhibition

Systemic (s.c.)

Accuracy ↑ (0.1, 1 mg/kg) (Puumala et

al., 1996)

Blondeau and

Dellu-Hagedorn

(2007)


Inhibition PR ↑ (1 mg/kg)

Largest effect in

IA-I group (Blondeau and

Dellu-

Hagedorn,

2007) Atomoxetine NAT

Inhibition PR ↑ (0.1, 0.5, 1 mg/kg) In all but E group

Tomlinson et al.

(2015)

A-412997 D4 Agonist

Systemic (i.p.)

SI ↑ (0.3, 1 µmol/kg)

(Tomlinson et

al., 2015)

pFA ↓ (0.3, 1 µmol/kg)

Tolcapone COMT

Inhibition

Accuracy ↑ (10, 15 mg/kg)

pFA ↓ (15 mg/kg)

SI ↑ (15 mg/kg)

44

1.3.5 Selection of the most appropriate method of separation

When deciding which model is best, the first question is what is being modelled. As shown

throughout this review modelling by performance is flexible allowing many measures to be used

individually or collectively. This decision also dictates which task to use. For example for a model

concerning deficits in vigilance, as seen in ADHD or schizophrenia, then the 5C-CPT is the most

viable choice. Also models involving impulsive action should strongly consider the important

difference between response disinhibition and waiting impulsivity (discussed above) as these have

been shown to be separate constructs, which can be manipulated independently (discussed in

Young et al. (2011)). However, for models of selective or sustained attention, the 5C-SRTT

measures of accuracy and omissions are well established parameters. The reduced length of

training time in the 5C-SRTT compared to the 5C-CPT can also be a determining factor in choice of

test.

Separation method differences are clear from the studies outlined in this review. To decide

between these factors there are a series of questions to ask. What level of deficit is required?

Winstanley et al. (2010) produced a model with a milder impairment using a simple median split. In

contrast upper and lower quartile separation used by Diergaarde et al. (2008) removes a middle

performing group to produce a model with a larger deficit. A larger deficit can also be produced by

increasing the challenge in the task. Dalley et al. (2007) provide an excellent example of

introducing an additional challenge in the form of longer ITIs to further separate the animals,

producing a larger deficit as shown by their model producing the highest 15th percentile of the

group (Isherwood et al., 2015). When choosing a more rigorous method it is important to consider

the principles of replacement reduction and refinement. A larger starting cohort will be required to

maintain statistical power in the group selected as a model. This contradicts the principle of

reduction of number of animals used, as recommended by the National Council of Reduction,

Refinement and Replacement (3Rs); so the two must be carefully balanced.

Methods of comparison vary between studies; a key factor to consider is within study referencing

compared to between study referencing. A within study reference is a more common approach.

This involves using measures such as mean, median, quartiles or percentiles to use the group‟s

performance to determine which animals are high versus low performers within that group. A main

advantage of this method includes consistent group numbers in high or low groups, making

comparisons more robust. Also it provides the opportunity for translation of this modelling method

between strain, gender or species which may have different baseline levels of performance. If the

groups differ in baseline performance then this too is a confounding factor. To account for this,

whole group baseline performance is important to consider when comparing between studies using

this method, and the separation levels should be clearly stated.

In between study referencing a line is produced from the data (e.g. <75%) and is then used in that

study and for future studies. This method provides more robust comparison between experiments.

However, how these reference lines are determined is often not clearly explained, questioning the

45

validity of this approach. Finally, a very interesting unique method is the dimensional analysis used

by (Blondeau and Dellu-Hagedorn, 2007). This method allows an unbiased approach to selecting

groups with different profiles using multiple measurements. As they have demonstrated, this allows

a range of profiles to be produced within a population. The use of additional measures such as

those in the 5C-CPT would allow new types of models to emerge from this method.

1.3.6 Conclusion

Animal models have several advantages including accurate control of variables and manipulation

of subjects. Recent models have attempted to recreate the symptomatology of disorders

characterised by attention and impulsivity deficits, using chemical lesions or genetic manipulations.

In a diverse population, where the aetiology is unknown, these methods restrict the ability to

identify novel targets for drug discovery. Separating animals based on performance in 5C-SRTT

and 5C-CPT offers a method of modelling without the limitation in assuming a mechanism of

action. Behavioural separation offers the further advantage of enhanced flexibility to investigate

many conditions which are characterised by low levels of attention or high impulsivity such as

ADHD, drug abuse, obsessive compulsive disorder, Parkinson‟s disease and schizophrenia.

Through continued use of these models, mechanisms underlying impulsivity have been identified

such as fronto-striatal interaction and the involvement of GABAergic signalling within the NAc. The

translational validity of these methods has been clearly demonstrated, particularly in relation to the

HI model of Dalley et al. (2007). It is hoped that many of the other models will be used to the same

level in order to determine reliability and improve translation. The series of models recently

produced using the 5C-CPT strive to improve translation of this work into a clinical setting by

mimicking more symptoms and offering specialised models for patient subtypes (Tomlinson et al.

2014; 2015). This aims to move towards a more individualised treatment method where

compounds can be pre-clinically tested for a variety of symptom subtypes. Some progress has

already been made in this area for the standard ADHD treatments, MPH and atomoxetine.

Separation of differential drug effects in animals separated by performance corresponds with the

inverted U shaped hypothesis of optimal arousal. Indeed this hypothesis highlights why behavioural

separation is so important. Pharmacological treatments clearly act differently in animal subjects

with poor attention or impulsivity and so need to be studied in these clinical populations. Finally,

one of the major considerations in this area is determination of the neurobiological basis of these

differences of performance in preclinical models and clinical population.

46

1.4 General Aims of Thesis

i) To further develop a method of modelling attention deficits in rats.

There are a number of strategies when choosing how to divide animals by their 5

choice task performance. Hayward et al. (2016) present the argument that dividing

above and below a within-study reference (i.e. median, or quartiles) offers higher

statistical and translational validity than a set reference point (i.e. 75% accuracy).

Therefore, we aimed to use this reference to refine the method used by Tomlinson

et al. (2014), (2015). An additional refinement was to use the more translational

measure of vigilance d′. To validate these design changes rats were tested in the

5C-CPT using methylphenidate and atomoxetine. The hypothesis of this experiment

was that if this method is valid, then the results of this would be comparable to

Tomlinson et al. (2014) as well as other studies with the same compounds, but with

increased translational validity.

ii) To use the high and low attentive model to study the pharmacology of

attention

It has previously been established that monoaminergic modulators promote

attention in low attentive animals performing the 5C-CPT. However, nicotine and

caffeine are widely used psychoactive stimulants that work via different

mechanisms. They have both been shown to improve sustained attention in the 5C-

SRTT, so we aimed to assess if nicotine and caffeine could also promote attention

or vigilance in low attentive animals in the 5C-CPT. We hypothesised that

improvements in sustained attention would be greater in low attentive animals as

has been seen with monoaminergic compounds.

The nicotinic acetylcholine α7 receptor has been implicated as a target for the

remediation of the cognitive symptoms of schizophrenia. Several compounds have

been developed for this purpose and seem to show improvement in a number of

cognitive parameters, including attention. Encenicline is a partial agonist of the α7

receptor and was in phase II clinical trials. We aimed to establish if pro-cognitive

effects of encenicline included improvement of inattention in low attentive animals.

iii) To examine the involvement of the frontoparietal network in performance of

the 5C-CPT and effects of attention promoting agents

There is a broad agreement of the vital involvement of the frontoparietal network in

humans and non-human primates. However, this requirement has not been shown

specifically for the 5C-CPT. The experimental hypothesis was that performance of

the task would promote activity in both regions and that this activity would be

dependent on task performance. If the frontoparietal network is important for

attention, then compounds that promote attention will promote changes in the

activity of neurones in these regions. To assess this, we compared frontoparietal

network activity in anaesthetized rats in response to injection with an attention

promoting dose of methylphenidate versus vehicle injection. We hypothesised that

47

we would see changes in activity in both PFC and PPC when treated with MPH

compared to vehicle treatment and, further, that the largest effect would be in the

attention-related alpha band.

48

2 General Methods

For Chapters 3, 4 and 5 the 5C-CPT was used to assess the accuracy, vigilance and impulsive

action of female Lister Hooded rats. The 5C-SRTT is a popular task with a large literature

demonstrating its usefulness in assessing attention and impulsivity (Robbins, 2002, Bari et al.,

2008, Dalley et al., 2007) ; however, without a „no go‟ stimulus it is unable to assess vigilance in

a robust manner (Young et al., 2009). The task, therefore, cannot translate its findings towards

the human CPT that it is based on. For this reason, the task is seen as less translatable for the

assessment of attention compared with the 5C-CPT, which does include „no go‟ trials, thereby

allowing calculation of d′ as an estimation of vigilance as used clinically (Lustig et al., 2013,

Hayward et al., 2016). It is for this key advantage of translation that the 5C-CPT is used in this

thesis.

2.1.1 Animals

All animals used in the studies presented in this thesis are adult female Hooded Lister rats

(Charles River, UK; weighing 210 ± 20 g at the start of experiments). Animals were housed in

groups of up to five for the duration of the studies. The environment was maintained at 21 ± 2

oC, 55 ± 5 % humidity. The diet was standard rat chow (Special Diet Services, UK) controlled to

maintain 90% free feeding weight throughout training and testing (10-12 g/rat/day). Home cages

were individually ventilated cages with two levels (GR1800 Double-Decker Cage, Techniplast,

UK) and testing was completed under a standard 12 hour light: dark cycle (lights on at 7:00 am).

Female rats were used as the 5C-CPT has been carefully validated in female rats in our

laboratory (Tomlinson et al., 2014, Tomlinson et al., 2015, Barnes et al., 2012a, Barnes et al.,

2012b). Some claim that to use females the stages of the estrous cycle need to be carefully

monitored and accounted for, however Prendergast et al. (2014) found that across an array of

different methods that female mice were less variable than males even without accounting for

the oestrus cycle. Additionally, they are poorly represented in preclinical research.

2.1.2 Animal Usage

For 5C-CPT experiments a total of 80 animals started training (Table 2.1). Animals used in the

electrophysiology experiment were ordered for those experiments only. Training for 5C-CPT

took approximately 8 months in total; at the time of the encenicline study, the animals were

approximately 12 months old, for nicotine and caffeine they were 14 months old and during the

final c-fos experiment they were approximately 17 months old.

49

Figure 2.1: Animals used for 5C-CPT experiments and the order of use. Between each experiment

complete health assessments were completed by a veterinarian and animals were tested to ensure

performance was maintained. Animals were excluded from tests for poor health or inconsistent

performance.

2.1.3 Apparatus

Training and testing 5C-CPT was conducted in eight standard nine hole chambers (Figure

2.2A; Campden Instruments Ltd, UK) with holes 2, 4, 6, and 8 occluded. Chambers were

controlled and data collected by ABet II Standard software (Lafayette Instrument Company, IL,

USA). Animal actions were detected by infrared photobeams across each apperture and an

infrared beam which was broken by movement of the magazine door to detect food collection

and initiate each trial. Animals were rewarded with 45 mg rodent reward pellets (Test Diet, MO,

USA).

Figure 2.2: 5C-CPT Apparatus for training and pharmacological testing. (A) A rodent in the

chamber being presented with a go trial. (B) Schematic representation of go and no go trials in the

chamber.

Go

TriaNo Go

Trial

A B

50

2.1.4 5C-CPT Task Structure

The task structure is shown in Figure 2.1. The animal initiates a trial by nosepoke into the

magazine tray. A variable inter trial interval (ITI) of 7-12 s follows in which response in any one

of the five is counted as a premature response and a 5s time out follows. If no response is

made the software randomly presents a stimulus in one of the five holes (go trial; 70%

weighting) or to present the light stimulus in all 5 holes (no go trial; 30% weighting). The

stimulus will stay on for 1s in the standard version of the task. The limited hold is a 5s timer from

stimulus presentation during which responses can be made. In go trials, response in the same

hole as the light was presented is rewarded, and a response in any other hole or no response

during the limited hold is punished with a time out. During time out the house light is illuminated

for 5s. In no go trials a response in any hole during the limited hold is punished and no

response is rewarded. After the reward is dispensed or time out concludes, the magazine tray is

lit and a response is needed to initiate the next trial. The task lasts either 120 trials or 30

minutes, whichever comes first.

Figure 2.3: 5C-CPT Task Structure. Ovals represent timing points, rectangles represent task measures

and triangles represent outputs to the subject.

51

2.1.5 5C-CPT Training

Animals were trained in a similar way to previously published (Barnes et al., 2012a, Barnes et

al., 2012b, Tomlinson et al., 2014, Tomlinson et al., 2015). They started with only go trials at a

stimulus duration and limited hold of 60 s, which were progressively shortened to 1 s (Table

2.1). The median duration from beginning of training to final criteria was 164 days in a

representative cohort (Table 2.1). Once the animals had reached criterion, performance was

maintained by once weekly training until and between testing.

52

Table 2.1: Training stages for 5C-CPT. Animals are progressively introduced to the different elements of

the task. Firstly nose poking response to a stimulus (stage 1-3) then no go trials (stage 4-5), then a

variable stimulus duration (stage 6-7) then final task criteria with short stimulus duration and a longer

variable inter-trial interval (stage 8).

Training Stage Stimulus Duration

(s)

Limited Hold

(s)

ITI (s) Criteria Trial Types

1 60 60 2 30 correct Go Only

2 45 45 2 30 correct

3 30 30 2 30 correct

4 10 12 5 <40% FA

>70%

Accuracy

<25%

Omissions

>50 Trials

Go and No

Go

64% Go Trials

5 8 10 5 <40% FA

>70%

Accuracy

<25%

Omissions

>50 Trials

p[HR]>p[FA]

6 5 5 5-7 <40% FA

>70%

Accuracy

<25%

Omissions

>50 Trials

Go and No

Go

70 % Go

Trials

53

7 2 5 5-7 <40% FA

>70%

Accuracy

<25%

Omissions

>50 Trials

8 1 5 7-12 <40% FA

>70%

Accuracy

<25%

Omissions

>50 Trials

0 5 0 1 0 0 1 5 0

0

2

4

6

8

T ra in in g L e n g th (D a y s )

Tra

inin

g S

tag

e

Figure 2.4: Representative progression of training. The median number of days from the start of

training to reaching criteria for each stage for n=40 animals (cohort used in Nicotine/Caffeine, EVP-

6124 and C-Fos Studies).

2.1.1 Signal Detection Theory

There are multiple versions of the clinical CPT available, but they are all unified by the principle

of detecting the difference between a target stimulus and a non-target stimulus (Young et al.,

2009, Riccio et al., 2002). In response to these two events, there can be four possible

54

responses. A target stimulus can be correctly responded to (hit) or not responded to correctly/

not responded to (miss) and non-target stimuli can either be not responded to (correct rejection)

or erroneously responded to (false alarm). Signal detection theory is used to interpret these

different responses and estimate the traits of sensitivity and bias to the stimuli (Marston, 1996,

Young et al., 2009). Sensitivity is how well the subject can determine the difference between

target and non-target stimuli. To have high sensitivity the number of hits needs to be high, as

well as the number of false alarms, need to be low. There are two ways to estimate the

sensitivity. If the hits and false alarms are normally distributed, then d′ can be used (calculation

in Table 2.2). If not then the sensitivity index (SI) must be used:

( ) ( )

When the 5C-CPT was first proposed Young et al. (2009) suggest SI was most suitable to avoid

assumptions of normality. However, in later papers, they began to use d′ as the data was in fact

normally distributed and this is a more translatable measure which is commonly used in the

clinical versions of CPT (Young et al., 2013). To estimate bias responsivity index is used. This

variable is necessary for establishing if the animals have a tendency to respond to all stimuli

(liberal response strategy) or withhold from all stimuli (conservative response strategy) which

the factors d′ and SI do not account for (Young et al., 2009, Young et al., 2013).

d′ is calculated as the z score of pHR minus the z score of pFA, as shown in Table 2.2. The z

scores are calculated as the individual‟s performance minus the average performance of the

cohort (across all treatments) divided by the standard deviation of the cohort.

Table 2.2: Summary of measures in the 5C-CPT, how they are calculated and what they mean.

Correct, Incorrect, Missed, Premature Response, False Alarm and Correct Rejection are as shown

in Figure 2.3.

Measure Calculation Meaning

Go Trials Accuracy

Selective/

Sustained

Attention

Omissions

Sustained

attention (and/or

motivation and/or

sedation)

Hit Rate (p[HR])

Used in signal

detection theory

55

calculations

Premature

Responses (PR)

Number of premature responses Waiting Impulsivity

(Impulsive Action)

Correct Latency Average time from stimulus to correct

response

Processing speed/

Motor Effects

Incorrect Latency Average time from stimulus to

incorrect response

Processing speed/

Motor Effects

Magazine Latency

(Go)

Average time from correct response

to reward collection

Motivation/ Motor

Effects

No Go Trials Probability of

False Alarms

(p[FA])

Response

Disinhibition

(Impulsive Action)

False Alarm

Latency

Average time from stimulus to false

alarm response

Processing speed/

Motor Effects

Magazine Latency

(No Go)

Average time from correct rejection to

reward collection

Motivation/ Motor

Effects

Signal

Detection

Theory

d′ ( ) ( ) Vigilance

Responsivity

Index (RI)

( )

Response Bias/

Strategy

2.1.2 Variable Stimulus Duration

In Chapters 4 and 5 a variable stimulus duration was used along with extended session length

and number of trials. This was done to provide a much greater challenge to the animals when

tested with nicotinic compounds. This has been done for a number of different assessments of

different assessments of nicotinic compound effect . The same stimulus durations (0.75, 1.25

and 2s), number of trials (250) and length of session (60mins) were previously used for testing

nicotinic acetylcholine receptor agonists in mice performing the 5C-CPT (Young et al., 2013).

The increase in attentional load promotes omissions in α7 nicotinic acetylcholine receptor

knock-out mice more than controls suggesting a link between high attentive load and the

nicotinic receptor system. We aimed to examine this further.

56

2.1.3 Division into subgroups

Animals were divided into high and low performing groups based on performance when treated

with the vehicle data of each study. The best performing ~25% of animals for d′ and accuracy

formed the high-attentive (HA) group and the worst performing ~25% of animals formed the low-

attentive (LA) group. This was designed based on an upper/lower quartile split, which

represents a careful balance between having significantly different groups and the number of

animals used (see Hayward et al. (2016)). To achieve this, animals were ordered by d′ and the

highest/ lowest ~25% animals were chosen, providing their accuracy was also above/below the

cohort average. This method is based on Blondeau and Dellu-Hagedorn (2007) dimensional

analysis grouping to produce combinations of symptoms based on the topology of the whole

cohort. The number of subjects used for HA/ LA comparison was based on examples from the

literature where group sizes are typically 8-12 (Dalley et al., 2007, Fernando et al., 2012,

Blondeau and Dellu-Hagedorn, 2007). For testing which involved a variable stimulus duration

the most challenging stimulus duration of 0.75 s was used to group the animals as it represents

the highest attentive load therefore causing the animals to separate most clearly.

57

3 Pharmacological Validation of a Refined

Low Attentive Model using

Methylphenidate and Atomoxetine

Authors: Andrew Hayward, Lisa Adamson, Joanna C. Neill

[Unpublished data]

3.1 Contributions

Andrew Hayward and Joanna C. Neill planned the study. Training of the animals was completed

by Andrew Hayward and Lisa Adamson. The study and data analysis was conducted by Andrew

Hayward. The manuscript was prepared by Andrew Hayward and Joanna C. Neill.

58

3.2 Context

As Chapter 1 has outlined, grouping animals by performance in 5 choice tasks offers many

advantages over other methods of modelling, particularly in low attention as the causes of

clinical inattention are still poorly understood. Low attentive models in the 5C-CPT exist in the

literature, here we repeat and refine one of the models to establish a model with the aim of

higher translation and a clearer deficit.

3.3 Abstract

Inattention is a symptom in a number of conditions including attention deficit hyperactivity

disorder, schizophrenia, bipolar disorder and Alzheimer‟s disease. Grouping animals based on

performance in an attentive task (5 choice tasks) enables a low attentive model where the

mechanism of action is not assumed. We present an improved method of grouping the animals

to produce a larger difference between groups and thus a greater understanding of the

mechanism of action of pharmacological agents that enhance attention. Adult female Lister

Hooded rats were trained to perform the 5 Choice – Continuous Performance Task (5C-CPT)

and tested with methylphenidate 0.5, 1 and 2 mg/kg or vehicle over four weeks. The order of

the treatments was randomised and counterbalanced. The 10 animals with the highest vigilance

(d′) and attention (accuracy) following vehicle treatment were classified as high attentive (HA)

and the 10 with the lowest performance were low attentive (LA). In HA animals,

methylphenidate reduced accuracy and d′ as well as increasing omissions, however, in LA

animals accuracy, and d′ were increased and omissions reduced. The same test was performed

for atomoxetine (0.5, 1 and 2 mg/kg) which also reduced d′ in HA animals, and incresed both

accuracy and d′ in LA animals. Atomoxetine also reduced the number of premature responses

in the LA animals. These results were largely in agreement with the findings of previous studies

of methylphenidate in LA animals separated according to these measures. However, we

additionally observed a reduction in omitted trials in LA animals following methylphenidate, likely

due to our removal of average performing animals resulting in a clearer separation of the two

groups. These findings suggest that future studies may benefit from taking a similar approach

when studying the differences in HA and LA animals, therefore this approach is taken

throughout the work presented in this thesis

59

3.4 Introduction

Attention can be defined as the ability to filter out irrelevant stimuli in order to optimise limited

neurochemical resources (Luck and Gold, 2008). Inattention is a core symptom of attention

deficit/ hyperactivity disorder (ADHD) which affects 5-8% of school-aged children and persists

into adulthood in roughly 50% of cases (Faraone et al., 2000, Barkley et al., 1990, Fayyad et al.,

2007, Bari et al., 2008). ADHD is most commonly separated into three sub-types; 1.

Predominantly inattentive (ADHD-I), 2. Predominantly hyperactive/impulsive (ADHD-HI) 3.

Combined type presentation (ADHD-C) (American Psychiatric Association., 2013). Subtypes in

children are often unstable, and inattentive symptoms increase with age, and impulsive

symptoms reduce, highlighting the importance of understanding inattention in adults (Larsson et

al., 2011). Inattention is also an important symptom in highly disabling conditions such as

schizophrenia, bipolar disorder and Alzheimer‟s disease (Luck and Gold, 2008, Foldi et al.,

2002). Therefore, increased understanding of the neurobiology of attention has an important

and broad application.

Methylphenidate is the first line treatment in the National Institute of Clinical Excellence (NICE)

guidelines (Palanivel et al., 2009). Methylphenidate (Ritalin™) acts via inhibition of

noradrenaline (NA) and dopamine (DA) transporters reducing their removal and increasing

levels at the synapse (Heal et al., 2009, Heal et al., 2008). It improves inattention in around 70-

80% of ADHD patients but due to increased dopamine in the mesolimbic system has addictive

potential. However, at clinical doses, it has been shown to reduce impulsivity in clinical studies

(Heal et al., 2009, Winstanley, 2011). Due to fear of misuse or diversion of the medication, it is

contraindicated in those with a history of drug addiction. The second line treatment for ADHD is

the non-stimulant, atomoxetine which is a selective noradrenaline transporter inhibitor which

causes an increase the length of time noradrenaline remains at the synapse. It also indirectly

increases dopamine, but only in the prefrontal cortex eliminating abuse liability (Bymaster et al.,

2002, Heal et al., 2009). Atomoxetine also alleviates impulsive measures in the continuous

performance, stop signal, and delay discounting tasks in humans (Barry et al., 2009,

Chamberlain et al., 2006). However, for attentive measures response rates and effect sizes are

lower than for stimulants (Heal et al., 2009, Cunill et al., 2013). A meta-analysis of atomoxetine

treatment concluded that the risk-benefit ratio for atomoxetine is even lower in an adult ADHD

population than in children as it has only a minimal effect on clinically meaningful endpoints

(e.g. job performance or sociability) (Cunill et al., 2013). With adult ADHD being increasingly

recognised to have a significant socioeconomic burden, it is important that we find new

pharmacological treatments with improved efficacy, safety and tolerability in the adult ADHD

population, or enhance the use of available medication by delivering the most appropriate

treatment to each subtype of patient (Kooij et al., 2010).

To enhance our understandings of the neurobiology underpinning attention, animal models of

low attention are required. Many models use pharmacological, physical or genetic

60

manipulations to impair attention in animals (reviewed by Bari and Robbins (2011)). However, in

conditions which are highly diverse and the causes not fully understood, these methods are

restrictive to the possible mechanisms studied (Jupp et al., 2013, Hayward et al., 2016).

Grouping animals based on performance in a behavioural task enables a holistic approach with

minimal assumptions of the mechanism (for review see Hayward et al. (2016)).

Tomlinson et al. (2014) recently demonstrated the flexibility of this method by producing low

attentive and high impulsive groups using the 5 choice continuous performance task (5C-CPT).

The 5C-CPT is designed to be highly translatable from the widely used clinical continuous

performance task (Young et al., 2009, Riccio et al., 2002, Lustig et al., 2013). This task has both

stimuli that require a response (go trials) and similar stimuli that require withholding of a

response (no-go trials) allowing assessment of attention, impulsivity and vigilance (Young et al.,

2009).Tomlinson et al. (2014) then proposed a third model of ADHD combined subtype-like

animals, showing that by careful choice of thresholds, different phenotypes can be produced

(Tomlinson et al., 2015). However, central to these studies is the very careful choice of

variables, for example in the ADHD-C type model the criteria were: accuracy <90%, sensitivity

index <0.3 and probability of false alarms >0.5, but no justification was given for the choice of

these thresholds. The same is true of a number of proposed behavioural separation models

(Hayward et al., 2016, Grottick and Higgins, 2000, Puumala et al., 1996, Paterson et al., 2011).

However, more flexibility is possible if animals are chosen based on mean, median, quartiles or

other percentiles of performance (Dalley et al., 2007, Diergaarde et al., 2008, Winstanley et al.,

2010). This can give consistent numbers in each group, which makes statistical analysis more

robust and allows easier translation of results between labs, strains and species. Improving the

robustness and reproducibility of animal models is essential to produce results that can be

translated to humans and so enable the development of enhanced therapeutic strategies.

Our objective was to determine if statistically significant differences in attention and vigilance

altered the effect of attention promoting compounds. We aimed to validate an alternative and

more flexible method to determine whether it can produce a model with predictive validity. To

judge validity, we compare to a previous method of separation by performance in 5C-CPT used

by Tomlinson et al. (2014) and the response to first and second-line treatments for ADHD

methylphenidate and atomoxetine. This method would allow the testing of novel compounds in

future experiments to advance understanding of attention.

3.5 Methods

3.5.1 Animals

38 female Hooded Lister rats (Charles River, UK; weighing 210 ± 20 g at the start of training)

were housed in groups of four in individually ventilated cages with two levels (Techniplast, UK)

with a standard 12-hour light-dark cycle (lights on 7:00 am). The environment was maintained at

21 ± 2 °C, 55 ± 5 % humidity. Diet was standard rat chow (Special Diet Services, UK) controlled

61

to maintain 90% free feeding weight throughout training and testing (usually 10 g/rat/day).

Water was available ad libitum for the duration of the study. All experiments were conducted

between 07:00-19:00 in the light phase and were conducted in accordance with the UK Animals

(Scientific Procedures) 1986 Act and University ethical guidelines.

3.5.2 Apparatus

Tests were conducted in eight standard nine hole chambers (Campden Instruments Ltd, UK)

with holes 2, 4, 6, and 8 occluded. Chambers were controlled and data collected by ABet II

Touch software (Lafayette Instrument Company, IL, USA). Animals were rewarded with 45 mg

rodent reward pellets (Test Diet, MO, USA).

3.5.3 5C-CPT Training Procedure

Animals were trained as described previously in Barnes et al. (2012a), Barnes et al. (2012b)

and Appendix. In brief, animals were trained for approximately 24 weeks to stability on final

training parameters of 1 s stimulus duration (SD), 2 s limited hold (LH), 5 s variable inter-trial

interval (vITI). This was done in a series of stages, gradually shortening the SD and LH, each

stage was achieved by meeting criteria of >70% accuracy, <25% omissions and >65% correct

rejections. Once meeting final criteria, animals were trained once per week to maintain

performance.

3.5.4 Testing Procedure

Testing 5C-CPT parameters were the same as the training procedure, with two alterations: a

reduced 1 s SD; and increased 7-12 s variable ITI. Methylphenidate and then atomoxetine were

tested in the same 38 animals following a two-week washout, a full health check and a 5C-CPT

training session to ensure performance was not different to before the study. A within subjects

latin square design was used to randomise the order of drugs. Each test day was separated by

a seven day washout period. Atomoxetine hydrochloride (Sigma, UK) and methylphenidate

hydrochloride (Sigma, UK) solutions were dissolved in saline (0.9% NaCl) as a dose of free

base to produce concentrations of 0.5, 1 and 2 mg/ml for each drug. Compounds were

administered via a 1 ml/kg intraperitoneal injection 30 minutes before testing.

3.5.5 Division into Subgroups


with vehicle (0.9% saline). The best performing 10 animals for d′ and accuracy formed the high-

attentive (HA) group, and the worst performing 10 animals formed the low-attentive (LA) group.

This is an upper/lower quartile split, which represents a careful balance between having

significantly different groups and the number of animals used (see Introduction). To achieve

this, animals were ordered by d′, and the highest/ lowest 10 animals were chosen, providing

their accuracy was also above/below average. In the methylphenidate group, six animals were

62

excluded due to >80% omissions or low trial numbers during vehicle treatment and for

atomoxetine four were excluded for the same reason.

3.5.6 Statistical Analysis

The method for calculation of all parameters is described in detail in Tomlinson et al. (2014) and

Young et al. (2013). The signal detection theory measure d′ was used in this case only after

confirming that probability hit rate (pHR) and the probability of false alarms (pFA) were normally

distributed, as described in Young et al. (2013). Here, d′ is calculated as described in Young et

al. (2013) as the z-score of pHR minus the z-score of pFA.

( ) ( )

( )

Using d′ is a further translational step towards making the rodent 5C-CPT closer to the human

CPT, compared to using sensitivity index (Young et al., 2013). Measures were calculated

individually for each rat at each SD for accuracy, omissions, pHR, pFA, d′ and responsivity

index (RI) where z scores were based on whole cohort data. After relevant measures were

calculated, data were analysed using InVivo Stat (Version 3.4), the most appropriate statistical

package for in vivo behavioural experiments (Clark et al., 2012). The analysis used a two-way

repeated measures mixed model ANOVA with GROUP as the treatment factor and

TREATMENT as the repeated factor for each measurement followed by LSD planned

comparisons for each treatment compared to vehicle. Normality and Sphericity of the data were

judged using the normality plot and the residual vs. predicted plots. Arcsine, square root or

Log10 transformations were used where appropriate. Data are presented as F values, and p

values for TREATMENT*GROUP interactions and effects are considered statistically significant

if the p-value from Fisher protected least squared difference (LSD) planned comparisons were <

0.05.

63

3.6 Results

3.7 Methylphenidate

3.7.1 Group Performance

It is important to establish overall group performance before separating the animals into groups,

particularly when within group reference points are used (Hayward et al., 2016), using

parameters such as upper/lower quartiles (Table 3.1).

Table 3.1: Group vehicle treated baseline before HA/LA grouping for methylphenidate

Measure Mean Standard

error

Median Lower

quartile

Upper

quartile

Accuracy (%) 93.9 1.06 96.2 91.3 100

Omission (%) 41.4 2.50 40.1 29.2 54.2

Premature

(Number)

13.7 2.15 7 5 23

p[FA] 0.360 0.0339 0.400 0.171 0.48

p[HR] 0.550 0.0227 0.583 0.458 0.644

d' 0.0787 0.136 -0.0183 -0.509 0.306

Responsivity Index -0.106 0.0541 -0.0846 -0.374 0.0875

Processed Trials

(Number)

101 3.42 107 85 120

Correct Latency (s) 0.885 0.0172 0.898 0.799 0.947

Incorrect Latency

(s)

0.797 0.105 0.997 0 1.38

Magazine Latency

(Go) (s)

1.81 0.525 1.16 1.05 1.48

False Alarm

Latency (s)

0.793 0.0263 0.8 0.704 0.901

64

Magazine Latency

(No Go) (s)

14.4 2.94 8.63 1.62 18.8

3.7.2 High/ Low Comparison

For Methylphenidate the ANOVA showed a number of significant group* treatment interactions,

which were followed by LSD planned comparisons to vehicle (saline) control. Firstly, accuracy

(Figure 3.1A, F1,18=13.4,p=0.0018) was significantly reduced by 0.5 mg/kg in HA animals

(p<0.05) and in LA animals increased at 0.5 and 1 mg/kg (p<0.05). Omissions (Figure 3.1B,

F3,54=4.59, p=0.0062) showed a significant increase at 2 mg/kg in HA animals, but reduced by 1

(p<0.05) and 2 mg/kg in LA animals (p<0.01). Hit Rate (Figure 3.2C, F3,54=5.79,p=0.0017) was

reduced by 2 mg/kg in HA animals (p<0.01), but increased by 0.5 (p<0.05), 1 (p<0.01) and 2

mg/kg (p<0.01) in LA animals. d′ (Figure 3.2A, F3,54=2.82, p=0.0477) was increased by 0.5

(p<0.05) and 1 mg/kg (p<0.01) in LA animals, but in HA animals d′ was reduced by 2 mg/kg

(p<0.01).

In the group level effects, HA animals showed significantly higher accuracy (Figure 3.1A,

F1,18=13.4,p=0.0018) and d′ (Figure 3.2A, F1,18=5.9, p=0.0258) compared with LA animals. LA

animals showed significantly higher premature responses (Figure 3.1C, F1,18=6.98, p=0.0166),

incorrect latency (Figure 3.3B, F1,18=4.5, p=0.0481) and magazine latency for no-go trials

(Figure 3.3D, F1,18=4.75, p=0.0429). Significant reduction of correct latency with higher doses

was seen irrespective of group (Figure 3.3A, F3,54, p=0.0335) which in planned comparisons

was significant for 1 mg/kg dose (p<0.01).

65

A c c u r a c y

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P re m a tu r e R e s p o n s e s

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C P r o c e s s e d T r ia ls

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1 0 5

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Nu

mb

er

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H A L A

D

Figure 3.1: Methylphenidate increased accuracy at 0.5 and 1 mg/kg in LA animals. (A) 0.5 mg/kg

increased accuracy in LA animals, but reduced it in HA animals, 1 mg/kg increased accuracy in LA

animals. (B) Methylphenidate increased omissions in HA animals at 2 mg/kg, but reduced omissions in LA

animals at 1 and 2 mg/kg. (C) Significant differences were observed between HA and LA animals, but

were not significantly altered by methylphenidate. (D) No significant changes were seen on number of

trials processed. Data are shown as mean ± SEM of n=10 HA, and n=10 LA animals each tested at all

doses. * and ** denote significance levels p< 0.05, 0.01 respectively for LSD planned comparisons of

vehicle treated LA animals to the three treatment levels following significance in GROUP*TREATMENT

interaction in a repeated measures two-way ANOVA and #, ## and ### denote the same, but to the HA

vehicle.

66

d '

-0 .7 5

-0 .5 0

-0 .2 5

0 .0 0

0 .2 5

0 .5 0

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ore

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# #

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A

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C p [F A ]

0 .2

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0 .5

0 .6

Pro

ba

bil

ity

0 0 .5 1 2 0 0 .5 1 2

H A L A

D

Figure 3.2: Vigilance is increased by 0.5 and 1 mg/kg in LA animals. (A) In HA animal‟s d′ is reduced

by 2 mg/kg methylphenidate and In LA animals it is increased at 0.5 and 1 mg/kg. (B) No significant

differences in responsivity index. (C) In HA animals p[HR] is reduced by 2 mg/kg methylphenidate, and in

LA animals 0.5, 1 and 2 mg/kg increased p[HR]. (D) No significant differences were observed in p[FA].

Data are shown as mean ± SEM of n=10 HA, and n=10 LA animals each tested at all doses. * and **

denote significance levels p< 0.05, 0.01 respectively for LSD planned comparisons of vehicle treated LA

animals to the three treatment levels following significance in GROUP*TREATMENT interaction in a

repeated measures two-way ANOVA and #, ## and ### denote the same, but to the HA vehicle.

67

C o rr e c t L a te n c y

0 .7 5

0 .8 0

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0 .9 0

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e (

ms

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A

+

+

In c o r re c t L a te n c y

0 .5

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F a ls e A la rm L a te n c y

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e (

ms

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0

5

1 0

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2 0

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e (

ms

)

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H A L A

+

+

D

Figure 3.3: No significant differences were seen on the latency measures due to methylphenidate.

(A) No significant differences in correct latency due to methylphenidate. (B) HA and LA were significantly

different to one another for incorrect latency, but this was not dependent on treatment. (C) No significant

differences in magazine latency for go trials. (D) For False alarm latency, no significant differences were

seen. (E) For Magazine latency in no-go trials, a significant difference between HA and LA animals was

seen, but it was not dependent on treatment. + denotes a significant difference between groups

irrespective of treatment. Data are shown as mean ± SEM of n=10 HA and n=10 LA animals each tested at

all doses.

3.8 Atomoxetine


Table 3.2 is presented below to show the vehicle treated baseline performance of the whole

cohort before behavioural grouping.

Table 3.2: Group vehicle treated baseline before HA/LA grouping for atomoxetine.

Measure Mean Standard

error

Median Lower

quartile

Upper

quartile

Accuracy (%) 87.9 3.02 91.6 79.1 99.1

Omission (%) 46.4 3.68 44.4 35.4 59.3

Premature 11.7 2.40 8.5 3 18

68

(Number)

p[FA] 0.334 0.0349 0.296 0.2 0.452

p[HR] 0.486 0.0425 0.547 0.316 0.631

d' 0.0389 0.263 0.0681 -0.688 0.922

Responsivity

Index

-0.192 0.0662 -0.222 -0.391 -0.037

Processed

Trials (Number)

111 2.59 115 103 120

Correct Latency

(s)

0.848 0.0272 0.872 0.779 0.915

Incorrect

Latency (s)

0.756 0.138 0.718 0.051 1.30

Magazine

Latency (Go) (s)

1.72 0.277 1.23 1.08 1.70

False Alarm

Latency (s)

0.753 0.0321 0.756 0.636 0.854

Magazine

Latency (No

Go) (s)

20.9 4.69 14.6 5.30 30.2


For atomoxetine the ANOVA showed a number of significant group*treatment interactions, LSD

planned comparisons revealed significant differences for each dose compared to saline treated

control. Accuracy (Figure 3.4A, F3,54=3.61,p=0.0189) was increased by 0.5 (p<0.01) and 1

mg/kg (p<0.05) in LA animals. Premature responses (Figure 3.4C, F3,54=3.56,p=0.0201) were

reduced by 0.5 and 2 mg/kg in LA animals (both p<0.05). d′ (Figure 3.5A, F3,54=3.56,p=0.0201)

was reduced by 2 mg/kg in HA animals (p<0.01) and increased by 0.5 and 1 mg/kg (both

p<0.05) in LA animals.

In group level effects, HA animals showed significantly higher accuracy (Figure 3.4A,

F1,18=19.96,p=0.0003), hit rate (Figure 3.5C, F1,18=9.4,p<0.0067), and d′ (Figure 3.5A,

F1,18=11.32,p=0.0035). However, LA animals showed significantly higher omissions (Figure

69

3.4B, F1,18=5.58,p=0.0296) and premature responses (Figure 3.4C, F1,18=4.94,p=0.0394).

Treatment effects irrespective of group were seen for magazine latency in go trials (Figure 3.6C,

F3,54=4.38,p=0.0079) which was reduced by 0.5 mg/kg of atomoxetine (p<0.01); false alarm

latency (Figure 3.6C, F3,54=2.88, p=0.0441) but was not significant in planned comparisons and

magazine latency in no go trials (Figure 3.6D, F3,54=2.89,p<0.0437) which was significantly

reduced by 0.5 (p<0.05) and 2 mg/kg (p<0.01) of atomoxetine.

A c c u r a c y

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mb

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D

Figure 3.4: In LA animals Accuracy is increased and premature responses reduced by atomoxetine.

(A) Accuracy was increased by 0.5 and 1 mg/kg of atomoxetine in LA animals. (B) HA animals had

significantly fewer omissions, but this was not dependent on treatment. (C) In LA animals 0.5 and 2 mg/kg

of atomoxetine reduced the number of premature responses. (D) No significant differences were seen for

the number of processed trials. Data are shown as mean ± SEM of n=10 HA and n=10 LA animals each

tested at all doses. * and ** denote significance levels p< 0.05, 0.01 respectively for LSD planned

comparisons of vehicle treated LA animals to the three treatment levels following significance in

GROUP*TREATMENT interaction in a repeated measures two-way ANOVA and #, ## and ### denote the

same, but to the HA vehicle group. + denotes a significant difference between groups irrespective of

treatment.

70

d '

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ba

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Figure 3.5: Atomoxetine reduced d′ in HA animals, but increased it in LA animals. (A) In HA animals

2 mg/kg of atomoxetine reduced d′ significantly, in LA animal‟s 0.5 and 1 mg/kg increased d′. (B) No

significant effect was seen on responsivity index. (C) No significant effect of atomoxetine was seen on hit

rate. (D) Atomoxetine has no significant effect on the probability of false alarm. Data are shown as mean ±

SEM of n=10 HA and n=10 LA animals each tested at all doses. * and ** denote significance levels p<

0.05, 0.01 respectively for LSD planned comparisons of vehicle treated LA animals to the three treatment

levels following significance in GROUP*TREATMENT interaction in a repeated measures two-way ANOVA

and #, ## and ### denote the same, but to the HA vehicle group. + denotes a significant difference

between groups irrespective of treatment.

71


0 .8

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e (

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A In c o r re c t L a te n c y

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)

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D

Figure 3.6: Atomoxetine had no significant effects on the latency measures in HA or LA animals.

3.9 Discussion

This study has shown that methylphenidate increases attention (accuracy) and vigilance (d′)

and reduces omissions (low attention/ motivation) in LA animals. The opposite effect was seen

in HA animals which had reduced performance on accuracy and d′. Atomoxetine also increased

accuracy and d′ in LA animals, but without a significant effect on omissions. However, it also

reduced premature responses in the same group. In HA animals, atomoxetine only reduced d′.

Therefore, in LA animals, both compounds showed an effect to improve selective attention and

vigilance. Methylphenidate also improved the ability to sustain attention, and atomoxetine

reduced waiting impulsivity.

The aim of this study was to assess how a novel grouping method altered the study of well-

characterised attention promoting compounds methylphenidate and atomoxetine. Tomlinson et

al. (2014) showed that in LA animals accuracy was significantly increased by 2 mg/kg

methylphenidate only and vigilance was significantly increased by 0.5 and 1 mg/kg doses, they

did not see a significant difference in HA animal performance. In LA animals our study agrees

with this entirely for the vigilance measure d′ (Figure 3.2A), but in the present study accuracy

was not affected at 2 mg/kg but was significantly increased at lower doses of 0.5 and 1 mg/kg

(Figure 3.1A). In our study vehicle accuracy was higher in HA animals and lower in LA animals

(HA: 98.1, LA: 77.64) when compared to Tomlinson et al. (2014) (HA: 96.5, LA 84.2), this may

72

be explained by our use of only the top/ bottom 10 animals and removing the animals in

between. This method produces a stronger separation between animals. The significant

reduction of accuracy by 0.5 mg/kg of methylphenidate may be due to the high baseline

accuracy and suggests that very high performing animals are impaired only by low doses of

methylphenidate. Tomlinson et al. (2014) also did not report any significant changes in

omissions due to treatment. Here we see a significant increase in HA animals given 2 mg/kg

methylphenidate, but a dose-dependent reduction in LA animals. Due to the stricter criteria used

in this study, there is a larger difference between the animals than in Tomlinson et al. (2014)

allowing this subtle, but opposing effect to be revealed. The theory of optimal arousal posits

that too low levels of forebrain monoamines result in an inability to maintain focus and that

increasing the level of monoamines improves the ability to sustain attention, but too much

causes too much reorienting of attention leading to distractibility (Wood et al., 2014). This is

often called an inverted U-shaped relationship or the Yerkes-Dodson effect (Wood et al., 2014).

So, if HA and LA animals had different levels of forebrain monoamines, this would explain why

the same compound could cause opposing effects. In LA animals it is improving towards the

optimum, where in HA it is moving them towards distractibility, causing more omitted trials or

less accurate performance. It has not yet been tested if 5C-CPT behavioural grouping can

produce groups with different baseline monoamine signalling. However, animals grouped by the

waiting impulsivity measure of premature responses have been shown to have significantly

reduced D2/3 receptors in the nucleus accumbens (Dalley et al., 2007). Similar studies to

analyse the receptor abundance and neurotransmitter levels in animals grouped in the 5C-CPT

would be highly informative. This could also include analysis of the neural circuitry involved in

response to the task as has been done for animals grouped for waiting impulsivity in the 5C-

SRTT by recording the extracellular neural potentials (Donnelly et al., 2014, Donnelly et al.,

2015).

In summary, we have shown that, with a different method of grouping animals based on

behavioural performance, we can recreate the major findings a previous paper. This method of

modelling has also shown novel differences not seen in the previous study exemplifying the

flexibility of this method of modelling. Future studies should focus on expanding upon this

method to allow novel compounds to be studied as well as increasing our understanding of what

can cause differences in baseline performance and how that mediates differences in response

to pharmacological treatment.

73

4 Nicotine and Caffeine in the 5 Choice

continuous performance task

Andrew Hayward, Lisa Adamson and Joanna C. Neill

[Unpublished data]

4.1 Contributions


by Andrew Hayward and Lisa Adamson. The study was conducted by Andrew Hayward. The

manuscript was prepared by Andrew Hayward and Joanna C. Neill.

74

4.2 Context

Chapter 3 established the validity of the model to produce a deficit which is sensitive to

compounds used to treat inattention clinically and that the results are comparable to a previous

model, but with a larger deficit and higher translational potential. We, therefore, extend this to

study two compounds that have not been tested in a behaviourally grouped model of low

attention, nicotine and caffeine.

4.3 Abstract

Inattention is a debilitating symptom in many psychiatric conditions. Grouping animals by

performance has emerged as a powerful method to analyse the effect of compounds on low or

high attentive individuals with strong predictive validity. Nicotine and caffeine are the most

widely used pro-cognitive compounds. Nicotine improves sustained attention in humans and

rodents, with a larger effect in low performing individuals. The effect of caffeine on attention has

been poorly studied in rodents compared to nicotine, but also seems to improve sustained

attention. The effect of acute doses of nicotine (0.01, 0.02 mg/kg) and caffeine (2.5, 5 mg/kg)

were assessed in adult female Lister hooded rats using a variable stimulus duration (vSD; 0.75,

1.25 and 2 s) version of the 5 choice continuous performance task (5C-CPT). This showed that

0.02 mg/kg nicotine reduces omissions (sustained attention), increases premature responses

(impulsivity) and increases the probability of false alarms (pFA; impulsivity). Caffeine (2.5

mg/kg) increased pFA and reduces latency to false alarm (impulsivity). We also grouped the

adult female Lister Hooded rats by performance in measures of selective attention and vigilance

to produce low attentive (LA) and high attentive (HA) groups. This grouping showed the

increase of pFA due to 0.01 mg/kg nicotine and 2.5 mg/kg caffeine is more distinct in HA than

LA animals. These changes show that nicotine and caffeine promote impulsivity in female Lister

hooded rats. Future studies should aim to use selective agonists of the nicotinic acetylcholine

receptors to understand their involvement in this effect.

75

4.4 Introduction

Nicotine is an active ingredient of tobacco and widely used in the form of cigarette smoking.

Nicotine acts at nicotinic acetylcholine receptors (nACh) to produce pro-cognitive effects such

as enhanced sustained attention and vigilance. These effects have been demonstrated in non-

smoking adults using the continuous performance task (CPT) (Rezvani and Levin, 2001,

Heishman et al., 2010, Levin et al., 1998). Pro-cognitive effects have also been shown in

rodents using the 5C-CPT (Young et al., 2013) and the 5 choice serial reaction time task (5C-

SRTT) (Grottick and Higgins, 2000). In healthy non-smokers, the attention-enhancing effects of

nicotine are most pronounced in low performing individuals (Wignall and de Wit, 2011, Poltavski

and Petros, 2006). Attention improvement has also been demonstrated using the 5C-SRTT in

male Lister Hooded rats where nicotine produces a greater improvement in low-performing

animals (Grottick and Higgins, 2000).

Caffeine is the active ingredient in coffee and energy drinks, making it one of the most widely

used psychoactive drugs in the world (Fulgoni et al., 2015). It is regularly used to promote

wakefulness, and this is achieved by non-selective antagonist action at adenosine A1 and A2A

receptors primarily (Ioannidis et al., 2014). Adenosine A1 receptors are present pre and

postsynaptically across many neurone types in the putamen, mediodorsal thalamus and

throughout the neocortex (Bauer et al., 2003). There are two subtypes of A2A receptors, the

typical, found in the striatum, and the atypical, located in the hippocampus and throughout the

cortex including the prefrontal cortex (Fredholm et al., 2003, Riccioni et al., 2010). Clinical

reports of its behavioural effect are mixed; with initial reports showing an improvement in ADHD

symptoms of inattention and low vigilance (Schnackenberg, 1973, Ioannidis et al., 2014), but

more recent reports have both supported and refuted this claim (reviewed in Ioannidis et al.

(2014)). In rodents, caffeine has been shown to reduce the number of omissions made in the

5C-SRTT at 2.5 mg/kg and severely impaired performance at 20 mg/kg (Bizarro et al., 2004).

However, another study using Long-Evans and CD rats found no significant changes of

omissions in either strain, but did report a significant reduction of correct latency at 3 mg/kg

dose (Higgins et al., 2007). In the same task, age-related reduction in performance is reversed

by caffeine including a reduction in omissions (Grottick and Higgins, 2002).

The aims of this study were to firstly assess if nicotine and caffeine cause alterations of

performance in the 5C-CPT when a variable stimulus duration is used in female Lister Hooded

rats. Secondly, we aimed to evaluate the effect of the two compounds in a population grouped

based on low attention and vigilance. We hypothesised that both compounds would improve

sustained attention shown by reduction of omissions and that for nicotine the effect would be

larger in low attentive animals as seen in clinical populations.

76

4.5 Experimental Procedures

4.6 Animals

37 female Lister Hooded rats (Charles River, UK; weighing 210 ± 20 g at the start of training)

were housed in groups of four in individually ventilated cages with two levels (GR1800 Double-

Decker Cage, Techniplast, UK) under a standard 12 hour light: dark cycle (lights on 7:00 am).

We used female rats as the 5C-CPT has been carefully validated in female rats in our


2012b). Also, male rats grow rapidly, which precludes social housing over the lengthy period it

takes to train and test rats in the 5C-CPT. Individual housing is stressful for this social species

and best avoided (Holson et al., 1991, Brown and Grunberg, 1995). The environment was

maintained at 21 ± 2 oC, 55 ± 5 % humidity. The diet was standard rat chow (Special Diet

Services, UK) controlled to maintain 90% free feeding weight throughout training and testing

(typically 10 g/rat/day). Water was available ad libitum for the duration of the study, except in

the operant chambers. All experiments were conducted between 09:00-17:00 in the light phase.

All experiments were conducted in accordance with the UK Animals (Scientific Procedures)

1986 Act and local University ethical guidelines.

4.6.1 Apparatus

Tests were conducted in eight standard nine-hole chambers (Campden Instruments Ltd, UK)

with holes 2, 4, 6 and 8 occluded. Chambers and data collection were controlled by ABet II

Touch software (Lafayette Instrument Company, IL, USA). Animals‟ behaviour was reinforced

with 45 mg rodent reward pellets (Test Diet, MO, USA).


Animals were trained as described in previously (Barnes et al. (2012a), Tomlinson et al. (2014)

and in Appendix). In brief, animals were trained for approximately 24 weeks to set criteria on

final training parameters of 2 s stimulus duration (SD), 2 s limited hold, 5 s inter-trial interval

(ITI). Training was conducted in a series of stages with successively longer SD and limited hold

where each stage was reached by meeting criteria of >70% accuracy, <25% omissions and

>65% correct rejections (see Barnes et al. (2012a), Barnes et al. (2012b)). Once rats reached

these final criteria, they were trained once per week to maintain performance and avoid over-

training.


Testing parameters were as per training, with a variable 0.75, 1.25 and 2 s SD and increased 7-

12 s variable ITI. As previous studies have found separable effects of nicotine in genetically

modified α7 receptor mutations at different SDs, a variable SD was used here (Young et al.,

77

2013, Young et al., 2007). To gather sufficient data for three SDs, the session was extended to

1 hour or 250 trials, whichever came first.

(-) Nicotine hydrogen tartrate salt (Sigma, UK) was prepared as a dose of free base to

concentrations of 0.1 and 0.2 mg/kg in saline solution (0.9% NaCl) and administered

subcutaneously in a volume of 1 ml/kg one hour before testing. Anhydrous caffeine (Sigma, UK)

was prepared in saline solution (0.9% NaCl) to concentrations of 2.5 and 5 mg/kg in a volume of

1 ml/kg 30 minutes before testing via the intraperitoneal route. The four treatments and a control

of saline solution were administered to each animal over 5 weeks. Each test day was separated

by a seven day washout period (no drug administration, or training in 5C-CPT). Treatment order

was randomised and counterbalanced using a within-subjects Latin Square design. For both

compounds doses were selected from previous effects in rats performing the 5C-SRTT (Bizarro

et al., 2004).



with vehicle (0.9% saline). The best performing ten animals for d′ and accuracy formed the high-

attentive (HA) group, and the worst performing ten animals formed the low-attentive (LA) group.


significantly different groups and the number of animals used (see Hayward et al. (2016)). To

achieve this, animals were ordered by d′, and the highest/ lowest ten animals were chosen,

providing their accuracy was also above/below average. As vigilance is a measure of overall

performance across go and no-go trials, accuracy was also used to give more weight to the

inattentive phenotype. Two animals were excluded for making >80% omissions.


The method for calculation of all parameters is described in detail in Tomlinson et al. (2014),

Young et al. (2013) and Appendix. The signal detection theory measure d′ was used in this case

only after confirming that probability hit rate (pHR) and the probability of false alarms (pFA) were

normally distributed, as described in Young et al. (2013). Here, d′ is calculated in the same way

as described in Young et al. (2013) as the z score of pHR minus the z-score of pFA.

( ) ( )

78

( )

Measures were calculated individually for each rat at each SD for accuracy, omissions, pHR,

pFA, d′ and responsivity index (RI) where z scores were based on full cohort data. After

important measures had been calculated, data were analysed using InVivo Stat (Version 3.4),

as the most appropriate statistical package for in vivo behavioural experiments (Clark et al.,

2012). Overall performance (Table 4.1) was compared using a one-way Analysis of Variance

(ANOVA) comparing treatment as a factor and using animal ID as a blocking factor to account

for the within-subjects design. Between-group comparisons used least square difference (LSD)

tests adjusted for multiple comparisons by a Benjamini-Hochberg‟s procedure to reduce the

false discovery rate. Measures compared by this method include accuracy and d′ for three SDs.

Grouped analysis used a two-way repeated measures mixed model ANOVA with GROUP as

the treatment factor and TREATMENT as the repeated factor for each measurement followed

by LSD planned comparisons for each treatment compared to vehicle. Normality and Sphericity

of the data were judged using the normality plot and the residual vs. predicted plots. Square

root, Arcsine, Log10 or RANK transformations were used when appropriate. Data are presented

as F values, and p values for TREATMENT*GROUP interactions and effects are considered

statistically significant if the p-value from Fisher protected least square difference planned

comparisons were < 0.05.

4.7 Results

4.7.1 Whole Group Baseline

In the whole cohort (Table 4.1), an ANOVA with LSD planned comparisons found that nicotine

significantly reduced omissions (F4=3.18, p=0.0156) at 0.2 mg/kg (p<0.05), but significantly

increased the number of premature responses (F4=3.29, p=0.0131) at 0.2 mg/kg (p<0.001),

increased responsivity index in response to 0.75 s SD (F4=2.48, p=0.0467) at 0.1 (p<0.05) and

0.2 mg/kg (p<0.01). In response to 1.25 s SD, responsivity index (F4=3.71, p=0.0067) was also

increased by 0.2 mg/kg (p<0.01). Nicotine also increased the probability of false alarms during

0.75 s SD (F4=2.48, p=0.0467) at 0.1 (p<0.01) and 0.2 mg/kg (p<0.05), 1.25 s SD probability of

false alarms (F4=3.71, p=0.0067) was also increased by 0.2 mg/kg (p<0.01) and false alarm

latency (F4=3.62, p=0.0077) was significantly reduced by 0.1 mg/kg (p<0.01). Nicotine also

reduced the number of trials completed in the first 20 minutes (F4=14.54, p=0.0001) at a dose

of 0.2 mg/kg (p<0.001). Caffeine had two significant effects on the whole cohort, it increased

probability of false alarms (F4=2.48, p=0.0467) during 0.75 s SD trials at 2.5 mg/kg dose

(p<0.05) and it reduced the time to make a false alarm response (F4=3.62, p=0.0077) for the

same dose (p<0.01).

79

Table 4.1: Whole cohort (n=35) 5C-CPT performance following vehicle compared to nicotine (0.1

and 0.2 mg/kg) and caffeine (2.5 and 5 mg/kg). Performance following the four treatments is compared

to performance after saline vehicle treatment. Significant Fisher protected LSD planned comparisons are

represented in bold, and asterisks denote the significance level. * p<0.05, ** p<0.01 and *** p<.001.

Vehicle Nicotine 0.1

mg/kg

Nicotine 0.2

mg/kg

Caffeine 2.5

mg/kg

Caffeine 5

mg/kg

Accuracy (%)

0.75 89.4 ± 1.7 84.1 ± 2.56 87.7 ± 1.94 90.6 ± 1.62 92.4 ± 1.2

1.25 94.7 ±

1.25 92.2 ± 1.5 92.8 ± 1.65 95.2 ± 0.821 95 ± 1.34

2 95.2 ±

1.26 93.1 ± 1.99 93.7 ± 1.36 96.6 ± 1.02 96 ± 1.08

Omissions

(%)

0.75 47.9 ±

2.76 44.4 ± 3.08 44.2 ± 3.31 46.8 ± 3.09

47.4 ±

2.91

1.25 40.3 ±

3.14 39.5 ± 2.99 34.3 ± 3.07

* 40.5 ± 2.84 40 ± 2.9

2 37.4 ±

3.14 34.4 ± 2.8 34.3 ± 3.25 37.7 ± 2.86

38.6 ±

2.78

Premature (Number) 9.66 ±

1.68 14.3 ± 2.68 19.7 ± 2.94

*** 12.3 ± 1.66

12.5 ±

1.81

d'

0.75 0.139 ±

0.149

-0.295 ±

0.224

-0.174 ±

0.221

-0.0985 ±

0.174

0.0738 ±

0.181

1.25 0.0185 ±

0.225

-0.262 ±

0.216

-0.177 ±

0.23

-0.132 ±

0.193

0.0951 ±

0.2

2 -0.0043 ±

0.204

-0.09 ±

0.238

-0.104 ±

0.211

-0.148 ±

0.215

0.0385 ±

0.179

Responsivity

Index

0.75 -0.387 ±

0.0487

-0.263 ±

0.0532*

-0.258 ±

0.0601**

-0.295 ±

0.0562

-0.326 ±

0.0483

1.25 -0.219 ±

0.0563

-0.158 ±

0.0582

-0.0617 ±

0.0627**

-0.184 ±

0.0534

-0.216 ±

0.045

80

2 -0.147 ±

0.0552

-0.134 ±

0.0588

-0.0969 ±

0.0557

-0.133 ±

0.0548

-0.198 ±

0.0464

pFA

0.75 0.187 ±

0.0282

0.281 ±

0.0315**

0.276 ±

0.0371*

0.249 ±

0.0311*

0.222 ±

0.0279

1.25 0.251 ±

0.0335

0.301 ±

0.0348

0.337 ±

0.0391**

0.279 ±

0.0317

0.242 ±

0.024

2 0.264 ±

0.0284

0.29 ±

0.0364

0.296 ±

0.0302

0.289 ±

0.0334

0.244 ±

0.0234

Correct Latency (s) 0.816 ±

0.0221

0.803 ±

0.0269

0.801 ±

0.0253

0.782 ±

0.023

0.796 ±

0.021

Incorrect Latency (s) 1.05 ±

0.0523

0.95 ±

0.0482

0.989 ±

0.0563

0.877 ±

0.0606

0.998 ±

0.0556

False Alarm Latency

(s)

0.763 ±

0.0265

0.689 ±

0.0322**

0.738 ±

0.0287

0.677 ±

0.0294**

0.719 ±

0.024

Magazine Latency

(Go) (s)

1.07 ±

0.0307

1.08 ±

0.0247 1.25 ± 0.115

1.06 ±

0.0321

1.27 ±

0.205

Magazine Latency

(No Go) (s)

10.8 ±

1.57 10.8 ± 1.55 8.81 ± 1.51 12.4 ± 1.8 9.6 ± 1.55

Trials

Completed

(Number)

0-20 67.3 ±

1.33 65.2 ± 2.08 53.1 ± 2.71

*** 67.6 ± 1.13

67.6 ±

1.14

21-40 67.8 ±

1.14 69.4 ± 1.07 68.3 ± 1.24 67.3 ± 1.63

66.5 ±

1.76

41-60 66.5 ±

1.39 68.9 ± 0.734 68.3 ± 1.13 68.4 ± 1.11 64.7 ± 2.1

4.7.2 High/Low Comparison

LSD comparisons adjusted for multiple comparisons showed a significant difference between

LA and HA animals for accuracy at 0.75 s SD (p<0.05) and for d′ at 0.75 (p<0.001), 1.25 and 2 s

SD (p<0.05; Figure 4.1).

81

V e h ic le A c c u ra c y

0.7

5

1.2

5

2.0

0

7 0

8 0

9 0

1 0 0

1 1 0

S tim u lu s D u ra tio n

Pe

rc

en

tag

e (

%)

A

*

V e h ic le d '

0.7

5

1.2

5

2.0

0

-1 .5

-1 .0

-0 .5

0 .0

0 .5

1 .0

1 .5


Sc

ore

B

L A

H A

* * * *

*

Figure 4.1: Comparison of accuracy and d′ after vehicle treatment in HA and LA groups when

grouped by these parameters. (A) Accuracy significantly differs between HA and LA animals only at the

most challenging SD of 0.75 s. (B) d′ significantly differed at all SDs, but most significantly at the most

challenging SD of 0.75 s. LSD comparisons were used and adjusted by the Benjamini-Hochberg method

for multiple comparisons. Significance levels are represented as * for p<0.05 and *** for p<0.001 after

adjustment.

A significant difference due to treatment and group interaction was also seen for omissions at 2

s SD (F4,72=2.64, p=0.0407; Figure 4.2B/D). However, LSD planned comparisons showed no

significant difference between any treatment and control, although a reduction in omissions,

when treated with nicotine 0.2 mg/kg, approached significance (p=0.053).

A c c u ra c y H A

0.7

5

1.2

5

2.0

0

7 0

8 0

9 0

1 0 0


Pe

rc

en

tag

e

A O m is s io n H A

0.7

5

1.2

5

2.0

0

0

1 0

2 0

3 0

4 0

5 0


Pe

rc

en

tag

e

B

V e h ic le

N ic o tin e 0 .1 m g /k g


C a ffe in e 2 .5 m g /k g

C a ffe in e 5 m g /k g

A c c u ra c y L A

0.7

5

1.2

5

2.0

0

7 0

8 0

9 0

1 0 0


Pe

rc

en

tag

e

C O m is s io n L A

0.7

5

1.2

5

2.0

0

0

2 0

4 0

6 0

8 0


Pe

rc

en

tag

e

D P re m a tu r e

HA

LA

0

5

1 0

1 5

2 0

Nu

mb

er

E

82

Figure 4.2: Performance measures of the go trials in 5C-CPT when treated with saline, nicotine (0.1

and 0.2 mg/kg) and caffeine (2.5 and 5 mg/kg). A significant group treatment interaction was seen for

omissions (B/D), but planned comparisons were not significant.

For pFA, a significant group*treatment interaction showed an increase at 0.75 s SD (F4,72=3.32,

p=0.0149; Figure 3B), planned comparisons showed this to be significant in HA animals when

treated with 0.2 mg/kg nicotine (p<0.01) and 2.5 mg/kg caffeine (p<0.01). Responsivity index

also showed a significant group treatment*interaction for the 2 s stimulus duration (F4,72=2.79,

p=0.0328; Figure 3D)) and planned comparisons showed a significant reduction when HA

animals were treated with 5 mg/kg caffeine (p<0.05).

83

Figure 4.3: Signal Detection Theory related measures of the 5C-CPT. (B) A significant increase in pFA was seen in HA animals following treatment with nicotine 0.2 mg/kg and caffeine

2.5 mg/kg. (D) 5 mg/kg of caffeine significantly reduced responsivity index in HA animals representing a more conservative response strategy. Significance levels are denoted as ** for p<0.01

and * for p<0.05.

p H R H A

0.7

5

1.2

5

2.0

0

0 .0

0 .2

0 .4

0 .6

0 .8


Pro

ba

bil

ity

A p F A H A

0.7

5

1.2

5

2.0

0

0 .0

0 .1

0 .2

0 .3

0 .4


Pro

ba

bil

ity

B

* *

* *

d ' H A

0.7

5

1.2

5

2.0

0

-0 .5

0 .0

0 .5

1 .0

1 .5


Sc

ore

C R I H A

0.7

5

1.2

5

2.0

0

-0 .6

-0 .4

-0 .2

0 .0

0 .2

V e h ic le





Sc

ore


D

*

p H R L A

0.7

5

1.2

5

2.0

0

0 .0

0 .2

0 .4

0 .6


Pro

ba

bil

ity

E p F A L A

0.7

5

1.2

5

2.0

0

0 .0

0 .1

0 .2

0 .3


Pro

ba

bil

ity

Fd ' L A

0.7

5

1.2

5

2.0

0

-1 .0

-0 .5

0 .0

0 .5


Sc

ore

G R I L A

0.7

5

1.2

5

2.0

0

-0 .8

-0 .6

-0 .4

-0 .2

0 .0


Sc

ore

H

84

A significant group*treatment interaction was seen for magazine latency in go trials (F4,72=2.52,

p=0.0485; Figure 4D) LSD planned comparisons showed a significant increase due to 0.2

mg/kg of nicotine in LA animals (p<0.001).


HA

LA

0 .6

0 .7

0 .8

0 .9

1 .0

Tim

e (

s)

A In c o r re c t L a te n c y

HA

LA

0 .0

0 .5

1 .0

1 .5

Tim

e (

s)

B F a ls e A la rm L a te n c y

HA

LA

0 .0

0 .2

0 .4

0 .6

0 .8

1 .0

Tim

e (

s)

C

M a g a z in e L a te n c y (G o )

HA

LA

0 .0

0 .5

1 .0

1 .5

2 .0

Tim

e (

s)

D

* * *

M a g a z in e L a te n c y (N o G o )

HA

LA

0

5

1 0

1 5

2 0




Tim

e (

s)


E

V e h ic le

Figure 4.4: Latency measures of the 5C-CPT following treatment with vehicle, nicotine (0.1 and 0.2

mg/kg) or caffeine (2.5 and 5 mg/kg). (D) A significant increase in magazine latency in go trials was seen

following 0.2 mg/kg of nicotine.

T r ia ls C o m p le te d H A

1-2

0

21-4

0

41-6

0

0

2 0

4 0

6 0

8 0

T im e (m in s )

Nu

mb

er

A T r ia ls C o m p le te d L A

1-2

0

21-4

0

41-6

0

0

2 0

4 0

6 0

8 0

T im e (m in s )

Nu

mb

er

B

V e h ic le

N ic o tn in e 0 .1 m g /k g

N ic o tn in e 0 .2 m g /k g



Figure 4.5: Number of trials completed per 20 minutes of the 5C-CPT. There was no significant

difference in the number of trials completed that differed between the HA and LA animals. A significant

treatment level effect can be seen in both (A) and (B) or 0.2 mg/kg nicotine to reduce the number of trials

completed (also see Table 1).

85

4.8 Discussion

This study has shown that acute doses of nicotine can cause a reduction in omissions showing

improved sustained attention and an increase in premature responses and pFA showing an

increase in impulsive action due to the treatment. A significant change was also seen in

responsivity index, which is used to assess response strategy, where the increase in this value

represents a more liberal response strategy after treatment (Young et al., 2009, Marston, 1996).

This evidence suggests a phenotype of high impulsivity, which is further supported by the

observed reduction in latency to make a false alarm response.

The grouped analysis showed that the HA animals were more susceptible to the increase in

pFA than the LA animals. While both groups had a similar pFA at 0.75 s SD, they may have

used different strategies to achieve this. The strategy can be deduced from the ratio of correct

rejections (1-pFA) in no-go trials to omissions in go trials as both represent a lack of response.

The HA group had much lower omissions than correct rejections. However, LA animals had

higher omissions showing they were not responding to go or no-go trials. Therefore in the LA

animals, the pFA may represent an inability to sustain attention and therefore respond to go or

no-go trials.

Bizarro et al. (2004) showed that the same doses of nicotine reduce omissions and increase

premature responses in the 5C-SRTT in male rats. In the 5C-CPT we found similar effects in

female rats. However, we did not see the same increase in accuracy at 0.2 mg/kg or a

significant reduction of correct latency at 0.1 mg/kg. However there are two factors which may

explain the difference, firstly, a higher baseline accuracy in this study compared to Bizarro et al.

(2004) would make increase less likely, but reduction more likely due to a ceiling effect.

Secondly, this may represent a difference in response between male and female rats. Female

rats are more susceptible to the rewarding effects of nicotine and thus compulsively administer

nicotine faster and at lower doses than males (Donny et al., 2000). A study by Taylor and

Maloney (2010) showed a synergistic effect of estradiol and nicotine in overectomised female

rats to improve performance in a maze based version of the 5C-SRTT. The effect of estradiol

suggests that nicotine needs to be studied in both males and females to compare gender-

related differences directly. Nicotine has been tested in the 5C-CPT by Young et al. (2013)

using four-day sub-chronic treatment with doses of 0.001-0.1 mg/kg in male mice. They found

significant improvement d′ at the 0.01 mg/kg dose using 0.75, 1.25 and 2 s SDs, which

significantly reduced omissions. They also analysed the performance on the first day of dosing

to ascertain acute effects of nicotine and found none. However, their acute data was not

presented in full to enable a detailed comparison, but in the data set non-significant differences

were observed for the 0.1 mg/kg dose in this study, and it is possible that their lower n number

of 15 did not have the statistical power to detect differences in the more variable acute data set,

which we can observe with 35 animals. Also, species differences cannot be ruled out

particularly in respect to the strategy of response, shown by a recent study which found rats to

86

be more dependent on a temporal strategy than mice (Cope et al., 2016). It has previously been

shown that acute doses of nicotine can promote task disruptive effects, whereas these are not

seen if subjects have previously received chronic or subchronic doses of nicotine (Hahn and

Stolerman, 2002). Differences between chronic and acute mechanisms of action have been

suggested to be via a number of different mechanisms. Firstly acute administration causes an

increase in plasma adrenocorticotropic hormone and cortisone; however, repeated injections do

not cause this response (Matta et al., 2007). Secondly, chronic dosing of nicotine facilitates

long-term potentiation and active up and down regulation of receptors (Fujii et al., 1999,

Mansvelder and McGehee, 2000). Future assessments could investigate this further by trying

subchronic or chronic doses to overcome disruptive effects or by analysing the effect of more

selective compounds to assess the role of nicotinic receptors with fewer off-target effects.

Caffeine, in the whole cohort, increased impulsive action (pFA) when under high attentive load

(i.e. low SD) and also reduced the false alarm latency. The increase in pFA was only observed

at the lowest dose (2.5 mg/kg) and at the most challenging SD (0.75 s) showing that under high

attentive load caffeine can promote impulsive behaviour. In the grouped analysis impulsive

action was also only significantly increased in HA animals, which could be explained by

differences in baseline omissions between LA and HA animals (as discussed above for

nicotine). Caffeine has been shown to improve 5C-SRTT accuracy in aged rats, which show a

reduction of accuracy (Grottick and Higgins, 2002). In healthy adult rats, no improvement in

accuracy has been observed, only a reduction in omissions at the doses tested here (Bizarro et

al., 2004). An increase in premature responses was also seen by Bizarro et al. (2004) at 5

mg/kg, but only when a fixed ITI was used, no difference was observed when a variable ITI was

used. Here we used a variable ITI and in agreement with their study found no significant effect

on premature responses. This measure is often used as an indicator of impulsive action, more

specifically waiting impulsivity. pFA is also a measure of impulsive action (response

disinhibition) it is, therefore, possible that the increase seen in pFA is related to the increase in

premature responses seen by Bizarro et al. (2004) with a fixed ITI. The increase in pFA would

support the hypothesis that, in healthy animals, caffeine can increase impulsive action.

However, premature responses can also represent timing strategies which would explain why

the effect is not seen with a variable ITI both in the Bizarro et al. (2004)‟s study and the work

presented here (Cope et al., 2016, Bizarro et al., 2004). Therefore the increase in pFA caused

by caffeine is independent of changes in premature responses showing an increase in response

disinhibition, but not waiting impulsivity. The mechanism by which caffeine can cause changes

in impulsivity is not known. However, the colocalisation of adenosine A2A receptors and

dopamine D2 receptors in the striatum offers one potential mechanism (Fink et al., 1992). The

selective removal of A2A receptors from the nucleus accumbens core prevents caffeine-induced

arousal (Lazarus et al., 2011). However, further research is needed to understand its

relationship with impulsivity.

87

In conclusion, acute doses of both nicotine and caffeine increased impulsive behaviour as

measured in the 5C-CPT in animals which have not been grouped based on behaviour. In

animals which have been grouped by behaviour nicotine increased impulsivity in HA animals.

Nicotine also reduced omissions, thus increasing sustained attention as has been previously

reported. These findings support previous work, but also highlight the power of the 5C-CPT to

assess impulsive action in a robust way and overcome issues of timing strategy. Future work

should compare acute to chronic or subchronic dosing for both compounds to see how this

differs their effect. Additionally, using the same method with more selective nicotinic

acetylcholine agonists would be important to establish the role of different receptor subtypes in

nicotine's action.

88

5 Partial agonism at the α7 nicotinic

acetylcholine receptor improves attention,

impulsive action and vigilance in low

attentive rats

Authors: Andrew Hayward, Lisa Adamson, Joanna C Neill

[Published in the Journal of European Neuropsychopharmacology:

HAYWARD, A., ADAMSON, L. & NEILL, J. C. 2017. Partial agonism at the alpha7 nicotinic

acetylcholine receptor improves attention, impulsive action and vigilance in low attentive rats.

Eur Neuropsychopharmacol, 27, 325-335.]

5.1 Contributions


by Andrew Hayward and Lisa Adamson. The study was conducted by Andrew Hayward. The

manuscript was prepared by Andrew Hayward and Joanna C. Neill and approved by Lisa

Adamson.

89

5.2 Context

With nicotine showing a limited effect in the task and no specific effect in low attentive animals

one theory is that non-specific effects of nicotine promote a stress response. Testing more

specific compounds is one way around this and knock-outs of the α7 nicotinic acetylcholine

receptor result in an inattentive phenotype making it a receptor of interest (Young et al., 2007).

5.3 Abstract

Inattention is a disabling symptom in conditions such as schizophrenia and attention

deficit/hyperactivity disorder. Nicotine can improve attention and vigilance but is unsuitable for

clinical use due to abuse liability. Genetic knockout of the α7 nicotinic acetylcholine receptor

(nAChR) induces attention deficits, therefore, selective agonism may improve attention, without

the abuse liability associated with nicotine. The α7 nAChR partial agonist encenicline (formerly

EVP-6124) enhances memory in rodents and humans. Here we investigate, for the first time,

the efficacy of encenicline to improve attention and vigilance in animals behaviourally grouped

for low attentive traits in the 5 choice-continuous performance task (5C-CPT). Female Lister

Hooded rats were trained to perform the 5C-CPT with a variable stimulus duration (SD).

Animals were then grouped based on performance into upper and lower quartiles of d′

(vigilance) and accuracy (selective attention), producing high-attentive (HA) and low-attentive

(LA) groups. LA animals showed an increase in selective attention and vigilance at 0.3 mg/kg

encenicline, a reduction in impulsive action (probability of false alarms) and an increase in

vigilance following 1 mg/kg at 0.75 s SD. At 1 mg/kg, HA animals had reduced selective

attention at 0.75 s SD and reduced vigilance at 0.75 and 1.25 s SD. Improvement of attention,

vigilance and impulsive action in LA animals demonstrates that encenicline has pro-attentive

properties dependent on baseline levels of performance. Our work suggests that α7 nAChR

partial agonism may improve attention particularly in conditions with low attention.

90

5.4 Introduction

Attention can be broadly defined as the ability to filter out irrelevant stimuli in order to optimise

limited neurochemical resources (Luck and Gold, 2008). Inattention is an early symptom in

highly disabling conditions such as schizophrenia, bipolar disorder, Alzheimer‟s disease and

attention deficit/ hyperactivity disorder (ADHD) (Luck and Gold, 2008, Foldi et al., 2002).

Nicotine acts at nicotinic acetylcholine receptors (nACh) to produce pro-cognitive effects, in

particular, enhanced selective attention. This effect has been demonstrated in non-smoking

adults using the continuous performance task (CPT) (Rezvani and Levin, 2001), in rodents

using the 5 choice continuous performance task (5C-CPT) (Young et al., 2013) and in the 5

choice serial reaction time task (5C-SRTT) (Grottick and Higgins, 2000). In healthy non-

smokers, nicotine improves attention most in low performing individuals (Wignall and de Wit,

2011, Poltavski and Petros, 2006). In animals, this has been demonstrated using the 5C-SRTT

where nicotine produces a greater improvement in low-performing animals (Grottick and

Higgins, 2000).

Smoking in schizophrenia patients may be a form of self-medication (Segarra et al., 2011,

Parikh et al., 2016, Young and Geyer, 2013). This idea is supported by the higher prevalence of

smoking in schizophrenia patients (86-90 %) compared to healthy controls (15-25 %) and

improvement of cognitive domains, including vigilance and attention, in schizophrenia patients

and healthy subjects following nicotine treatment in the CPT (Rezvani and Levin, 2001,

Heishman et al., 2010, Segarra et al., 2011). However, nicotine has a high abuse liability and

many unpleasant side effects including dizziness, headache, agitation, anxiety and loss of

appetite (White and Levin, 1999, Watkins et al., 2000). This makes it unsuitable as a treatment,

particularly for schizophrenia and ADHD patients who often also exhibit high levels of

impulsivity, which further increases the risk of drug addiction.

The α4β2 and α7 receptors are the most abundant nicotinic acetylcholine receptor (nAChR)

subtypes in the CNS. The α7 nAChR is a pentameric ligand-gated ion channel located both pre

and post synaptically throughout the cortex enabling fast synaptic neurotransmission (Vizi and

Lendvai, 1999). Mice with a deletion of the α7 nAChR show cognitive deficits including reduced

sustained attention, supporting a role for α7 nAChR mechanisms in attention (Young et al.,

2007, Hoyle et al., 2006). It has also been shown that attention deficits in α7 receptor knockout

mice can be reversed by β2 containing receptor agonists (Kolisnyk et al., 2015). This suggests

that activation of β2 nAChRs can bypass attentional deficits due to α7nAChR deficiency.

However, the β2-containing heterodimeric (largely α4β2) nAChRs are predominantly implicated

in nicotine dependence via activation of the mesolimbic dopamine system (Picciotto et al., 1998,

Bloem et al., 2014, Brioni et al., 1996, Pons et al., 2008). Therefore α7 receptor activation

represents a more favourable target.

91

We have shown previously that in sub-chronic phencyclidine (PCP) treated rats (a well-

validated model for schizophrenia, McLean et al., 2011) and in healthy control animals (McLean

et al., 2016, McLean et al., 2011) the α7 full agonist PNU-282987 improves performance in

novel object recognition, a visual recognition memory task. In contrast, the same compound has

been shown not to improve attention or vigilance in the 5C-CPT in both normal mice and those

with a scopolamine-induced deficit (Young et al., 2013). This lack of efficacy is in agreement

with other studies using α7 full agonists in tasks of attention; the data suggest therefore that

visual recognition memory, not attention is improved by full agonists of the α7 receptor (Young

and Geyer, 2013).

However, the α7 partial agonist ABT-126 produced a significant increase in attention in a phase

II clinical study when assessed using the MATRICS Consensus Cognitive Battery (Haig et al.,

2016), and this is supported by the effects of tropisetron and R-3487, which are also α7 receptor

partial agonists. Both compounds are also antagonists at the 5-HT3 receptor, but antagonism at

the 5-HT3 receptor does not improve attention (Muir et al., 1995). Tropisetron improved

sustained attention in schizophrenia patients assessed with the Cambridge Neuropsychological

Test Automated Battery (CANTAB) (Shiina et al., 2010) and R3487 improved sustained

attention in a visual signal detection task in rodents; these results await clinical confirmation

(Rezvani et al., 2009). Partial agonists at the α7 receptor have not been studied in animals

using a specific task of attention such as the 5C-CPT, nor have their effects been investigated

in low attentive animals.

Encenicline is a potent and selective α7 nAChR partial agonist and 5-HT3A receptor antagonist

(Prickaerts et al., 2012). However, at low nanomolar concentrations in the brain, it is thought

that 5-HT3 receptor binding is minimal, but could reduce side-effects such as nausea via

peripheral action (Prickaerts et al., 2012). In rats, a dose of 0.3 mg/kg of encenicline improves

visual recognition memory in the novel object recognition task with a 24-hour inter-trial interval

in normal rats and reverses a scopolamine-induced deficit in the same task (Prickaerts et al.,

2012).

The 5C-CPT is a test of sustained/selective attention, impulsive action and vigilance, developed

from the clinically used CPT. It is also based on the widely-used rodent 5C-SRTT. However, it

provides enhanced validity and translation to the clinic compared with the 5C-SRTT by including

trials where no response is required. Comparing the two, the 5C-CPT is the more translational,

whereas the 5C-SRTT offers higher throughput (Lustig et al., 2013). In both of these tasks,

several studies have separated animals into groups based on variation in their task

performance, which are stable across multiple test sessions (Blondeau and Dellu-Hagedorn,

2007, Dalley et al., 2007, Grottick and Higgins, 2000, Tomlinson et al., 2014); reviewed in

Hayward et al. (2016)). We have demonstrated that animals respond differentially to

pharmacological treatments, in particular, methylphenidate and atomoxetine, drugs currently

used to treat ADHD in patients, based on this natural variation of performance when the test

92

parameters of the 5C-CPT are manipulated (Tomlinson et al., 2014, Tomlinson et al., 2015). It is

our view that this paradigm offers an unbiased method to study attentional deficits of relevance

to several disorders without assuming a mechanism, as is the case with genetic or chemical

manipulations (Hayward et al., 2016).

Effects of a partial agonist at the α7 nAChR on attention and vigilance have yet to be studied

using the 5C-CPT and, as nicotine has a greater effect to improve attention in low-performing

groups, our aim was to compare the effects of encenicline in high and low attentive rats. We

hypothesised that encenicline would improve attention in the 5C-CPT, particularly in low-

attentive animals.

5.5 Experimental Procedures

5.5.1 Animals

37 female Lister Hooded rats (Charles River, UK; weighing 210 ± 20 g at the start of training)

were housed in groups of four or five in individually ventilated cages with two levels (GR1800

Double-Decker Cage, Techniplast, UK) under a standard 12 hour light: dark cycle (lights on

7:00 am). We used female rats as the 5C-CPT has been carefully validated in female rats in our


2012b). In addition, male rats grow rapidly, which precludes social housing over the lengthy

period of time it takes to train and test rats in the 5C-CPT. Individual housing is stressful for this

social species and best avoided (Holson et al., 1991, Brown and Grunberg, 1995). The

environment was maintained at 21 ± 2 oC, 55 ± 5 % humidity. The diet was standard rat chow

(Special Diet Services, UK) controlled to maintain 90% free feeding weight throughout training

and testing (typically 10 g/rat/day). Water was available ad libitum for the duration of the study,

except in the operant chambers. All experiments were conducted between 09:00-17:00 in the

light phase. All experiments were conducted in accordance with the UK Animals (Scientific

Procedures) 1986 Act and local University ethical guidelines.

5.5.2 Apparatus

Tests were conducted in eight standard nine-hole chambers (Campden Instruments Ltd, UK)

with holes 2, 4, 6, and 8 occluded. Chambers and data collection were controlled by ABet II

Touch software (Lafayette Instrument Company, IL, USA). Animals‟ behaviour was reinforced

with 45 mg rodent reward pellets (Test Diet, MO, USA).


Animals were trained as described previously (Barnes et al. 2012a; 2012b; Tomlinson et al.

2014). In brief, animals respond to one of five holes when they detect a light stimulus. A nose

poke either while the stimulus is on (stimulus duration (SD)) or within a set period from it first

93

coming on (limited hold) causes release of a food reward. Rare no-go trials are also

interspersed (30% of trials), in these all 5 lights illuminate and no response is required to get a

food reward. Incorrect responses, missed go trials and responded to no-go trials are punished

with a 5 s time out with house light on. When food is dispensed or at the end of a time out the

food tray lights up and entry into the tray starts a timer before the next trial (inter-trial interval

(ITI)). Response during this period is a premature response and also results in a time out.

Animals were trained for approximately 24 weeks to set criteria on final training parameters of 2

s SD, 2 s limited hold, 5 s ITI. This was conducted in a series of stages with successively longer

SD and limited hold where each stage was reached by meeting criteria of >70% accuracy,

<25% omissions and >65% correct rejections (see Barnes et al. (2012a), Barnes et al. (2012b).

Once rats reached these final criteria, they were trained once per week in order to maintain

performance and avoid over-training.


Testing parameters were as per final training criteria, with a variable 0.75, 1.25 and 2 s SD and

increased 7-12 s variable ITI. As a previous study found separable genetic effects of α7

receptor mutations at different SDs, a variable SD was used here (Young et al., 2013, Young et

al., 2007). To gather sufficient data for three SDs, the session was extended to 1 hour or 250

trials, whichever came first.

Encenicline ((R)-7-chloro-N-(quinuclidin-3-yl)benzo[b]thiophene-2-carboxamide; kindly donated

by Autifony Therapeutics, Verona, Italy), was prepared at concentrations of 0.1, 0.3 and 1

mg/kg in distilled water and administered by oral gavage 30 min before testing in a volume of 2

ml/kg. Each test day was separated by a seven day washout period (no drug administration, or

training in 5C-CPT). Treatments were randomly assigned based on a within-subjects Latin

square design. Doses were selected from published studies showing efficacy in novel object

recognition in rats (Prickaerts et al., 2012, van Goethem et al., 2015).



with vehicle (0.9% saline). The best performing 10 animals for d′ and accuracy formed the high-

attentive (HA) group and the worst performing 10 animals formed the low-attentive (LA) group.


significantly different groups and the number of animals used (see Hayward et al. (2016)). To

achieve this, animals were ordered by d′ and the highest/ lowest 10 animals were chosen,

providing their accuracy was also above/below average. As vigilance is a measure of overall

performance across go and no-go trials, accuracy was also used to separate the group into an

attention phenotype. Two animals were excluded for making >80% omissions.


94

The method for calculation of all parameters is described in detail in Tomlinson et al. (2014) and

Young et al. (2013). The signal detection theory measure d′ was used in this case only after

confirming that probability hit rate (pHR) and the probability of false alarms (pFA) were normally

distributed, as described in Young et al. (2013). Here, d′ is calculated in the same way as

described in Young et al. (2013) as the z-score of pHR minus the z-score of pFA.

( ) ( )

( )

Using d′ is a further translational step towards making the rodent 5C-CPT closer to the human

CPT, compared to using sensitivity index (Young et al., 2013). Measures were calculated

individually for each rat at each SD for accuracy, omissions, pHR, pFA, d′ and responsivity

index (RI) where z scores were based on whole cohort data. After relevant measures were

calculated, data were analysed using InVivo Stat (Version 3.4), as the most appropriate

statistical package for in vivo behavioural experiments (Clark et al., 2012). Overall performance

(Table 2) was compared using a one-way Analysis of Variance (ANOVA) comparing treatment

as a factor and using animal ID as a blocking factor to account for the within-subjects design.

Between-group comparisons used least square difference (LSD) tests adjusted for multiple

comparisons by a Benjamini-Hochberg‟s procedure. Measures compared by this method

include accuracy, pFA and d′ for three SDs. Grouped analysis used a two-way repeated

measures mixed model ANOVA with GROUP as the treatment factor and TREATMENT as the

repeated factor for each measurement followed by LSD planned comparisons for each

treatment compared to vehicle. Normality and Sphericity of the data were judged using the

normality plot and the residual vs. predicted plots. Arcsine, square root or Log10 transformations

were used when appropriate. Data are presented as F values and p values for

TREATMENT*GROUP interactions and effects are considered statistically significant if the p-

value from Fisher protected least square difference planned comparisons were < 0.05.

5.6 Results


95

It is important to establish overall group performance before separating the animals into the

groups, particularly when within group reference points are used (Hayward et al., 2016), using

parameters such as upper/lower quartiles (Table 5.1). Comparison for all (n=37) animals

showed no significant difference between treatment levels following a one-way ANOVA with

animal ID as a blocking factor.

Table 5.1: When considered as a single cohort no treatment effect of encenicline was observed.

Measure Variant Vehicle 0.1 mg/kg 0.3 mg/kg 1.0 mg/kg

Accuracy (%) 0.75 s 93±1.5 90±1.6 93±1.5 90±1.5

1.25 s 95±1.2 97±0.62 97±0.57 96±0.95

2 s 96±0.90 97±0.91 98±0.53 97±0.94

Omissions (%) 0.75 s 44±2.8 47±2.6 43±2.6 48±2.6

1.25 s 38±2.9 40±3.0 37±2.8 41±2.8

2 s 36±3.1 38±2.9 33±3.2 36±2.5

pFA 0.75 s 0.23±0.028 0.20 ±0.024 0.21±0.027 0.19±0.024

1.25 s 0.24±0.026 0.22±0.019 0.26±0.027 0.22±0.027

2 s 0.23±0.020 0.21±0.02 0.23±0.023 0.24±0.023

d′ 0.75 s 0.029±0.22 -0.11±0.23 0.059±0.23 -0.053±0.21

1.25 s 0.065±0.22 -0.092±0.18 -

0.033±0.21

-0.077±0.25

2 s -0.086±0.22 0.14±0.23 0.094±0.20 -0.32±0.20

RI 0.75 s 0.32±0.055 0.21±0.056 0.26±0.052 0.18±0.053

1.25 s 0.35±0.048 0.26±0.050 0.38±0.045 0.29±0.043

2 s 0.36±0.046 0.33±0.047 0.39±0.044 0.33±0.048

Trials (Number) 0-20 mins 69±0.98 68±1.1 70±0.98 66±2.0

21-40 mins 68±1.1 69±1.5 69±1.1 67±1.1

96

41-60 mins 56±2.0 54±2.0 56±2.2 56±2.0

Premature

(Number)

- 11±1.4 7.4±1.1 11±2.1 11±1.7

Correct Latency

(s)

- 0.82±0.022 0.85±0.023 0.82±0.023 0.83±0.022

Incorrect Latency

(s)

- 0.95±0.069 1.2±0.054 1.0±0.054 1.0±0.065

Magazine

Latency (Go) (s)

- 1.1±0.024 1.1±0.038 1.0±0.029 1.0±0.028

False Alarm

Latency (s)

- 0.74 ±0.031 0.75±0.031 0.73±0.032 0.71±0.030

Magazine

Latency (No Go)

(s)

- 10±1.5 9.8±1.4 11±1.5 13.±1.8


Comparison of the main measures (Accuracy, pFA and d′; Figure 5.1) using adjusted LSD tests

found that HA and LA groups differed significantly in accuracy and d′ at 0.75 (both p<0.01) and

1.25 s (both p<0.05) SD. The data show most robust separation at the most challenging

(shortest) SD (0.75 s).

97

V e h ic le A c c u ra c y

0.7

5

1.2

5

2.0

0

7 0

8 0

9 0

1 0 0

1 1 0


Pe

rc

en

tag

e (

%)

** *

V e h ic le d '

0.7

5

1.2

5

2.0

0

-1 .5

-1 .0

-0 .5

0 .0

0 .5

1 .0


Sc

ore

** *

L A

H A

A B

Figure 5.1: Group differences between high and low attentive groups (HA/LA) at increasing SDs.

During saline treatment, the difference between HA/LA is most significant at the most challenging (i.e.,

shortest) stimulus duration (0.75 s), with LA showing significantly lower attention (Accuracy; A) and d′

(vigilance; B). Data are shown as mean ± SEM. n=10 HA and n=10 LA animals. *, **, denote significance

levels p< 0.05, 0.01 of Benjamini-Hochberg‟s procedure adjusted LSD planned comparisons comparing

HA with LA groups at each SD.

A c c u ra c y H A

0.7

5

1.2

5

2.0

0

7 0

8 0

9 0

1 0 0


Pe

rc

en

tag

e *

O m is s io n H A

0.7

5

1.2

5

2.0

0

0

2 0

4 0

6 0


Pe

rc

en

tag

e

**

d ' H A

0.7

5

1.2

5

2.0

0

-1 .5

-1 .0

-0 .5

0 .0

0 .5

1 .0

1 .5

V e h ic le

E V P 0 .1 m g /k g

E V P 0 .3 m g /k g

E V P 1 m g /k g


Sc

ore

** *

A c c u ra c y L A

0.7

5

1.2

5

2.0

0

7 0

8 0

9 0

1 0 0


Pe

rc

en

tag

e

*

O m is s io n L A

0.7

5

1.2

5

2.0

0

0

2 0

4 0

6 0


Pe

rc

en

tag

e

*

d ' L A

0.7

5

1.2

5

2.0

0

-1 .5

-1 .0

-0 .5

0 .0

0 .5

1 .0

1 .5


Sc

ore

**

A B C

D E F

Figure 5.2: Encenicline improved selective attention and vigilance in low attentive (LA) animals

only. In HA animals, 1 mg/kg encenicline significantly reduced performance in accuracy (A), omissions (B)

and d′ (D) at 0.75 s SD, d′ was also reduced by the same dose at 1.25 s SD. In LA animals, accuracy

(selective attention; D) was significantly increased at 0.3 mg/kg for 0.75, d′ (F) is significantly increased at

0.3 and 1 mg/kg of encenicline at 0.75 s SD and omissions (E) are increased at 0.1 mg/kg at 0.75 s SD.

Data are shown as mean ± SEM of n=10 HA and n=10 LA animals each tested at all doses. * and **

denote significance levels p< 0.05, 0.01 respectively for LSD planned comparisons of vehicle treated

98

animals to the three treatment levels following significance in GROUP*TREATMENT interaction in a

repeated measures two way ANOVA.

In HA animals encenicline impaired performance in a dose-dependent manner as demonstrated

by significantly reduced accuracy at 0.75 SD (Figure 5.2A; F3,54=3.91,p=0.013) an effect that

was significant at 1 mg/kg (p<0.05) of encenicline compared with the vehicle-treated group,

Figure 2. A significant increase in omissions was also observed at 0.75 s SD (Figure 5.2B; F3,

54=2.99, p=0.039) again significant at 1 mg/kg of encenicline (p<0.01). These two factors led to

a reduction of the signal detection theory parameter d′ (d prime) at all SDs: 0.75 (Figure 5.2C;

F3, 54=9.34, p<0.0001), 1.25 s (F3, 54=3.24, p=0.029) 2 s (F3,54=3.11,p=0.034), planned

comparisons showed significantly reduced d‟ for 0.75 and 1.25 s SDs at the 1 mg/kg dose

(p<0.01 and p<0.05 respectively). As d′ is a robust measure of vigilance, this suggests that the

HA animals became less vigilant as well as less attentive when treated acutely with 1 mg/kg of

encenicline. These effects, combined with increased omissions and a lack of significant

changes in any of the latency measures and total trials completed suggests that partial agonism

of 7 nicotinic receptors induces a reduction in the ability to sustain attention throughout the

task in HA animals.

In contrast, encenicline enhanced performance in LA animals as shown by a significant

increase in accuracy at 0.75 s SD (Figure 5.2D; F3,54=3.1, p=0.034), an effect that was

significant at 0.3 mg/kg of encenicline (p<0.05). This change corresponds to an increase in

selective attention (Robbins, 2002). LA animals also showed increased omissions at 0.75 s SD

(Figure 5.2E; F3, 54=2.99, p=0.039). This effect was significant following treatment with the

lowest dose of 0.1 mg/kg of encenicline (p<0.05), this dose did not produce any other significant

effects on attentive measures in either HA or LA animals. Similar to accuracy, d′ showed the

opposite effect in LA compared with HA animals and was increased (i.e., vigilance was

enhanced) at 0.75 s SD (Figure 5.2F; F3, 54=9.34, p<0.0001) with a significant effect produced

at 0.3 and 1 mg/kg of encenicline (p<0.05).

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Figure 5.3: Encenecline reduces impulsivity in LA animals at 0.75 s SD. In HA animals pFA (A)

trended to significance for 0.75 s SD and 0.1 mg/kg (p=0.081). The probability of false alarms (pFA; B)

significantly reduced in low attentive animals (LA) at 1 mg/kg encenicline at 0.75 SD. No significant effects

were seen on premature responses. Data are shown as mean ± SEM of n=10 HA and n=10 LA animals

each tested at all doses. *, denotes significance levels p< 0.05 for LSD planned comparisons of vehicle

treated animals to the three treatment levels following significance in GROUP*TREATMENT interaction in

a repeated measures two-way ANOVA.

The impulsivity measure, pFA, was significantly reduced in LA animals at 0.75 s SD (Figure

5.3B; F3, 54=3.1, p=0.034) following treatment with 1 mg/kg encenicline (p<0.05) compared to

the saline control group. As false alarms represent an impulsive behaviour, this shows reduced

response disinhibition, i.e., enhanced inhibitory control (for impulsive action).

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C D E

F G

Figure 5.4: Latency measures of the 5C-CPT showed no significant TREATMENT*GROUP

interaction. Data are shown as mean ± SEM of n=10 HA and n=10 LA animals each tested at all doses.

No significant effect was seen on the number of trials completed or on any of the latency

measures for GROUP*TREATMENT interaction (Figure 5.4). This is most likely due to high

within-subject variability in these measures as when comparisons between subjects were made

(LSD) they were significant for „no go‟ magazine latency and incorrect latency. However, the

design of this study uses Fisher protection to reduce false positives, so these are not

considered valid differences.

5.7 Discussion

To study the effect of encenicline in high and low attentive animals we firstly established that,

after grouping, the animals were significantly different in the measures for attention and

vigilance. Treatment with encenicline showed opposite effects between HA and LA groups for

accuracy, the correlate of selective attention, where LA animals had enhanced selective

attention and HA animals had reduced selective attention. A similar effect was observed for

vigilance via the measure d′. For both of these measures 0.3 mg/kg was the optimal dose, i.e.,

was most effective at the most challenging SD of 0.75 s. An effect was also observed on pFA,

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but not on premature responses (both measures of impulsive action), indeed these have

previously been shown to be separable constructs pharmacologically and genetically (Young et

al., 2011). Therefore, our present study shows that LA animals are more able to withhold from

responding to a prepotent stimulus but are not significantly different in their ability to wait for the

stimulus when treated with encenicline. When assessing false alarms, it is important to evaluate

whether reduced responses in „no go‟ trials are also seen in „go‟ trials, reflecting reduced

motivation rather than enhanced response inhibition. Changes in omissions and pFA occurred

at different concentrations of the drug suggesting that these effects are independent of one

another. The changes we observed in omissions were apparent only for the challenging 0.75 s

SD and were observed at 1 mg/kg in HA animals and 0.1 mg/kg in LA animals. Changes in

omissions can reflect sustained attention, motivation, sedative or motor effects; in order to

ascertain which of these are affected other measures are required (Robbins, 2002). Sedative

and motor effects should be detected as changes across the latencies; motivational changes

would alter the magazine latencies. Incorrect latency and no go magazine latency were

significantly different in planned comparisons, but not via the GROUP*TREATMENT interaction

(Figure 5.4). This is most likely due to high within-subject variability, which is only accounted for

in the repeated measures ANOVA. This high variability would require larger group sizes in order

to determine whether an effect is robust. However, sedative or motor effects would also be

expected to alter correct latency and motivational effects would be expected to alter „go‟

magazine latency. As neither of these was observed, the changes in motivation may represent

lapses in the ability to sustain attention; however, this requires further testing. The difference in

dose required to elicit the effect in HA and LA animals may represent differences in

neurobiology between the groups, in agreement with opposing effects seen on other measures.

In summary, encenicline improves selective attention and vigilance in LA animals at 0.3 mg/kg

and reduces impulsivity at 1 mg/kg.

As we had extended the number of trials to 250 rather than the standard 120 trials, satiety/

reduced motivation are important factors for consideration. Looking at Table 1 and Figure 4 A

and B the number of trials is lower at the final time point. However this was not different

between treatment groups suggesting that it did not contribute significantly to the drug effect.

This conclusion is further supported by our observation of no significant effect between

treatments on magazine latency, a measure of satiety. Using a variable SD enabled us to

sample a span of difficulty levels, however, in the 2 second SD, both groups showed >90%

accuracy suggesting that a ceiling effect may play a role at this SD preventing increases from

being detected.

The 5-HT3 receptor is a homologue of the α7 receptor and so α7 agonists often show

appreciable affinity and act as antagonists at this receptor subtype (Prickaerts et al., 2012).

However, the selective 5-HT3 receptor antagonist ondansetron does not improve attention in

rats in the 5C-SRTT, suggesting that attention promoting effects seen here are not due to an

interaction with the 5-HT3 receptor (Muir et al., 1995).

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Increased accuracy at 0.75 s stimulus duration in LA animals is interesting as it seems to follow

an inverted U-shaped dose response trend which has been reported for other nicotinic

compounds (Kolisnyk et al., 2015, Olincy and Freedman, 2012, Picciotto, 2003). A recent

microdialysis study by Huang et al. (2014) found that the effect of encenicline on

neurotransmitter efflux in the medial prefrontal cortex differs depending on dose. In that study,

0.1 mg/kg of encenicline increased dopamine, acetylcholine and glutamate release in the

prefrontal cortex, whereas only acetylcholine was increased at the higher dose of 0.3 mg/kg.

This may be due to receptor populations existing both pre- and post-synaptically, giving the α7

receptor a modulatory role which differs depending on the concentration of acetylcholine at the

synapse (Vizi and Lendvai, 1999). An inverted U-shaped trend (Yerkes-Dodson effect) is also a

property of the arousal of attention (Wood et al., 2014) which has been linked to prefrontal

monoamine levels (Arnsten, 2009). The theory of optimal arousal and neurotransmitter

differences may contribute to the outcomes of this study and would make it interesting to see a

combined microdialysis or fast scan voltammetry and attentive task study using encenicline,

particularly comparing high/low performing animals.

A previous study found no effect of the α7 full agonist, PNU-282987 in the 5C-CPT in either

untreated mice or in those with a scopolamine-induced attention/ vigilance deficit, in agreement

with our study when we considered the entire cohort together (Young et al., 2013). However,

once we separated animals into HA and LA groups, the α7 receptor partial agonist encenicline

clearly improved attention, vigilance and response inhibition in LA animals. There are two key

differences between the work of Young et al. (2013) and the present study; firstly, the model

used in our study is a behavioural separation, whereas Young et al. (2013) used a

pharmacologically-mediated deficit. Scopolamine has a number of non-specific effects, which

may also affect performance, such as increased locomotor activity, pupil dilation and mnemonic

changes (Klinkenberg and Blokland, 2010). The second difference is the mechanism of action

of PNU-282987, which is a full agonist, whereas encenicline is a partial agonist, allowing it to

act as an agonist or antagonist, depending on endogenous acetylcholine levels (Prickaerts et

al., 2012, Hajos et al., 2005). Furthermore, as outlined earlier, other α7 nAChR partial agonists

have been shown to improve attention (Rezvani et al., 2009, Haig et al., 2016, Shiina et al.,

2010), but have not been tested in specific tasks of attention/ vigilance and not in animals

separated according to performance.

In a small study of schizophrenia patients, encenicline added onto regular medication had no

effect on the attentive measures in the CogState schizophrenia battery compared to placebo

(Preskorn et al., 2014). The small sample size in this study where the placebo group composed

of 4 patients was a serious limitation and the authors recommend caution for these measures.

Indeed, a larger study, using the same doses found significant effects of encenicline across a

broad range of cognitive tests, including attention, using the CogState battery (Keefe et al.,

2015). In a small pilot study in schizophrenia patients both mismatch negativity (a negative

potential following an odd stimulus in a sequence) and P300 (positive inflection after a standard

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stimulus presentation) which can be correlated to the severity of cognitive deficits (Ford et al.,

1999, Baldeweg et al., 2004) were shown to be normalised by encenicline (Preskorn et al.,

2014). However, a recent phase III trial did not reach significance for primary cognitive

endpoints in schizophrenia patients compared to placebo (Pharmaceuticals, 2016). Also, in the

Alzheimer‟s disease Phase III trial, rare, but serious, gastrointestinal side effects have put

testing on hold (Pharmaceuticals, 2015). These findings suggest that encenicline itself is not

likely to be used clinically in either of these conditions, perhaps due to specific properties of the

molecule, rather than the target itself.

Our data and other studies suggest that the α7 receptor is a valuable target for improving

attention in disorders where this is impaired, and supports efforts to find a clinically-viable

molecule. Development is still on-going for new α7 partial agonists which may prove more

successful (Bristow et al., 2016). An alternative route of investigation is the positive allosteric

modulators of the α7 receptor but these have yet to be tested in attention tasks. It is important

to note that no drug has yet received a license specifically for improving cognition in

schizophrenia; this is clearly a particularly challenging unmet clinical need. In our view, it is most

likely that pharmacological combined with psychological and other interventions, such as

cognitive remediation and exercise therapy, providing a holistic approach to treatment is most

likely to produce positive effects for this symptom domain and improve quality of life for patients.

In summary, this study demonstrates, for the first time, that separation by performance in the

5C-CPT is feasible using a variable SD. The difference in performance was greatest when the

animals were most challenged; in this case with a 0.75 s SD. Encenicline showed contrasting

effects on attention and vigilance in LA and HA animals. In LA animals, encenicline improves

selective attention, vigilance and response inhibition (impulsive action), but in HA animals

selective attention and vigilance are reduced. These findings show for the first time, that an α7

nAChR partial agonist improves attention when tested in a specific task of attention designed for

high translation (5C-CPT) and that this depends on baseline levels of attention. Our data

suggests that partial agonism at the α7 nAChR has the potential to enhance attention in patients

stratified according to low attentional performance.

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6 Frontoparietal neural activation correlates

with vigilance in the 5C-CPT

Authors: Andrew Hayward, Ines Das Neves, Joanna C. Neill , John

Gigg

[Unplublished data]

6.1 Contributions

Andrew Hayward, John Gigg and Joanna C. Neill planned the study. Training of the animals

was completed by Andrew Hayward. The behavioural aspects of the study were conducted by

Andrew Hayward; histological aspects were completed by Ines Das Neves and Andrew

Hayward. The manuscript was prepared by Andrew Hayward and John Gigg.

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6.2 Context

The previous chapters have focused on pharmacology in the 5C-CPT. Here we aim to establish

if the frontoparietal network is involved in 5C-CPT task performance as an initial step towards

understanding the pharmacological response to these compounds.

6.3 Abstract

The medial prefrontal, cingulate and posterior parietal cortices form the frontoparietal network,

which is essential for orienting attention. Vigilance is the ability to adaptively allocate attention

and respond appropriately to unpredictable stimuli. This process is impaired in clinical

conditions such as attention deficit hyperactivity disorder, schizophrenia and bipolar disorder.

Vigilance is assessed clinically using the continuous performance task (CPT). This has been

translated to rodents as the 5 choice continuous performance task (5C-CPT). As the ability to

translate between rodent and human research is key to drug discovery, a reverse translation of

the 5C-CPT has been produced. In both the reverse translated and widely used Conner‟s CPT

the frontoparietal network is engaged in the human brain. To date, this network has not been

probed in rodents using 5C-CPT. We aimed, therefore, to assess whether task engagement in

the 5C-CPT correlates with activation of the frontoparietal network as shown previously shown

for humans. Female Lister Hooded rats (n=30) were trained to criterion in the 5C-CPT. We then

assessed whether their performance measures in the 5C-CPT (e.g., vigilance; d‟) correlated

with neuronal activity in prelimbic/infralimbic and parietal cortices, as measured by expression of

the immediate early gene c-Fos. C-Fos staining showed a significant correlation with the

vigilance parameter d′. The probability of false alarms (pFA) showed a negative correlation,

which approached significance and was more pronounced in the prefrontal than the posterior

parietal cortex. No correlation was seen for hit rate (pHR) or number of premature responses.

This study identifies the importance of the rat medial prefrontal (mPFC) and posterior parietal

cortex (PPC) for task engagement during the 5C-CPT. In particular, cellular activity correlates

positively with vigilance. This suggests that as task engagement/performance increases this

requires a further matched recruitment of mPFC and PPC circuits. This cellular metric is in

agreement with fMRI studies in clinical versions of the task. Therefore, this study further

supports a vital function of these regions in sustained attention as well as the utility of the 5C-

CPT as a highly translatable task.

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6.4 Introduction

Vigilance is the ability to remain alert to changing situations to optimally either elicit or withhold

a response to a given stimulus, also referred to as “psychological readiness”. Its importance

was highlighted for the first time when studying the performance of radar operators in World

War II who had to monitor radar outputs over extended periods for rare but important events

(Mackworth, 1948). Vigilance is strongly related to attention and is often impaired in psychiatric

conditions such as attention deficit/ hyperactivity disorder (ADHD) (Huang-Pollock et al., 2012,

Riccio et al., 2002) and schizophrenia (Nuechterlein et al., 2015, Luck and Gold, 2008). The

continuous performance task is the most widely used paradigm to measure vigilance in human

subjects (d prime; d′) alongside selective attention (accuracy of response) (Riccio et al., 2002,

Nuechterlein et al., 2008).

Increased activity in the frontoparietal network, as assessed by functional magnetic resonance

imaging (fMRI), has been observed across a diverse array of cognitive functions, including

memory, arithmetic and language, suggesting that it is fundamental for engagement in many

cognitive tasks (Duncan, 2010, 2013). This network comprises the dorsolateral prefrontal,

anterior cingulate and posterior parietal cortices (Petersen and Posner, 2012, Duncan, 2010). A

clinical study by Fedorenko et al. (2013) compared fMRI activity patterns across a similarly

diverse array of tasks, where each had an easy and a difficult version. This showed that more

challenging tasks caused corresponding elevated activity in the frontoparietal network,

presumably due to the higher level of engagement required. Convergent research shows that

the main function of the frontoparietal network is to decide where to orient attention by

combining prefrontal „top-down‟ goal-directed information with a saliency map of the

environment produced by the posterior parietal cortex (Ptak, 2012, Marshall et al., 2015,

Petersen and Posner, 2012, Buschman and Miller, 2007).

In rodents, the frontoparietal network appears to function in a similar way to that in primates,

with increased neuronal firing in the posterior parietal cortex (PPC) when an animal attends to a

stimulus (Broussard, 2012). This is shown by PPC neurones in a sustained attention task firing

in association with successful responses to stimuli compared to misses and further reinforced

by higher firing in response to task-relevant stimuli compared to visually similar distractors

(Broussard et al., 2006, Broussard, 2012). The 5 choice continuous performance task (5C-CPT)

is a translational test used in rodents to assess attention, vigilance and impulsivity (Young et al.,

2009). The task involves either responding to a single visual stimulus („go‟) or withholding a

response when multiple visual stimuli co-occur („no-go‟). The 5C-CPT is the only task to

measure attention, impulsive action and vigilance in rodents simultaneously. As it is based on

the clinical CPT, it has high translational validity and relevance (Lustig et al., 2013, McKenna et

al., 2013, Young et al., 2009). To highlight the latter, McKenna et al. (2013) produced a reverse-

translated version of the 5C-CPT for human use. With this version of the task, they used fMRI to

analyse the functional correlates of vigilance in humans. They found differential activation of the

107

frontoparietal network by the target and non-target trials, demonstrating that the posterior

parietal cortex is activated in both trial types and the dorsolateral prefrontal cortex is activated

during non-target trials (McKenna et al., 2013). This is in agreement with an fMRI study which

used the Conner‟s CPT and also showed activation in both prefrontal and posterior parietal

cortex, thus, frontoparietal engagement in the task (Ogg et al., 2008). As the frontoparietal

network has not been studied previously in rodents for the 5C-CPT, this result is ripe for

translation, which would allow comparison of rodent and human functional circuitry as well as an

assessment of the translational validity of the task.

This study aimed to add further translational validity to the 5C-CPT by assessing the role of the

frontoparietal network in the 5C-CPT. For this two key hypotheses were tested: (1) 5C-CPT

performance will engage neuronal activity across the frontoparietal network. (2) the level of

neuronal activity will correlate positively with successful performance of the task. To detect

frontoparietal engagement, we measured immunohistochemical staining for the immediate early

gene c-fos, which is widely used to indicate neuronal activation or plasticity (Burnham et al.,

2010, Sagar et al., 1988).

6.5 Methods

6.5.1 Animals

Female Lister Hooded rats (Charles River, UK; n=30, weighing 215 ± 20 g at the start of

training) were housed in groups of four or five in individually ventilated cages with two levels

(GR1800 Double-Decker Cage, Techniplast, UK) under a standard 12 hour light: dark cycle

(lights on 7:00 am). The environment was maintained at 21 ± 2 °C, 55 ± 5 % humidity. Animals

were food restricted to 90% free-feeding body weight (usually 10g/rat/day) for training and

testing. All experiments were conducted in accordance with the UK Animals (Scientific

Procedures) 1986 Act and local University ethical guidelines and permissions.

6.5.2 5C-CPT Apparatus, Training and Testing

Standard nine-hole operant conditioning chambers (Campden Instruments Ltd, UK) were

controlled by ABet II Standard software (Lafayette Instrument Company, IL, USA) and used as

previously described (Hayward et al., 2017). Final task parameters (2s stimulus duration, 2s

limited hold, 5s inter-trial interval) were reached over a training period of approximately 24

weeks (Barnes et al., 2012a). Animals were rewarded with 45 mg rodent pellets (Test Diet, MO,

USA). After meeting final criteria of 75% accuracy, <40% false alarms and >20% omissions,

animals were trained once per week to maintain performance and avoid over-training. Animals

were then randomised and allocated in a random but counterbalanced (by task performance)

way to two groups. One group was tested with a standard 5C-CPT procedure (5s limited hold,

1s stimulus duration and 10s variable inter-trial interval; n=23) and the other housed individually

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in their home cage for the same period as a control (n=7). The order in which animals were

tested was randomised.

6.5.3 Tissue Extraction

One hour after testing or control situation, animals were deeply anaesthetised with urethane

(30%, w/v in 0.9% saline, 2 g/kg) and perfused with phosphate buffered saline (1x) followed by

paraformaldehyde (4% w/v) and brains were extracted. Brains were stored in paraformaldehyde

for 48 hours, then sucrose solution (30% w/v) for 24 hours, rapidly frozen in isopentane (Fisher

Scientific, UK) and stored at -80 oC until sectioned. Free-floating sections (40μm) were prepared

using a freezing sledge microtome (Leica, UK) and stored in a cryoprotectant solution (30%

Glycerol, 30% Glycol Ethylene, 40% PBS 1x) at -20oC until staining. The brains were

randomised by animal id before slicing.

6.5.4 Immunohistochemistry

Sections were selected for staining based on the rat brain atlas (Paxinos, 2006) to include

medial prefrontal cortex (mPFC; a combination of cingulate cortex 1 (Cg1) and prelimbic cortex

(PrL)) and posterior parietal cortex (PPC; medial parietal associative cortex; MPtA) (Figure 6.1).

These regions were chosen based on anatomical and functional evidence that they represent

homologues of the human posterior parietal cortex (Corwin and Reep, 1998, Broussard, 2012)

and prefrontal cortex (Uylings et al., 2003). Tissue was randomised by rat identification number

into four batches for staining.

To stain the tissue for c-Fos activity, sections were first washed in 0.1M PBS with 0.1% Triton

(PBS-T) and then immersed in 0.3% hydrogen peroxide (Riedel-de Haën, Germany) diluted in

100% methanol for 15 minutes, to block endogenous peroxidase activity. This was followed by

3x five-minute washes in PBS-T. Sections were then incubated for an hour in a blocking

solution of 10% normal goat serum (Vector Laboratories, UK) and 1% bovine serum albumin

(BSA; Sigma-Aldrich, UK) in PBS-T. Primary incubation with the c-Fos antibody (sc-52

polyclonal rabbit anti c-Fos, Santa Cruz Biotechnology, USA; 1:1000) was carried out overnight

at 4oC. This dilution factor was selected following a series of pilot experiments. After 3x five-

minute washes in PBS-T, sections were incubated with the secondary antibody (Biotinylated

anti-rabbit Ig6 (H+L), Vector Laboratories, UK; 1:200) for 1h at 4°C. Both the primary and

secondary antibodies were used in solution with 1% Goat Serum and 1% BSA in PBS-T.

Following 3x ten-minute washes in PBS-T, an ABC kit solution (Vectastain ABC Kit, Elite PK-

600, standard; Vector Laboratories, UK) was applied for 30 minutes. After 3x ten-minute

washes, a diaminobenzidine solution (DAB substrate kit for peroxidases, sk-4100; Vector

Laboratories, UK) was applied until the tissue had darkened (1.5-2min) and brain sections were

then washed in distilled water. All steps in the staining process were carried out under light

agitation. Once three washes had been completed, the stained sections were mounted onto

polylysine slides (Fisher Scientific, UK) and sequentially dehydrated in 70%, 90% and 100%

109

ethanol (5 minutes per concentration). The clearing agent, Histo-clear II (National Diagnostics,

UK) was applied for 5 minutes, and the slides were finally coverslipped with DPX (Sigma-

Aldrich, UK) as the mounting medium.

Figure 6.1: Regions analysed for C-Fos quantification. Representative brain sections to delineate

regions (grey) analysed for c-Fos-positive cells in mPFC (PrL and Cg1) and posterior parietal cortex

(PPC). (B) A representative example of c-Fos-positive staining from the prefrontal cortex. Key: Cg1,

cingulate; PrL, prelimbic cortex; PPC, posterior parietal cortex. The level of sections labelled relative to

Bregma. Adapted from Paxinos (2006).

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6.5.1 Stain Optimisation

To maximise stain quality the concentration of primary antibody, length of incubation and

distribution of sections in staining wells was optimised. In the first optimisation experiment, the

procedure outlined in the immunohistochemistry section was carried out using primary antibody

concentrations of 1:50, 1:300, and 1:500. For this, rat brain sections that did not contain the

areas of interest for the study were selected from 4 animals. For each animal, 3 sections of

brain tissue were stained at each of the selected antibody concentrations. A comparison of 24

or 48 hour incubation was conducted for each concentration. As staining for the two conditions

was similar, 24 hours was used to improve speed and tissue integrity. Frontal and parietal

sections (from the same level as those to be used in the main experiment) were selected from 2

of the four animals. The sections from one animal were separated into two wells; 6 frontal

sections in one and 3 parietal sections in the other. The second animal‟s sections were

combined in the same well. The tissue was incubated in primary antibody (1:300) for 24 hours.

The combined method resulted in uneven staining, so frontal and parietal tissues were

separated in further staining.

The second optimisation experiment was based on the results of the first. Here, brain tissue

was stained at the following concentrations: 0 (negative control), 1:300, 1:500 and 1:1000.

Primary incubation was carried out overnight. All sections used in this experiment were from

between the frontal and parietal regions. The 1:1000 primary antibody concentration provided

the clearest distinction between nuclei and background and so was used in the main

experiment. The negative control showed no specific staining.

6.5.2 Quantification

Staining and counting were performed blind to animal grouping and task performance to prevent

experimenter bias. Images were acquired via an Olympus BX51 microscope and counting was

conducted online to allow adjustment of illumination/focus and improve identification (Image-Pro

Plus v6.3, Media Cybernetics Inc.). Regions were identified bilaterally and selected at 4x

magnification. Counting was completed on a median of sixty 120x120μm squares randomly

positioned bilaterally within each region of interest and spread across three sections for each

region for each rat (Figure 6.1). Cells were counted and the median for each region (mPFC and

PPC) calculated.


The method for calculation of all parameters in relation to behavioural performance was as

described in detail in Tomlinson et al. (2014) and Young et al. (2013). The signal detection

theory measure d′ was used in this case only after confirming that probability of hit rate (pHR)

and false alarms (pFA) (see below) was normally distributed (Young et al. (2013). d′ was

calculated in the same way as by Young et al. (2013) as the z score of pHR minus the z-score

of pFA.

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( ) ( )

As d′ is used as the measure of vigilance in clinical CPT, the use here is a further step to

improve translatability of preclinical research compared to using the sensitivity index (Young et

al., 2013).

Statistical tests were conducted using InVivoStat v3.4 (Clark et al., 2012). Graphs were

produced in GraphPad Prism for Windows 7 (GraphPad Software, USA). A repeated measures

Analysis of Covariance (ANCOVA) was used to compare the tested (n=23) to non-tested (n=7)

animals, with testing as a between-subject factor and region (mPFC vs. PPC) as the within-

subject factor. The four staining runs were used as a blocking factor to account for any variation

due to differential staining and count order was used as a covariate due to a trend in count

order and the number of cells (Figure 6.2). The analysis used Fisher protection as this only

reports the results of planned comparisons if model effects were significant. Thus, it reduces the

number of comparisons and, therefore, the chance of false positives. For all comparisons

normality of data was judged using Q-Q plots and sphericity of data by residual vs. predicted

plots.

0 1 0 2 0 3 0

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C o u n t O rd e r

C-F

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m P F C

P P C

Figure 6.2: Correlation between count order and the number of identified c-Fos-positive neurones.

A practice effect was clear where the number of identified cells increased with further assessments of

sections. Due to randomisation of count order, this did not bias the data; also count order was used as a

covariate to account for this trend statistically.

To analyse correlation of c-Fos-positive staining with task performance, a multivariate analysis

of covariance (MANCOVA) was used. In this, region (mPFC vs PPC) was compared to the

continuous variable d′, p[FA], p[HR] or premature responses; the blocking factor of staining

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batch and the covariate of count order were also used as in the previous comparison. For

reporting MANCOVA results, the r2 value was used to show how much variance is explained by

this model. The r2 was adjusted for the number of variables used. The significance of any factor

in the model is reported as F value with degrees of freedom and p-value.

6.6 Results

Significant model effects were seen for region (Figure 6.3; F1,26=29.95,p<0.0001) as well as the

interaction between testing and region (F1,26=6.8, p=0.015). Planned comparisons were

significantly different between frontal and parietal in tested (p<0.001) and non-tested animals

(p<0.01). However, whether rats were from tested or home cage groups had no effect on the

number of c-Fos-positive cells (Figure 6.3; F1,26=0.01, p=0.93).

m P F C P P C

0

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T ested

N o n -T e s ted******

Figure 6.3: C-Fos-positive cell count was higher in PPC compared to PFC. Tested animals (n=23)

and non-tested group (n=7). A significant difference was seen between prefrontal cortex (mPFC) and

posterior parietal cortex (PPC). C-Fos-positive cell count was similar between tested and non-tested

groups. Boxes extend from the lower to the upper quartile of each data set; the midline corresponds to the

median and whiskers indicate the data range (min to max). *** denotes the model level effect of p<0.001

in the factor REGION.

For d′ the MANCOVA model fit the data well (r2=0.741) and showed a significant positive

correlation between the number of c-Fos-positive cells and the vigilance measure d′ (Figure

6.4C; F1=12.57, p=0.0011), where cell counts increased with d′. There was no significant

difference between regions (F1=2.42, p=0.1285) or any interaction between region and d′

(F1=0.319, p=0.94).

No significant influence of pHR (r2= 0.6632, F1=0.37, p=0.55) or premature response was seen

(r2=0.6733, F1=2.5, p=0.1222). For pFA, the model fits well (r

2=0.6911), and a negative

correlation of less staining with higher pFA was observed that closely approached significance

(F1= 3.77, p=0.0599). While the interaction between region and pFA was not significant, it can

be seen that this trend was more pronounced in the mPFC than the PPC (Figure 6.4).

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0 .0 0 .2 0 .4 0 .6 0 .8

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-Fo

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me

dia

n)

r2= 0 .6 7 3 3 p = 0 .1 2 2 2

D

Figure 6.4: Vigilance (d′) correlates positively with the number of c-Fos-positive cells in mPFC and

PPC (C). (A) There was no significant correlation in tested rats between pHR and number of c-Fos-positive

cells. (B) A negative correlation trended towards significance for pFA, and the correlation was stronger in

the PFC than the PPC. (C) A significant positive correlation was seen comparing d′ to the number of c-

Fos-positive cells, which shows that animals with higher d′ also had higher abundance of c-Fos-positive

cells. (D) No significant correlation was seen when comparing the number of premature responses to c-

Fos-positive cell count. R2 values were adjusted to account for the number of comparisons and denote

how well the data fit the ANCOVA model, p values denote the significance of the model effect for each

behavioural measure.

6.7 Discussion

This study has shown a positive correlation between vigilance (d′) and c-Fos expression in

mPFC and PPC. Thus, activation of the frontoparietal network (mPFC and PPC) of the rat

during 5C-CPT correlates with performance in the task. This shows, as hypothesised that neural

activity is linked to and correlated with performance in the 5C-CPT. However; the study also

revealed no difference in c-Fos-positive cell count between tested and untested animals, which

suggests the system is not involved in task performance. Based on previous studies

investigating neurotransmitter release (Broussard, 2012, Dalley et al., 2004a, Robbins, 2002),

fMRI BOLD signal (Duncan, 2013, Petersen and Posner, 2012) and the electrophysiological

measures of single unit firing and local field potentials (Broussard and Givens, 2010, Donnelly

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et al., 2014, Donnelly et al., 2015) in these regions during performance on tasks of attention,

we would expect to see a difference between tested and untested animals. Therefore, we can

conclude one or more of the following: (1) the animals within their home cage utilised the same

regions for related or unrelated processes (e.g. attending to stimuli around the home cage); (2)

The method used did not have high enough spatial or temporal resolution to detect changes

and other methods with higher temporal or spatial resolution should be considered in the future

(3) variation was too high and so larger group sizes are needed (4) inter-individual differences

were too great to allow between subject comparison, and a method should be considered which

allows within-subject comparisons of changes in activation at multiple time points. For example,

as used in fMRI studies such as that of McKenna et al. (2013), the signal is normalised to a

baseline from within the same subject. It is not possible with post-mortem techniques such as c-

Fos immunohistochemical staining to assess multiple time points (i.e., attentive and non-

attentive) or (5) that rats do not utilise the frontoparietal network to perform the 5C-CPT.

The final suggestion above is unlikely to be true. A number of studies have shown that lesion of

either prelimbic or cingulate cortex of the rodent severely reduces sustained attention in the 5

choice serial reaction time task (effectively the 5C-CPT without „no-go‟ trials) (Bari and Robbins,

2011, Muir et al., 1996). This is supported by PPC recordings that show that more neurones

fire during a sustained attention task when a response is made to a stimulus compared to

omitted trials and that less firing is seen in response to a distracting stimulus compared to task-

relevant stimuli (Broussard et al., 2006, Broussard, 2012). This demonstrates the involvement of

the rodent frontoparietal network in tasks similar to the 5C-CPT, both using different methods.

This is further shown by the correlation of c-Fos with vigilance in our study and may suggest

that the control was unsuitable or too variable (note the high variance in the home cage group:

Figure 6.3).

In the tested group, c-Fos staining in both the mPFC and PPC was strongly and specifically

correlated with performance in the 5C-CPT via the measure d′, which represents vigilance. As

vigilance requires both the allocation of sustained attention and the ability to withhold

responding, it is the most challenging construct tested by the 5C-CPT. Therefore, we suggest

that this increase in the number of c-Fos-positive cells represents increased utilisation of the

frontoparietal network due to higher task engagement. This is consistent with the clinical study

by Fedorenko et al. (2013) where task difficulty was positively correlated with larger responses

from the multiple demand system, which includes the frontoparietal network. The prefrontal

cortex is thought to use event-related inhibitory alpha oscillations to reduce the response to

salient, but task-irrelevant stimuli (Klimesch, 2012, Jensen and Bonnefond, 2013). So the near

significant (p=0.0599) negative correlation between c-Fos count and pFA may be of interest to

pursue with methods of higher temporal resolution, as hypofrontality or decrease in mPFC

activity is linked to increased impulsive/hyperactive symptoms in ADHD patients (Huang-Pollock

et al., 2012, Riccio et al., 2002).

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In conclusion, we have shown for the first time that the level of mPFC and PPC cellular activity

correlates positively with vigilance in the 5C-CPT. This is strongly supported by previous

experiments in humans and non-human primates using indirect measures of neuronal activity

such as fMRI. However, spatially and temporally specific methods are needed to ascertain how

the frontoparietal network is involved in task performance in rodents, where a large portion of

novel ideas are tested. The 5C-CPT offers a valuable translational methodology, but more

research is required to determine how changes in task performance are represented in the

brain. Advancement in this area would greatly improve the translation of research from rodents

towards clinical application.

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7 Oscillations in cognition: an attention-

promoting dose of methylphenidate

augments alpha rhythm in the dorsal

attention network

Authors: Andrew Hayward, Joanna C. Neill, John Gigg

[Unpublished data]

7.1 Contributions

Andrew Hayward and Joanna C. Neill and John Gigg planned the study. The experimental

procedures were conducted by Andrew Hayward and John Gigg. Analysis was completed by

Andrew Hayward. The manuscript was prepared by Andrew Hayward and John Gigg.

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7.2 Context

The previous chapter established that the frontoparietal cortex is more engaged in animals

which perform better in the task so we wanted to study if methylphenidate increased activity in

these regions as a possible endophenotype of attention modulating compounds.

7.3 Abstract

The frontoparietal network orients attention in both humans and rodents. In humans,

methylphenidate is the first line treatment for attention deficit hyperactivity disorder (ADHD) due

to its ability to improve attention. It increases functional magnetic resonance imaging blood

oxygen level dependent signal (fMRI BOLD) in the frontoparietal network, both during visual

attention tasks and in resting state in ADHD patients. The increase in activity across the

frontoparietal network correlates with improvement in ADHD symptoms.

Electroencephalography in patients has shown that methylphenidate increases alpha

oscillations in frontal and parietal regions as well as increasing beta in frontal regions, which

correlates with improved performance in attentive tasks. This study aimed to assess if

methylphenidate can increase activity in the frontoparietal network in rats, and whether this

activity change is seen in particular within the alpha and beta frequency bands.

We recorded the intracranial electrical activity of the PFC and PPC in five urethane

anaesthetised female Lister Hooded rats after saline (vehicle) and then methylphenidate

(1mg/kg) injection. Subsequent analysis found that around 30 minutes after methylphenidate

injection a significant increase of alpha band oscillations arose in the PFC, alongside this in

PFC the slower theta and delta bands reduced in power and the high-frequency gamma band

also reduced in power. In the PPC high gamma band oscillations significantly increased at the

same time point.

Alpha oscillations are related to selection and suppression of visual stimuli in attention and so

the presence of these in the PFC is in agreement with our hypothesis. As previous experiments

used scalp EEG, the lack of gamma oscillations in these is not surprising as they do not travel

far from the source. Finally, beta oscillations were seen in EEG, but not here, as beta

oscillations are linked to cognitive focus, they may only be seen during active task engagement.

In conclusion, we have shown for the first time that a pro-attentive dose of MPH has a

significant effect on alpha, delta and gamma oscillations within the frontoparietal network of the

rat.

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7.4 Introduction

Attention is an essential process for adaptively allocating limited neural resources across a

broad range of incoming stimuli. It is an essential component of goal-directed behaviours,

including verbal and spatial working memory, response inhibition and decision-making.

Techniques such as functional magnetic resonance imaging (fMRI) and electroencephalography

(EEG) have been used to study brain regions involved in attentional processing. Indeed, fMRI

data across a diverse array of tasks has identified a set of commonly recruited brain regions.

These regions form the multiple demand system and are involved in orienting to and processing

stimuli (Duncan, 2010, Duncan, 2013). The key cortical regions of this system for orienting of

attention are the prefrontal cortex (anterior cingulate cortex and dorsolateral prefrontal cortex)

and the posterior parietal cortex (intraparietal sulcus), collectively termed the frontoparietal

network (Duncan, 2010, Petersen and Posner, 2012, Duncan, 2013). Altered activity has been

identified in this network in conditions with inattention such as attention deficit hyperactivity

disorder (ADHD) (Lin et al., 2015) and schizophrenia (Poppe et al., 2016, Tu et al., 2013). The

importance of the network is also shown by its preservation through evolution, as it has been

shown in humans (Duncan, 2013, Petersen and Posner, 2012, Scolari et al., 2015), non-human

primates (Duncan, 2010), rats (Broussard et al., 2006, Broussard, 2012, Broussard and Givens,

2010) and other rodents (Sellers et al., 2016).

Methylphenidate (MPH) is the current first line treatment for ADHD in the national institute of

clinical excellence (NICE) guidelines due to its ability to improve attention (Palanivel et al.,

2009). In rodents the ability of MPH to improve attention has been demonstrated clearly in

animals with low performance in the 5 choice serial reaction time task (5C-SRTT) and the 5C-

CPT (Tomlinson et al., 2014, Puumala et al., 1996, Blondeau and Dellu-Hagedorn, 2007,

Caballero-Puntiverio et al., 2017). MPH acts at dopamine and noradrenaline transporters to

inhibit dopamine and noradrenaline reuptake at presynaptic terminals in the prefrontal cortex

and nucleus accumbens (Heal et al., 2009). This action promotes a large efflux of dopamine

and noradrenaline, which peaks around 30 minutes after i.p. injection. MPH-evoked

neurotransmitter changes were also linked to increased fMRI BOLD signal within the

frontoparietal network during working memory and visual attention tasks compared to saline

treatment in healthy adults (Tomasi et al., 2011). Also, MPH can normalise resting state fronto-

parieto-cerebellar network dysfunction in children with ADHD and the normalisation of this

network correlates strongly with a reduction of ADHD symptoms (An et al., 2013). Studies using

electroencephalography (EEG) to examine neural activity as the synchronised electrical

oscillations of groups of neurones have shown that MPH increases alpha oscillations at central

and parietal electrodes as well as increasing beta at frontal electrodes, which correlates with

improved performance in attentive tasks (Loo et al., 2004). EEG offers a very high temporal

resolution (but poor spatial resolution) as well as insight into the various activation states

promoted by different frequencies. However, it cannot easily reveal information about the

operation of circuits within the brain, which requires a targeted and invasive approach.

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In this study, we recorded the local field potential (LFP) from PFC and PPC using multi-

electrode array depth recordings in urethane-anaesthetized rats. We assessed the effects of

MPH on these regions to establish whether they are the source regions for previously-described

MPH-induced alterations in EEG oscillations. Based on the fMRI data we expected to see

increased activity in both PFC and PPC when treated with MPH compared to vehicle treatment

and from the EEG studies we expected the main effect of MPH to be in the alpha and beta

frequency ranges.

7.5 Methods

7.5.1 Animals

5 female Lister Hooded rats (275g ± 30g on the day of surgery) were group housed in

individually ventilated cages with two levels (GR1800 Double-Decker Cage, Techniplast, UK)

under a standard 12 hour light: dark cycle (lights on 7:00 am). The environment was maintained

at 21 ± 2 °C, 55 ± 5 % humidity. Animals were anaesthetised between 9:00-12:00 during their

light phase. All experiments were conducted in accordance with the UK Animals (Scientific

Procedures) 1986 Act and local University ethical guidelines.

7.5.2 Surgery

Initial anaesthesia was induced by urethane (30%, w/v in 0.9% saline, 1.3 g/kg) and up to two

top-up doses of urethane (0.1 ml) were administrated at approximately 30-min intervals until

areflexia was achieved. Body temperature was maintained at 37 °C using a homoeothermic

heating pad. The rat was head-fixed in a stereotaxic frame and a 2-mm diameter craniotomy

was drilled above mPFC (Bregma + 2.5-3.0, ML 2.5, DV 3.0) and PPC (Bregma - 4.5, ML 2.0,

DV 1.0). The dura was carefully incised and a 2 x 16 multi-electrode array (NeuronexusTech,

USA) was inserted at each site. The electrode had two 10mm shanks, each with 16 iridium

contacts (recording area 177μm2) spaced linearly (100μm apart) along each shank with 500μm

between shanks. The frontal electrode was inserted at a 30° angle from vertical, in the coronal

plane. The posterior electrode was inserted vertically in the sagittal plane.

7.5.3 Recording

Electrodes were coated using the lipophilic fluorescent compound Di-I before insertion, and

placement was confirmed post hoc (DiCarlo et al., 1996). Electrode arrays were attached to an

electrode board and preamplifier (Plexon, USA) with a fixed gain of 20x. An AC amplifier was

adjusted to give a total gain of 2000x (Recorder64, Plexon, USA). Data were processed online

using a Windows computer running (Recorder64 (Plexon, USA). LFPs were digitised at 1 kHz

and bandpass filtered (1-500Hz). Once stable signals were achieved each animal received a

saline injection (1 ml/kg, i.p.) and the LFP was recorded for 30 min. Animals then received an

injection of theo-methylphenidate hydrochloride (Tocris Biosciences, UK) made up in saline

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(0.9% NaCl) to a dose of 1 mg/ml and an injection volume of 1 ml/kg (i.p.). LFP was then

recorded for a further 60 minutes.

7.5.4 Histology

After recording animals were perfused with phosphate buffered saline (1x) then

paraformaldehyde (4% w/v) and brains were extracted. Brains were stored in paraformaldehyde

for 48 hours, then sucrose solution (30% w/v) for 24 hours. Free-floating sections were sliced at

40μm using a freezing sledge microtome (Leica, UK) and imaged Olympus BX51 microscope

using a Texas red filter. Sections were later Nissl stained to confirm anterior-posterior position.

7.5.5 Analysis

The analysis was completed using custom written MATLAB scripts. Signals were down-sampled

and filtered using the function decimate to 250 Hz and the power spectral density (PSD)

calculated using the Chronux toolbox function for power spectral density (PSD; mtspecgramc)

using a sampling window of 2 seconds, a shift of 1.8 s, a time-bandwidth product of 4 and 2.5

multi-tapers. Average PSD across the 30 minute saline treatment was calculated and for each

time point in the methylphenidate analysis, the PSD was divided by the average saline PSD to

normalise each animal to its saline treated baseline (Figure 7.2). The relative PSD was then

averaged within the band's delta (1-4 Hz) theta (4-8 Hz) alpha (8-15 Hz) beta (15-30 Hz) low

gamma (30-45 Hz) and high gamma (55-90 Hz). Relative PSD of the methylphenidate recording

for each band was then averaged into three 20 min bins. Time points and regions were

compared using a two-way ANOVA with animal ID as a blocking factor to produce a pseudo-

repeated design (InVivo Stat, v3.4; Clark et al. (2012)). Least Square Difference planned

comparisons between the first time point and the other two for each region were adjusted with

the Benjamini-Hochberg procedure to reduce the false discovery rate (Benjamini and Hochberg,

1995).

7.6 Results

Initial processing with the Chronux function mtspecgramc showed that the saline control was

stable across the 30 min recording, but with some fluctuation, particularly in the high gamma

range (Figure 7.1). Therefore, the average across the 30 minutes is highly representative of the

state of the regions after saline application. This supports the normalisation of methylphenidate

data to the saline control in order to show drug effects.

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Figure 7.1: representative power spectral densitiy for saline recording. This shows a stable recording during saline application allowing this data to be used to normalise the methylphenidate data to. Colour bar represents power spectral density in microvolts per hert.

The PSDs were normalised to saline control and showed a clear deviation from this baseline

approximately 30min after MPH injection (Figure 7.2). This change was seen to start in all

animals 20-40 minutes into the MPH recording. From this point onwards a clear difference was

seen, which represents the neural action of methylphenidate. For statistical analysis, the signal

was decomposed into the relevant frequency bands (delta, theta, alpha, beta, low gamma and

high gamma) and also into three 20-minute time bins to account for the observed time-

dependent changes.

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In the mPFC, the delta band significantly reduced over time (F2=7.18, p=0.0045; Figure 7.3A)

adjusted LSD comparisons agreed for the mPFC at 20-40 and 40-60 minutes (p<0.05 and

p<0.01, respectively) when compared to 0-20 minutes. The theta band (Figure 7.3B) showed

no significant model level effects, but adjusted LSD comparisons for the mPFC were

significantly different for 20-40 and 40-60 minutes (p<0.05 for both) compared to 1-20 minutes.

In the alpha band a significant increase over time was seen (F2=9.64, p=0.0012; Figure 7.3C)

and adjusted LSD comparisons showed a significant difference for mPFC between 1-20 and 40-

60 minutes (p<0.01). In the beta (Figure 7.3D) and low gamma (Figure 7.3E) bands, no

significant differences were seen. However, in high gamma a significant interaction between

time and region was seen (F2=7.06, p=0.0048; Figure 7.3F) and LSD comparisons showed this

to be a significant reduction at 40-60 minutes compared to 1-20 minutes for mPFC (p<0.05), but

an increase in the same comparison for PPC (p<0.05).

Figure 7.2: Methylphenidate takes 20-40 minutes to cause changes in the neural oscillations. A representative spectrogram from a single rat shows the onset of LFP changes in the mPFC and PPC approximately 30 minutes after MPH injection (injection at 0min). The main effect in this example is a reduction of power in low frequency delta and theta and increased power in alpha within the mPFC. In the PPC power is increased for theta and high gamma bands. Colours represent the power relative to saline.

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Figure 7.3: Methylphenidate significantly increases power in the alpha band, particularly in the

mPFC, while reducing power at lower frequencies. Delta, Theta and high Gamma bands were

significantly reduced in mPFC 40-60 minutes after dosing, while alpha band oscillations were significantly

increased. In the PPC methylphenidate promotes increased high gamma band activity. * p<0.05 ***

p<0.01.

7.7 Discussion

In this study, the LFP oscillations in the mPFC and PPC were studied following administration of

methylphenidate, the current first-line treatment for ADHD. The main effect of MPH dosing was

a dramatic change in neural oscillations that started around 30min after injection. The latency of

this change is consistent with that previously reported for peak dopamine and noradrenaline

release in the striatum and mPFC following i.p. MPH injection (Heal et al., 2009, Berridge et al.,

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2006). The timing of the change supports the hypothesis that the effects seen after that point

were due to MPH-evoked catecholamine signalling. In the mPFC reduced low-frequency delta

and theta oscillations were seen alongside increasing alpha band activity, which more than

doubled in the period 40-60min post-injection compared to the vehicle reference. Some

changes in beta oscillations were seen, but were not consistent across animals and so did not

reach significance. Finally, for mPFC, the high gamma band reduced in the final 20min period

post-injection compared to the first. However, in PPC a significant increase was seen in the high

gamma band (Figure 7.3F).

Our observed changes in LFP power following MPH dosing are likely to be relevant to the

clinical effectiveness of this drug. Delta oscillations are thought to represent basic homeostatic

and motivational processes (Knyazev, 2012) and so reduction of this is likely the result of power

moving into other frequency bands. Theta at rest is thought to represent under arousal or

drowsiness (Barry et al., 2003, Lazzaro et al., 1999, Sergeant, 2000, Tye et al., 2014) –

effectively disengagement from a task - and its presence over frontal and central regions is

strongly correlated to ADHD symptoms in children and adults with the condition (Barry et al.,

2003, Shi et al., 2012, Tye et al., 2014). Therefore, the reduction seen here in the PFC may be

related to the stimulant effect of methylphenidate to promote arousal and reduce theta power.

Activity in the alpha band is thought to be inhibitory in nature (Jensen and Mazaheri, 2010),

allowing it to fit a filtering role for suppression and selection of stimuli in attention (Klimesch,

2012). Thus, the alpha power increase seen here may represent the mechanism by which MPH

facilitates the filtering of sensory input to support attention. In rodent studies, increases in alpha

were seen during anticipation of stimuli and around distraction signals, supporting a role in

inhibition of response in rodents, as in humans (Broussard and Givens, 2010). In relation to

MPH, this could relate to increased sustained attention, which has been shown behaviourally

(Tomlinson et al., 2014, Heal et al., 2013). Furthermore, alpha oscillations are thought to

negatively modulate excitatory gamma band activity (Jensen and Mazaheri, 2010). In the

context of the current study, this may explain why a reduction of high gamma in the mPFC was

seen alongside an increase in alpha, whereas, in the PPC, the reverse was observed. Recent

evidence using magnetoencephalography in humans performing an attentive task further

supports the theory that high gamma (70-120 Hz) in the mPFC modulates the PPC which, in

turn, modulates the visual cortices at the same frequency band; this results in an increase in

synaptic gain, thereby enabling allocation of attention (Marshall et al., 2015). It is our hypothesis

that MPH promotes sustained and selective attention through control of alpha and gamma

within the frontoparietal network. It is through this mechanism that we propose well-documented

increases in attention and vigilance may be mediated (Tomlinson et al., 2014, Puumala et al.,

1996, Blondeau and Dellu-Hagedorn, 2007, Caballero-Puntiverio et al., 2017).

Moving forward with this research there are two key directions: Firstly, establishing how MPH

alters ongoing electrical oscillations within the active brain, which could be performed with

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simultaneous recording of electrical activity with 5C-CPT performance as has recently been

done in the study of impulsivity in the 5C-SRTT (Donnelly et al., 2014, Donnelly et al., 2015).

This would allow insight into whether MPH alters the timing of oscillation patterns, such as

event-related synchronisation and desynchronisation in the alpha band (Klimesch, 2012).

Secondly, other compounds could be studied in the same way to add a much deeper insight

into how these changes are caused. For example, atomoxetine is the third line treatment for

ADHD but has a more selective pharmacology by only acting at noradrenaline reuptake

transporters. Response to this drug could help dissociate the noradrenergic versus

dopaminergic effects of MPH.

In conclusion, we have shown for the first time that a pro-attentive dose of MPH has a

significant effect on EEG oscillations within the frontoparietal network of the rat. In particular,

MPH reduced delta, theta and high gamma as well as increased alpha in the mPFC and

increased high gamma in the PPC. These effects suggest that MPH could facilitate the

„normalisation‟ of the EEG in low-performing subjects to improve sustained attention and reduce

distractibility.

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8 General Discussion

In the first part of this thesis modelling of attention deficits by grouping rats based on

performance in the 5C-CPT was reviewed. This method was then refined and validated

(Chapter 3) to facilitate this type of modelling. For this, three compounds were assessed for the

first time in this model (Chapters 4 and 5), the involvement of the frontoparietal network in the

5C-CPT was verified (Chapter 6) and the effect of methylphenidate on this network was

established (Chapter 7). This work was conducted to fulfil three general aims:

iv) To further develop a method of modelling high and low attention in rats.

v) To use the high and low attentive model to study the pharmacology of

attention

vi) To examine the involvement of the frontoparietal network in performance of

the 5C-CPT and the effects of attention-promoting agents

The work here has established a more translatable model that is effective for studying

compounds with attention enhancing potential.

8.1 Aim 1: To further develop a method of modelling

attention deficits in rats.

In Chapter 3 the method of characterizing animals by performance as a model of low attention

was refined from, and compared to, the work of Tomlinson et al. (2014). There were two

changes made to the experimental design of that work: firstly, rather than grouping animals as

above or below a series of set values (e.g., 70% accuracy), we chose to use the upper and

lower quartiles of the group and exclude animals with performance close to the median. The

exclusion of the moderate performers resulted in a larger seperation between groups; secondly,

we used the measure d′ from signal detection theory to establish vigilance in the animals. This

is important as d′ is the measure used clinically in the CPT, where sensitivity index (SI) was

used previously in pre-clinical studies. This is due to SI being robust to non-parametric data,

however, animals produce normally distributed data sets so d′ is more applicable (Young et al.,

2013). Therefore, the work outlined in Chapter 3 establishes a refined model that has a clearer

deficit of attention and higher translational potential than the previous model. The work in

Chapters 4 and 5 extends this to test compounds that have not previously been tested in 5C-

CPT using animals grouped by performance.

The study in chapter 3 confirmed that methylphenidate increases selective attention and

vigilance in LA animals, but there were a number of differences to Tomlinson et al. (2014) on

other measures. For example, a reduction of omissions was seen with the refined method,

which represents an increase in sustained attention. This was not observed in the study by

Tomlinson et al. (2014), but is reproduced in other studies using the 5C-SRTT in low-performing

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animals (Paterson et al., 2011). Improvements in sustained attention are also seen in ADHD

patients treated with methylphenidate and in low attentive individuals without a diagnosis of

ADHD (Agay et al., 2014). We can, therefore, propose that the robust deficit produced by the

refined model presented here enhances the translational potential of findings in this model. For

atomoxetine, the same improvement in LA animals for accuracy and d′ was shown in Chapter 3

and by Tomlinson et al. (2014). The reduction of premature responses was not seen in

Tomlinson et al. (2014). During vehicle treatment, the number of premature responses was

significantly different between HA and LA animals, which shows that LA animals also presented

with high waiting impulsivity. Therefore, the reduction in premature responses is in agreement

with the results of atomoxetine in high impulsive animals using a different separation method

(Tomlinson et al., 2014).

Due to the similarities in the effects of both methylphenidate and atomoxetine to those reported

in the literature, the conclusion derived is that this method is capable of studying

pharmacological treatments in a translational manner and represents a refinement in modelling

low attention. It was used, therefore, to address the second aim of the Thesis.

8.2 Aim 2: To use the high and low attentive model to study

the pharmacology of attention

Due to the wide range of conditions with inattention as a symptom, it is essential to continue the

development of novel modulators of attention and to study how well-known compounds interact

in the LA model validated in Chapter 3. Research has primarily focussed on monoaminergic

compounds as attention promoting agents due to the clinical success of amphetamine,

methylphenidate and atomoxetine in ADHD. However, the ability to produce a robust deficit

without chemical or genetic manipulation allows an unbiased assessment of under-researched

mechanisms. In Chapter 4, therefore, we studied nicotine and caffeine, two established

psychoactive stimulants that facilitate sustained attention (Rezvani and Levin, 2001, Heishman

et al., 2010, Levin et al., 1998).

Acute doses of both nicotine and caffeine increased 5C-CPT impulsive behaviour as measured

by pFA in animals that were not grouped based on their performance. In animals which were

grouped by performance, nicotine increased impulsivity only in HA animals. In addition, nicotine

also reduced omissions, thereby, increasing sustained attention, as previously reported.

Therefore, these findings not only support previous work but also highlight the power of the 5C-

CPT to use both pFA and premature responses as robust measures of impulsivity. This is

important as waiting impulsivity (premature responses) and response disinhibition (pFA) are

separate constructs, which can be manipulated independently (Young et al., 2011). Also, recent

research suggests that premature responses are linked more to response timing strategy than

impulsivity (Cope et al., 2016). It is suggested that nicotine in particular should be researched

further with chronic dosing regimens as a high level of variability in the present nicotine study

128

may have been due to the acute effects of nicotine on stress markers such as corticosterone,

which are not present in chronic dosing (Matta et al., 2007).

Nicotine has shown some potential to modulate attention in the 5C-CPT and genetic knock-out

of the α7 nicotinic acetylcholine receptor induces attention deficits. Encenicline is a partial

agonist of this receptor with cognition improving properties (Prickaerts et al., 2012), but has not

yet been assessed for its effect on attention in LA subjects. In Chapter 5 a detailed analysis

using the model developed in Chapter 3 revealed that encenicline has a contrasting effect in HA

and LA animals. In LA animals it improves performance across measures of accuracy, response

disinhibition and vigilance, but in HA animals it impaired performance on the same measures.

This suggests that encenicline may be an effective compound for the reduction of inattentive

symptoms in states such as ADHD, schizophrenia or Alzheimer‟s disease and that baseline

inattention should be a factor when deciding which treatment to use. As there are few

compounds available to alleviate LA in schizophrenia, this could be an important compound for

pharmacotherapy. However, encenicline has not shown efficacy in stage II clinical trials for

schizophrenia and in another trial elderly Alzheimer‟s patients developed severe gastrointestinal

side effects. As the α7 nicotinic acetylcholine receptor is an area of increasing research for

schizophrenia, it is hoped we will see compounds that will overcome the issues faced by

encenicline (Young and Geyer, 2013). One promising compound has been recently

characterised demonstrating a continued interest in the receptor (Bristow et al., 2016).

8.3 Aim 3: To examine the involvement of the frontoparietal

network in performance of the 5C-CPT and effects of

attention promoting agents

The frontoparietal network is involved in the process of orienting attention both in human and

non-human primates. However, its involvement in the performance of the 5C-CPT in rodents

has not been investigated. In Chapter 6 this was probed by ex vivo immunohistochemistry for a

marker of neural activity, the immediate early gene C-Fos. The present results showed a

correlation between C-Fos expression in the frontoparietal network and vigilance in the 5C-CPT,

which suggests involvement of the medial prefrontal cortex and posterior parietal cortex in 5C-

CPT performance. This is not clear though as there is no difference between untested controls

and animals performing 5C-CPT. In the context of previous studies showing the involvement of

both regions in humans and non-human primates using indirect measures of neuronal activity

such as fMRI (Duncan, 2010, Duncan, 2013, Petersen and Posner, 2012), it seems possible

that flaws in the untested control may explain this, but this requires further analysis.

In Chapter 7 the response of the frontoparietal network to an attention-promoting dose of

methylphenidate was assessed in anaesthetised animals. This study showed for the first time

that methylphenidate simultaneously promotes alpha and gamma EEG rhythms whilst reducing

both delta and theta oscillations in the frontoparietal network. Alpha oscillations have been

129

associated with the suppression and selection of stimuli to promote attention. Increases in theta

have been associated with a loss of attentional focus (introspection). Therefore, the effects seen

in this study may be a mechanism by which methylphenidate alters the firing patterns of

neurones in the frontoparietal network to improve attention.

8.4 Limitations and Future Research

The majority of preclinical research using 5-choice tasks has used male rodents. Childhood

ADHD is diagnosed more frequently in males (2.6-5:1) than females, however, in adulthood, this

ratio reduces (1-2:1) (Bachmann et al., 2017, Williamson and Johnston, 2015b). This may be

due to the symptom profile of ADHD in males and females being different. Females tend to

show higher levels of inattentive symptoms, whereas, males tend to show a higher prevalence

of hyperactive and impulsive symptoms (Williamson and Johnston, 2015b, Ramtekkar et al.,

2010a). As the latter reduce more with ageing than inattentive symptoms, females are more

likely to persist into adulthood with ADHD.

Based on this, there is a female population with high inattention in adulthood, which is poorly

represented in the preclinical research. Researchers have avoided using female rodents mainly

due to fear of increased variation due to the stages of the oestrous cycle. This can be easily

measured and, thus, accounted for in study design and statistics. However, to maintain study

power, this may require larger groups, which adds cost and time to a study. In contrast, recent

evidence has shown that female rats and mice are not more variable across a vaiety of

behavioural tasks than males even if the oestrous cycle is not accounted for (Prendergast et al.,

2014, Becker et al., 2016). Therefore, the representation of females needs to be increased in

neuroscience research to improve the translatability of the findings.

Validation of the new method compared to that of Tomlinson et al. (2014) is important in order

to establish similarities and differences in the methods. However, we still do not know the

causes of the differences between HA and LA animals. The differences in response to

pharmacological challenge suggest there may be differences in neurobiology between the two

populations. As discussed in the introduction, the Dalley et al. (2007) high impulsivity model has

been shown to differ in pharmacological response (Fernando et al., 2012), but it has also shown

differences in D2/3 receptor binding as a mechanism by which the difference is produced

(Dalley et al., 2007). Future work with the LA animal model presented in Chapter 3 could assess

whether there are differences in the expression of, or binding to, proteins important for attention.

Clinical ADHD heritability studies have highlighted some potential targets for this investigation

such as DAT and dopamine D4 receptor (Klein et al., 2017). Analysing differences in HA and LA

animals of receptors such as DAT and DRD4 would help to understand (a) the neurobiology

that underlies performance variation in rodent populations and (b) whether these mechanisms in

rodents are similar to those in human cases, therefore reinforcing the construct validity of the

model.

130

Another question highlighted in the model of Dalley et al. (2007) is whether differences between

animals are a state or a trait (Crabbe and Cunningham, 2007). That is, whether differences are

inherent to the animals (trait) or induced by environment and/or training (state). Once

biomarkers such as DAT and dopamine receptor D4 are discovered these could be traced

through genetics and epigenetics to establish if these markers are predictive of poor

performance. However, future work could also attempt to further validate the method by

establishing stability of individuals and groups and then addressing any instability with improved

training methods. One such improvement would be a subject-driven training method, as

suggested by Martin et al. (2015) for the 5C-SRTT. With this method, task difficulty is increased

or reduced based on the performance of the individual on a trial-by-trial basis. This would

include the reduction of stimulus duration and the gradual introduction of „no go‟ trials. This has

three key benefits: 1) The adaptation after incorrect responses reduces the likelihood of

disengagement; 2) based on its use in 5C-SRTT, it would be expected to reduce training time;

3) the standardisation of training will make it more reliable and results more reproducible; and 4)

training automation allows analysis of differential training speeds with much more detail and

confidence. The final point is of particular interest, as it would allow links to be researched

between learning speed and final performance.

Another important consideration when using 5 choice tasks are ceiling and floor effects. These

may occur if it becomes much more difficult or impossible to pass a certain performance point.

For example, if an animal is performing the task to 98% accuracy, then increasing performance

beyond that level is unlikely, purely due to the restraints of the task. Because of this, even were

the animal to improve in performance the task would be unable to detect it. This factor is

particularly important when looking at very high or low performing individuals. However, this

potential issue may be overcome in a number of ways: 1) Careful control of training, for

example, the subject-driven methods discussed above, as over long periods of training animals

will trend towards higher performance over time 2) Increasing the task difficulty, for example, by

reducing stimulus duration, increasing inter-trial intervals or using audible or visual distractors.

Both of these methods would shift performance away from the artificial limits of detection by the

task making assessments more reliable.

This work has mostly focused on the HA and LA animals in each cohort to assess any

differential response to pharmacological challenge. To answer the question of how attention

interacts with performance, other methods of analysis could also be used. For example,

regression analysis would allow the assessment of relationships between baseline performance

and changes from the baseline. Regression analysis would also have higher power due to use

of the full cohort rather than just 25% of the cohort for each group (HA/LA). However, it is

important to remember that the goal of this research is to be translational towards conditions of

low attention such as ADHD and schizophrenia. These conditions are extreme manifestations of

low attention, which are modelled well by the LA group. Therefore, as changes in the LA group

are of the most clinical relevance these need to be studied in isolation.

131

Chapter 6 highlighted the involvement of the frontoparietal network in the 5C-CPT. However,

the method used does not allow comparison of an animal to itself and can offer no insight into

the evolution of neuronal activity changes during task performance. This could be improved in

future work by using a different methodology. The recording of electrical activity in awake

animals performing the 5C-SRTT has recently been achieved (Donnelly et al., 2014, 2015). As

neurones transmit information as electrical potentials, measuring electrical changes is a more

direct way to analyse activity than C-Fos expression. Also, the fact the animals are awake and

performing the task allows within-subject comparison of attentive and non-attentive states. This

would be a powerful way to analyse the involvement of the frontoparietal network in the

performance of the 5C-CPT.

In Chapter 7 we showed that changes in neural electrical oscillatory activity are evoked by

methylphenidate in the frontoparietal network. However, we have not yet established any

transfer of information between these regions. This is something that will be addressed in future

work. Information transfer entropy is an analysis method designed to model the complex

dependencies of a system in a directional manner (Schreiber, 2000). As neural oscillations are

complex non-linear systems, the application of information transfer entropy is ideal to establish

if causal interactions exist between different frequencies of oscillations (Besserve et al., 2010).

By application of this method of analysis to the existing data presented in Chapter 7, the

hypothesis that methylphenidate alters the transfer of information between nodes of the

frontoparietal network can be tested. We would hypothesise that the regions would synchronise

as they do in non human primates (Buschman and Miller, 2007). However, as the directionality

of the synchronisation is dependent on if the stimuli are „top-down‟ or „bottom-up‟. Therefore, it

would be very interesting to establish how methylphenidate alters the directionality of

frontoparietal synchronisation.

The work presented in this thesis could be extended in different directions. Firstly, future studies

could compare the effect of methylphenidate to other compounds that alter attention. This could

include the selective noradrenaline transporter inhibitor atomoxetine to a dopaminergic

transporter inhibitor. In this experiment, the mechanism of generation of alpha and gamma

oscillations can be further understood in relation to attention-promoting compounds. Secondly,

the experiment could be repeated, but with animals already grouped based on performance in

the 5C-CPT to assess whether the electrical oscillations of the regions differ in the two

populations and if methylphenidate normalises any differences found as it does clinically (An et

al., 2013, Loo et al., 2004). Finally, to assess the involvement of the frontoparietal cortex in

attention, it will be essential to record its activity during performance of the 5C-CPT. A method

that could be used for this type of assessment has recently been published for the 5C-SRTT

(Donnelly et al., 2014). This would allow the analysis of changes in oscillatory state as attention

is allocated, offering tremendous insight into the involvement of the frontoparietal network.

Future studies could then assess if and how methylphenidate and other attention modulating

132

compounds alter neural oscillations in real time and if the response is different in high attentive

and low attentive animals.

8.5 Conclusion

The work presented in this thesis has refined a method for modelling low attention in rodents,

which is of relevance to many clinical conditions such as ADHD, schizophrenia and Alzheimer‟s

disease. The refinements outlined have brought it closer to the clinical version of the task by

calculating the same parameter (d′) to estimate vigilance and by using upper and lower quartiles

to produce a clearer deficit. The utility of the model has been shown by examination of the α7

nicotinic acetylcholine receptor partial agonist encenicline; results show for the first time that this

compound improves both attention and vigilance whilst reducing impulsive action in LA animals.

This work has highlighted partial agonists of the α7 receptor as potential attention-promoting

agents. This is important as there is a large body of research into this receptor for the alleviation

of cognitive symptoms in schizophrenia (Young and Geyer, 2013). Additionally, the present

study highlights important differences between the full agonist PNU-282987, which has no effect

on attention, and the partial agonist encenicline, which improves attention. It is hoped this can

help to guide the assessment of novel compounds for their ability to improve inattention during

the early stages of drug development.

The present studies on activity of the frontoparietal network have shown that it correlates

positively with 5C-CPT performance and that methylphenidate promotes a dramatic change in

the frequencies of neural oscillations across this network. The reduction of theta and increase in

alpha is consistent with the effect of methylphenidate in human EEG and so offers insight into

the localisation/mechanism of action of the effect of methylphenidate. This finding has

highlighted the importance of studying neural oscillations in relation to attention-promoting

compounds and has scope to be extended by analysis into how these regions communicate.

Finally, the application of the methods used here to study animals as they perform the 5C-CPT

would be a powerful tool to study the effects of attention-promoting agents and how they alter

communication across underlying brain regions.

133

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