Neurocircuitry controlling reward-directed behaviour in rats: Contribution of striatal sub-regions and

prelimbic cortex

PhD. Thesis

By

Christine Stubbendorff M.Sc. Department of Neuroscience, Psychology and Behaviour

University of Leicester Leicester

2016

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Neurocircuitry controlling reward-directed behaviour in rats: Contribution of striatal sub-regions and prelimbic cortex C. Stubbendorff Rodent striatum is involved in sensory-motor transformations and reward-related learning, with lesion studies suggesting functional differences between striatal subregions. Dorsomedial striatum (DMS) is associated with goal-directed behaviour; dorsolateral striatum (DLS) mediates automated stimulus-response and nucleus accumbens (NAc) is involved in reward expectation. Corticostriatal communication from prelimbic cortex (PrL) to DMS and NAc likely modulates appetitive behaviour. The studies reported here investigated how specific elements of reward-related behaviour are maintained by striatum and cortico-striatal interaction. To better understand the functional significance of DLS sensory responses we developed a novel tactile discrimination task in head-fixed rats. Initial results using this task linked DLS sensory responses to either reward-expectation or motor-initiation but could not distinguish between the two. Next, to separate reward and motor components of striatal neural responses and to examine the role of cortico-striatal interaction, we developed a novel discrimination task requiring rats to either respond or suppress responding to reward-predicting cues. Neuronal responses in DLS, DMS, NAc and PrL were recorded during the discrimination task in overtrained rats. In both striatum and PrL, neuronal responses to cue-onset did not appear to be influenced by differences in reward expectation. However, responses in NAc and DMS showed a possible contribution from motor preparatory processes. Overall, striatal and PrL responses as well as synchronisation between striatal sub-regions and between PrL and striatal sub-regions were greater in error trials (false alarms and misses) than correct response trials (hits and correct rejections). Error responses during performance of an overtrained task may signal trials in which the animal tests the consistency of the learned stimulus response contingencies and thus engage striatal networks associated with goal-directed rather than habitual behaviour. The trial type-dependent differences in synchronisation between PrL and all three striatal subregions may indicate modulation from other brain areas or interactions between different cortico-striatal-thalamic circuits.

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Acknowledgements

I would like to thank David Jones, Tony Smith, Andrew Warren and Gerald Gutteridge in the

Biomedical Joint Workshop, University of Leicester and Rob Hemmings in School of Psychology,

University of Leicester. Without their ingenuity, skill and patience the work presented here could not

have been produced. I would like to thank Rodrigo Q. Quiroga, Systems Neuroscience Group,

University of Leicester for supplying Matlab based spike sorting code, and Manuel Molano, Center

for Neuroscience and Cognitive Systems, Istituto Italiano di Technologia, Rovereto, Italy, for further

adapting this code to our tetrode recordings. I would also like to thank Tracie Payne, (Department of

Neuroscience, Oberlin College, OH, USA) for advice on the Go/NoGo discrimination task. A big thank

you to my supervisors, Todor V. Gerdjikov and Andrew M.J. Young and my fellow colleagues in the

lab; Rachel E. Rickard, Rosie Parry, Daniel Dautan and Aman Asif-Malik for daily advice and support.

Last, but not least, I would like to thank the rats; Elvis, Sherlock, Darwin, Moses, Herbie, Zorro,

Houdini, Pavlov, Freud, Jung, Han Solo, Obi-wan, Odin, Frej, Loke, Mimer, Lucifer, Nixon, Jekyll, Hyde,

Yoda, Gandalf, Lenin and Mao, for good behaviour.

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List of contents

Chapter 1: The role of striatum and cortico-striatal circuits in reward-directed behaviour ............... 8 1.1 Introduction ............................................................................................................. 8

1.1.1 The role of striatum in reward-directed behaviour ....................................... 9 1.1.2 The role of medial prefrontal cortex in behavioural control ....................... 10 1.1.3 Cortico-striatal circuits ................................................................................. 11 1.1.4 The role of striatum and mPFC in food seeking and its clinical relevance .. 13 1.1.5 Project overview .......................................................................................... 14

Chapter 2: Contribution of Dorsolateral striatum to tactile processing in the awake rat ................. 17 2.1 Introduction ........................................................................................................... 17

2.1.1 Whisker stimulation in head fixed rats ........................................................ 18 2.1.2 Aims ............................................................................................................. 19

2.2 Methods ................................................................................................................. 19 2.2.1 Animals ........................................................................................................ 19 2.2.2 Initial behavioural screening ........................................................................ 20 2.2.3 Construction of electrode microdrives ........................................................ 20 2.2.4 Surgery ......................................................................................................... 21 2.2.5 Behavioural training and testing .................................................................. 22 2.2.6 Electrophysiological recordings ................................................................... 24 2.2.7 Technical challenges with obtaining single unit recordings ........................ 25

2.3 Results .................................................................................................................... 25 2.3.1 Behaviour ..................................................................................................... 25 2.3.2 Electrophysiological recordings ................................................................... 26

2.4 Discussion ............................................................................................................... 28 2.4.1 Conclusion .................................................................................................... 30

Chapter 3: Exploring motor and reward components of striatal responses to reward-paired auditory cues .................................................................................................................... 32 3.1 Introduction ........................................................................................................... 32

3.1.1 The role of striatum in reward-directed behaviour ..................................... 32 3.1.2 Dorsomedial striatum .................................................................................. 33 3.1.3 Dorsolateral striatum ................................................................................... 34 3.1.4 Nucleus Accumbens ..................................................................................... 35 3.1.5 Interaction between striatal sub-regions .................................................... 36 3.1.6 Study aims .................................................................................................... 37 3.1.7 Hypothesis ................................................................................................... 37

3.2 Methods ................................................................................................................. 38 3.2.1 Animals ........................................................................................................ 38 3.2.2 Apparatus ..................................................................................................... 38 3.2.3 Behavioural training ..................................................................................... 38 3.2.4 Tetrode drives .............................................................................................. 41 3.2.5 Surgery ......................................................................................................... 41 3.2.6 Electrophysiological recordings ................................................................... 43 3.2.7 Verification of tetrode placement ............................................................... 43 3.2.8 Statistical analysis ........................................................................................ 45

3.3 Results .................................................................................................................... 47 3.3.1 Behaviour ..................................................................................................... 48 3.3.2 Firing rate responses .................................................................................... 48 3.3.3 Baseline firing rates ..................................................................................... 50 3.3.4 Effect of previous trial response .................................................................. 51

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3.3.5 Coherence between striatal subregions ...................................................... 53 3.3.6 Differences between tasks ........................................................................... 55

3.4 Discussion ............................................................................................................... 55 3.4.1 Behaviour ..................................................................................................... 55 3.4.2 Baseline single unit activity .......................................................................... 56 3.4.3 Single unit responses to cue onset .............................................................. 57 3.4.4 Recent behavioural experience predicts neuronal response to cue ........... 60 3.4.5 Striatal subregions collectively respond to cue onset ................................. 61 3.4.6 Coherence between striatal subregions ...................................................... 62 3.4.7 Conclusions .................................................................................................. 64

Chapter 4: Corticostriatal contribution to reward-directed behaviour ............................................. 66 4.1 Introduction ........................................................................................................... 66

4.1.1 Prelimbic cortex and behavioural control ................................................... 66 4.1.2 Prelimbic modulation of striatal processes ................................................. 67 4.1.3 Study aims .................................................................................................... 70

4.2 Methods ................................................................................................................. 70 4.2.1 Animals ........................................................................................................ 71 4.2.2 Apparatus ..................................................................................................... 71 4.2.3 Behavioural training ..................................................................................... 71 4.2.4 Surgery ......................................................................................................... 71 4.2.5 Electrophysiological recordings ................................................................... 72 4.2.6 Verification of tetrode placement ............................................................... 73 4.2.7 Statistical analysis ........................................................................................ 73

4.3 Results .................................................................................................................... 74 4.3.1 Behaviour ..................................................................................................... 74 4.3.2 Firing rate response to cue onset ................................................................ 74 4.3.3 Striatal response to cue onset ..................................................................... 75 4.3.4 Prelimbic cortex baseline firing rates .......................................................... 76 4.3.5 Prelimbic cortex response to cue onset ...................................................... 76 4.3.6 Effect of previous trial response .................................................................. 77 4.3.7 Coherence between prelimbic cortex and striatal subregions .................... 78 4.3.8 Differences between tasks ........................................................................... 80

4.4 Discussion ............................................................................................................... 80 4.4.1 Baseline single unit activity .......................................................................... 80 4.4.2 PrL single unit response to cue onset .......................................................... 81 4.4.3 Effect of previous trial response .................................................................. 82 4.4.4 Coherence between PrL and striatum ......................................................... 83 4.4.5 Conclusions .................................................................................................. 85

Chapter 5: Final Discussion ................................................................................................................ 87 5.1.1 Summary of conclusions from experimental chapters ................................ 87 5.1.2 Comparison between findings in experimental chapters ............................ 89 5.1.3 Future perspectives ..................................................................................... 91

Appendix .......................................................................................................................................... 93 References .......................................................................................................................................... 96

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List of tables

Table 3-1 Coordinates targeted for recording of single unit responses in striatal subregions. .......... 42

Table 3-2 Number of analysed neurons from each structure. ............................................................ 47

Table 4-1 Number of analysed and significantly responding units in PrL ........................................... 74

Table 4-2 Log transformed coherence between PrL and DLS, DMS and NAc after cue onset. ............ 79

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List of Figures

Figure 1-1 Projection routes in cortico-striatal-thalamic circuits. ...................................................... 11

Figure 2-1 Illustration of rat somatosensory cortex and apparatus used in the experimental setup.17

Figure 2-2 Discrimination task............................................................................................................. 24

Figure 2-3 Rats trained to discriminate between rewardable (Go) and non-rewardable (NoGo)

whisker stimulation licked more during Go vs. NoGo paired stimulation. ........................................... 26

Figure 2-4 DLS cue-evoked responses during discrimination and reversal in one animal. ................. 27

Figure 3-1 Behavioural paradigm ........................................................................................................ 39

Figure 3-2 Response ratios in the Go-NoGo Plus and Go-NoGo Minus task ....................................... 40

Figure 3-3 Tetrode based spike sorting. .............................................................................................. 41

Figure 3-4 Verification of tetrode placement. ...................................................................................... 44

Figure 3-5 Striatal neuron population respond transiently to cue onset. ............................................ 45

Figure 3-6 Example spike rasters and waveforms from neurons in DLS, DMS and NAc in both tasks. 46

Figure 3-7 Behavioural performance. ................................................................................................... 49

Figure 3-8 Log transformed firing rate responses to cue onset. .......................................................... 51

Figure 3-9 Effect of previous trial response on cue-induced firing. ...................................................... 52

Figure 3-10 Log transformed baseline coherence between striatal subregions. ................................. 54

Figure 4-1 Verification of tetrode placement ...................................................................................... 72

Figure 4-2 PrL neuron population respond transiently to cue onset.................................................... 75

Figure 4-3 Log transformed firing rate responses to cue onset in PrL. ................................................ 76

Figure 4-4 Effect of previous trial response on cue-induced firing. ...................................................... 77

Figure 4-5 Log transformed baseline coherence between PrL and DLS, DMS and NAc. ...................... 78

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List of abbreviations

DLS dorsolateral striatum

DMS dorsomedial striatum

NAc nucleus accumbens

mPFC medial prefrontal cortex

PrL prelimbic cortex

IL infralimbic cortex

CR correct rejection

FA false alarm

5-CSRTT 5 choice serial reaction time task

MSN Medium spiny neuron

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Chapter 1: The role of striatum and cortico-striatal circuits in

reward-directed behaviour

1.1 Introduction

The basal ganglia are strongly involved in action selection. This includes both its expression through

adaptive motor control and the processes that lead to movement, including the elements that drive

actions, such as emotions, motivation, and cognition (Haber, 2003, Devan et al., 2011). The basal

ganglia consist of dorsolateral striatum (DLS), dorsomedial striatum (DMS) and nucleus accumbens

(NAc), collectively known as the striatum, as well as the globus pallidus, ventral tegmental area,

substantia nigra pars reticulate and subthalamic nucleus (Devan et al., 2011, Balleine et al., 2009,

Haber, 2003). In rodents, 90–95% of neurons in dorsal striatum and NAc are GABAergic inhibitory

spiny projection neurons, commonly referred to as medium spiny neurons (MSNs), while the

remaining striatal neuronal population consists of interneurons (Gonzales and Smith, 2015). MSNs

project through the basal ganglia via two different routes: the direct pathway project from striatum

to the internal segment of the globus pallidus and the substantia nigra pars reticulate, to the

thalamus and back to cortex (Haber, 2003, Gonzales and Smith, 2015, Joel and Weiner, 2000). The

indirect pathway projects from striatum to the external segment of globus pallidus which connects

reciprocally to the subthalamic nucleus, which in turn project to the internal segment of the globus

pallidus (Haber, 2003, Gonzales and Smith, 2015, Joel and Weiner, 2000). Neurons in the direct

pathway predominantly express D1 dopamine receptors, whereas neurons in the indirect pathway

predominantly express D2 dopamine receptors (Haber, 2003, Gonzales and Smith, 2015, Joel and

Weiner, 2000). However, a number of striatal MSNs projects to both the external and internal globus

pallidus (or substantia nigra pars reticulate) and some MSNs express both D1 and D2 dopamine

receptors (Gonzales and Smith, 2015). The dopaminergic system plays an important modulatory role

in basal ganglia function. Whereas dopamine modulation of the direct pathway is thought to

facilitate movement, dopamine modulation of the indirect pathway is thought to inhibit it (Jin et al.,

2014). However, a recent study found that different subsets of direct and indirect pathway neurons

were engaged during sequence initiation, execution and termination, which suggests that the roles

of the direct and indirect pathway may not be quite so strictly divided (Jin et al., 2014). The striatal

dopaminergic systems have both tonic and phasic patterns of activity. Tonic stimulation of striatal

D2 dopamine receptors by basal dopamine levels is considered essential for normal motor and

cognitive functions of the basal ganglia, whereas sensory-evoked phasic stimulation of D1 dopamine

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receptors, as seen following appearance of reward-predicting cues, likely provide a teaching signal

for instrumental learning (Marcott et al., 2014, Redgrave et al., 2010, Grace et al., 2007).

Although the majority of striatal cells are medium spiny neurons (MSNs) (Gonzales and Smith,

2015), acetylcholine release from cholinergic interneurons within striatum is thought to modulate

dopamine transmission by acting at both muscarinic and nicotinic acetylcholine receptors (Threlfell

and Cragg, 2011). The subtypes of muscarinic and nicotinic acetylcholine receptors differ between

dorsal striatum and NAc, thus enabling cholinergic interneurons to modulate dopamine transmission

differently in specific striatal sub-regions (Threlfell and Cragg, 2011), which in turn may underlie

differences between striatal sub-regions in their contribution to behaviour (Aoki et al., 2015).

1.1.1 The role of striatum in reward-directed behaviour

The striatum is the main input structure to the basal ganglia and is associated with cognitive and

motivational processing (Haber, 2003) as well as with the execution of motor response (Haber, 2003,

Costa et al., 2004, Pisa and Schranz, 1988) and is considered a key brain region for the regulation of

stimulus-driven behaviour (Yin et al., 2008, Balleine, 2005, 2007). Lesion studies suggest that DLS

(homologue to putamen in humans), DMS (homologue to caudate nucleus in humans) and NAc are

functionally segregated (Balleine et al., 2009, Redgrave et al., 2011). Whereas DMS is considered to

be responsible for acquisition in the early stages of learning and in updating of stimulus-response-

outcome contingencies (Devan et al., 2011, Yin et al., 2005), DLS is primarily associated with

automated stimulus-response behaviour (Yin et al., 2006) and NAc is thought to mainly integrate

motivational aspects of learning (Haber, 2003, Liljeholm and O'Doherty, 2012). Lesions of the DMS in

rats reduce sensitivity to changes in action-outcome contingency as well as post-training outcome

devaluation, suggesting that DMS contributes to behavioural flexibility (Devan et al., 2011) and plays

a key role in the initial phase of goal-directed learning, encoding the association between action and

its specific consequence (Yin et al., 2005). As a task is learned and becomes habitual, responding

becomes dependent on the DLS (Balleine et al., 2009, Tang et al., 2009). When rats are over-trained

on a lever pressing task they become insensitive to changes in outcome value, that is, they continue

pressing the lever even when the reward is devalued (Yin et al., 2006). Several studies suggest DLS

plays a crucial role in the fine tuning of precise motor responses which, through repeated training

and pairings of stimulus-outcome associations, optimise the rat’s motor response toward achieving a

desired outcome (Featherstone and McDonald, 2004, 2005, Balleine et al., 2009, Tricomi and

Lempert, 2015, Pisa and Schranz, 1988, Devan et al., 2011). Rats with lesions in NAc consistently

show a reduction in the vigour of performance during the acquisition of instrumental learning.

However, they remain sensitive to changes in the instrumental contingency (Hart et al., 2014). This

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suggests that NAc’s involvement in instrumental learning is specific to the modulation of response

vigour or affective arousal. In rats, trained to respond to an auditory cue for reward, neurons in NAc

responded during subsequent exploration of the reward receptacle regardless of whether the

reward was delivered or withheld, whereas uncued entries to the reward receptacle, which were

never rewarded, did not produce excitation in NAc neurons (Nicola et al., 2004b). This finding

demonstrates how NAc reward response can be triggered not just by the actual delivery of reward

but also by conditioned stimuli associated with the reward (Nicola et al., 2004b).

1.1.2 The role of medial prefrontal cortex in behavioural control

Medial Prefrontal cortex (mPFC) plays a crucial role in the organisation of previously acquired

information and in subsequent integration of this information into the planning and execution of

complex behaviour (Groenewegen and Uylings, 2000, Dalley et al., 2004). MPFC is thought to exert

an influence on appetitive behaviour (Riga et al., 2014) via top down control of downstream areas in

nucleus accumbens (NAc) (Riga et al., 2014, Balleine et al., 2009, Christakou et al., 2004, Stefanik et

al., 2015) and medial parts of dorsal striatum (Christakou et al., 2001, Baker and Ragozzino, 2014,

Thorn and Graybiel, 2014). Whereas infralimbic cortex (IL), in ventral mPFC, is associated with habit

formation (Maier, 2015, Smith and Graybiel, 2013), prelimbic cortex (PrL), in dorsal mPFC, is involved

in goal-directed behaviour and complex behaviour that requires flexible switching between different

context-dependent strategies (Riga et al., 2014, Heidbreder and Groenewegen, 2003, Funamizu et

al., 2015).

PrL and IL afferents project mainly from perirhinal, agranular insular and the piriform

cortices, hippocampus and the medial basal forebrain, whereas limbic subcortical information

mainly reaches the PrL and IL via the midline thalamus and the basal nuclei of the amygdala (Hoover

and Vertes, 2007, Vertes et al., 2012, Mattinson et al., 2011, Heidbreder and Groenewegen, 2003).

Furthermore, mPFC is reciprocally connected to the basolateral amygdala (Little and Carter, 2013).

The cell population in mPFC comprises primarily pyramidal neurons, which are excited by glutamate,

cholinergic interneurons as well as inhibitory GABAergic interneurons (Steketee, 2003). Within the

mPFC, dopamine release inhibit pyramidal neurons and stimulates GABA release from GABA

interneurons, which in turn further inhibit pyramidal neurons (Steketee, 2003). PrL projects mainly

to NAc core whereas the NAc shell receives mPFC afferents from IL (Ding et al., 2001, Hart et al.,

2014, Gabbott et al., 2005, Balleine et al., 2009, Groenewegen et al., 1999, Balleine et al., 2007,

Heidbreder and Groenewegen, 2003) as part of the limbic cortico-striatal-thalamic circuit and to

dorsomedial striatum (DMS) as part of the associative cortico-striatal-thalamic circuit (Gabbott et al.,

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2005, Groenewegen et al., 1999, Hart et al., 2014, Balleine et al., 2007, Balleine and O'Doherty,

2010, Heidbreder and Groenewegen, 2003).

1.1.3 Cortico-striatal circuits

The functional segregation in striatum is further maintained by spatially segregated cortico-striatal-

thalamic circuits, in which DMS, DLS and NAc receive projections from different cortical regions

(Figure 1-1B) and in turn project topographically through the other parts of basal ganglia, to

thalamus, and back to cortex (Redgrave et al., 2011, Haber, 2003, Alexander et al., 1986) (Figure 1-

1A). DMS is part of the associative cortico-striatal-thalamic circuit and receives projections from

medial prelimbic (PrL), cingulate and motor cortex (Van Waes et al., 2012, Balleine et al., 2009). The

associative cortico-striatal-thalamic circuit in involved in acquisition of stimulus-response-outcome

contingencies and behavioural flexibility (Balleine et al., 2007, 2009). In a conditional discrimination

task, contralateral inactivation of PrL and DMS in rats impaired performance in trials when rats had

to change their behaviour to obtain a reward, whereas performance within trial blocks, where no

switching was required, was unaffected (Baker and Ragozzino, 2014). This suggests that

communication between PrL and DMS, as part of the associative cortico-striatal-thalamic circuit,

modulate cue-guided behavioural shifting during tasks that require discrimination between sets of

different stimulus-outcomes.

Figure 1-1 Projection routes in cortico-striatal-thalamic circuits. A. Illustration of cortico-triatal-thalamic circuits, conveying limbic (shown in red), associative (shown in yellow–green) and sensorimotor (shown in blue–white) information (Schematic from Redgrave et al., 2011). B. Cortical input to striatal sub-regions and affiliation of striatal subregions to sensory-motor, associative and limbic cortico-striatal-thalamic circuits. Abbreviations: IL, Infralimbic; PL, Prelimbic; CG, cinculate; M1, motor; M2, medial agranular; SS, somatosensory; I, insular; LO, lateral orbital (schematic from van Waes et al., 2012).

DLS is part of the sensory-motor cortico-striatal-thalamic circuit and receives projections

from primary motor and sensory cortex (Van Waes et al., 2012, Redgrave et al., 2011). The sensory-

motor circuit is involved in automated stimulus-response behaviour (Balleine et al., 2007). In rats,

sensory input to sensory cortex in the form of whisker stimulation led to increased neural activity in

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DLS (Mowery et al., 2011) and coherence between local field potentials in motor cortex and dorsal

striatum has been found to increase as rats learn an operant task (Koralek et al., 2013), which is

consistent with sensory-motor cortico-striatal-thalamic circuit’s role in over-trained habitual

responding.

NAc is part of the limbic cortico-striatal-thalamic circuit and receives projections from PrL

and IL cortex (Balleine, 2005, Hart et al., 2014, Van Waes et al., 2012). The limbic cortico-striatal-

thalamic circuit maintains motivational aspects of reward-seeking behaviour (Yin et al., 2008,

Balleine, 2005) and disruption of mPFC-NAc core connectivity, through contralateral lesions of mPFC

and NAc core, have been shown to interfere with the planning of responding to reward-paired cues,

but only in trials following immediately after a trial with a rewarded correct response (Christakou et

al., 2004). Thus, the interaction between cortex and NAc is likely to be involved in the updating of

response-outcome contingencies and may be particularly sensitive to recent reinforcement.

Many studies suggest consistent functional differences between striatal sub-regions (Devan

et al., 2011, Balleine et al., 2009). However, adaptive behaviour in a natural environment requires

the ability to associate multiple cues with a variety of possible outcomes and subsequent

implementation of an appropriate behavioural response. Successful behaviour necessitates

integration of reward processing, associative learning and motor planning and thus interaction

between brain regions maintaining these processes (Haber and Knutson, 2010, Joel and Weiner,

2000). Within the striatum, axons and dendrites within each sub-region often cross into other

striatal sub-regions (Haber, 2003). This inter-striatal connectedness, in conjunction with the

striatum’s position in the limbic, associative and somatosensory cortico-striatal-thalamic circuits,

makes the striatum a likely candidate site for interaction and coordination between these circuits,

and organisation of adaptive behavioural output (Liljeholm and O'Doherty, 2012).

As in the striatum, the associative, sensory-motor and the limbic circuits all project to and

from sub-regions of the thalamus (Haber and Calzavara, 2009) and the thalamus may play an

important role in modulation of the joint output of these circuits, with distinct groups of thalamic

nuclei likely contributing to different aspects of sensory, motor, and cognitive processing (Haber and

Calzavara, 2009). Traditionally the thalamus has been regarded primarily as a passive relay station

for sensory and motor signals (Fama and Sullivan, 2015). However, the thalamus is now considered

to also contribute to cognitive processes, including attention, speed of information processing, and

memory (Fama and Sullivan, 2015) and gamma coherence between LFPs in mPFC and mediodorsal

thalamus in relation to reward delivery has been found to increase in rats as a result of instrumental

learning (Yu et al., 2012). The convergence of cortico-striatal-thalamic circuits in sub-regions of the

thalamus makes this structure another possible hub for interaction and coordination between

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cortico-striatal-thalamic circuits. However, the focus in this thesis will be limited to the role of

striatal subregions and their interaction with prelimbic cortex.

One hypothesis for the role of these integrated cortico-striatal projections is that they

contribute to the regulation of stimulus-driven responses (Mowery et al., 2011, Pidoux et al., 2011)

and may work as a relay station modulated by the salience of the input stimulus, so that the most

salient input is selectively disinhibited. This disinhibition of the input signal may in turn permit

salient sensory signals to dominate the input to targeted motor systems while tonic inhibition of

input from less salient signals is maintained, thereby facilitating the appropriate behavioural

response (Redgrave et al., 2010, 2011, Pidoux et al., 2011). Characterizing the dynamic modulation

of behaviour imposed by the salience of sensory input in behaving animals will be a key step to

understanding the normal function of cortico-striatal projections.

1.1.4 The role of striatum and mPFC in food seeking and its clinical relevance

The use of “natural” rewards such as water or food in an experimental setting allows us to study

neuronal responses to reward directed behaviour in healthy animals, thereby providing vital insight

into how core regions of the brain are affected by reward experience. The presentation of food or

food associated stimuli evokes dopamine release and neuronal activity both in striatum and mPFC

(Carrelli, 2002, Hadad and Knackstedt, 2014, McCutcheon, 2015). The dopamine response to feeding

experience differs between brain areas, possibly underlying the different roles these areas

undertake in the modulation of food seeking and consumption. One study measuring extracellular

dopamine levels using micro-dialysis in rats found that introduction to novel food increases

dopamine release in both mPFC and NAc, whereas repeated exposed to the same food, or to objects

associated with this food, only led to increased dopamine release in mPFC but not in NAc (Bassareo

and Di Chiara, 1997). Another study using fast scan cyclic voltammetry to measure dopamine release

in DMS, DLS and NAc core and shell in rats found that unexpected reward only evoked DA release in

NAc core but not in any other striatal subregion (Brown et al., 2011). Furthermore, reward predictive

cues only evoked dopamine release in NAc core and in DMS but not in NAc shell or DLS (Brown et al.,

2011). In rats trained to associate flavoured saccharin solutions with intragastric infusion of either

sugar or water, injection of D1-like receptor antagonists in NAc, mPFC, the amygdala or lateral

hypothalamus during training, decreased preference for the sugar-associated flavour, further

suggesting that flavour preference is maintained by a network of brain regions (Sclafani et al., 2011).

Obesity, caused by inappropriate feeding habits, is a growing health problem across the

world (Lutsiv et al., 2015, Naef et al., 2015) and has been associated with altered dopamine function

in striatum (Naef et al., 2015), as well as changes in neurotransmitter function in mPFC and thalamus

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(Blasio et al., 2014, Cole et al., 2015). However, research studying NAc function in relation to feeding

has shown that dopamine release within NAc as well as neuronal firing respond to both “natural”

rewards like food and water and to cocaine self-administration (Carrelli, 2002, Hadad and

Knackstedt, 2014) and dysfunction in neural circuitry involved in food seeking has been associated

with addictive behaviour. Disruption of NAc function in rats produces shift in effort-related choice

behaviour towards decreases willingness to work for food (Nunes et al., 2013) but has also been

found to reduce response inhibition and increase impulsive choice (Feja et al., 2014, Pothuizen et al.,

2005). Dysfunction in the neural processes involved in habituation and reward related learning has

been implicated in several psychiatric disorders related to motivation and attention. Drug addiction

can be defined as a maladaptive compulsive habit and chronic use of cocaine or methamphetamine

has been shown to lead to reorganisation of the dorsal striatum (Belin et al., 2009), (Belin et al.,

2009, Willuhn et al., 2012) and PFC (Hearing et al., 2012). Individuals with antisocial personality

disorder have been shown to have increased volume in the putamen compared with control subjects

and studies suggest that the dorsal striatum in antisocial individuals do not process absence of

reward appropriately, causing it to continuously respond to a stimulus after it has ceased to be

rewarding (Glenn and Yang, 2012). In patients with schizophrenia dopamine transmission in the

striatum is increased during psychotic state and this increased activity is correlated with positive

symptoms such as hallucinations and delusion (Sorg et al., 2012, Goda et al., 2015). Dysregulation of

(mPFC) glutamatergic and cholinergic circuitry has been implicated in disorders such as

schizophrenia, depression and addiction (Mattinson et al., 2011) and Bulimia Nervosa (Hadad and

Knackstedt, 2014). By studying how the brain processes sensory inputs and translates them into a

learned behavioural response, we not only gain a better understanding of processes governing our

everyday behaviour but may also provide important clues to the development of these detrimental

illnesses.

1.1.5 Project overview

Together the subunits of striatum maintain a range of functions crucial for assessing stimulus-

outcome contingencies and optimising the individual’s responses to these cues (Balleine et al., 2009,

Liljeholm and O'Doherty, 2012). Optimising responses to cues requires the retention of learned

stimulus-outcome contingencies, as well as planning and execution of reward-directed motor

responses. Region-specific lesion studies suggest that motivational, motor and cognitive components

of reward-directed behaviour are represented differently in each striatal sub-region (Balleine et al.,

2009, Devan et al., 2011, Hart et al., 2014, Yin et al., 2005, 2006). These lesion studies provide clues

on whether striatal sub-regions are necessary for specific components of reward-related behaviour.

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However, examining the activity of the three main subareas simultaneously in the non-lesioned

brain allows comparisons between structures within animal and trial as well as an assessment of

how network activity between the sub-regions relates to behavioural choice.

Cortico-striatal communication from medial prefrontal cortex (mPFC) to NAc and DMS likely

play a role in appetitive behaviour, particularly when tasks are demanding and involve shifts

between several stimulus-response-outcome contingencies (Baker and Ragozzino, 2014, Funamizu

et al., 2015, Riga et al., 2014, Heidbreder and Groenewegen, 2003). Examining the activity in mPFC

and striatal sub-regions simultaneously during complex behavioural tasks allows evaluation of the

contribution of cortico-striatal communication to the modulation of behaviour.

Characterizing the dynamic modulation of behaviour imposed by the reward expectation as

well as motor preparation in behaving animals will be a key step to understanding the normal

function of cortico-striatal projections. However, in most standard behavioural paradigms, cues

signalling reward availability also signal to the animal to make a motor response, thereby making

standard behavioural paradigms unable to separate motor and reward component of neural

responses to reward-paired sensory stimuli. However, by developing a novel behavioural paradigm,

in which contribution of these two components can be separated, a greater understanding of striatal

and cortico-striatal modulation of reward-directed behaviour can be achieved.

Overview of aims in experimental chapters

The study reported in Chapter 2 aimed to assess whether the level of salience of sensory input to

DLS affects the sensory representation in structure. To this end, a novel tactile discrimination task

was implemented in head fixed rats, in which sensory stimulation to one whisker is associated with a

reward whereas stimulation of another whisker is associated with reward omission. In animals over-

trained on the discrimination task, DLS evoked tactile responses were expected to be stronger in

response to stimulation of the reward-paired whisker compared with stimulation of the whisker

paired with reward omission.

The study reported in chapter 3 examined the contribution of single unit activity obtained in DMS,

DLS and NAc simultaneously in rats during execution of two comparable conditioned discrimination

tasks; a standard Go-NoGo task (Go-NoGo Minus) and a novel Go-NoGo task (Go-NoGo Plus).

Importantly, in the Go-NoGo Minus task, reward expectancy was exclusively linked to motor

initiation but not with motor suppression, whereas in the Go-NoGo Plus task, reward expectancy

was coupled with either motor initiation or motor suppression in different trials within the same

session. Through comparison of the single unit responses to cue onset in these two tasks, this study

aimed to examine the role of individual striatal sub-regions, as well as communication between sub-

16

regions, on reward expectancy and preparation of motor response during conditioned

discrimination. Striatal sub-regions associated with motor preparation, such as DLS and to lesser

extend NAc, were expected to produce a stronger response to cues signalling motor initiation

compared with DMS, whereas sub-regions modulated by reward expectancy, most notably NAc,

were expected to produce a stronger response to cues signalling the opportunity to obtain a reward

compared with non-rewarded trials.

The study presented in chapter 4 examined the contribution of PrL single unit activity and

synchronisation between PrL and DMS, DLS and NAc during execution of the same two Go-NoGo

behavioural paradigms presented in chapter 3. Because mPFC projects directly to DMS and NAc but

not to DLS, greater task-related synchronisation was expected between PrL and DMS and PrL and

NAc compared with synchronisation between PrL and DLS in response to trial onset cues.

17

Chapter 2: Contribution of Dorsolateral striatum to tactile

processing in the awake rat

2.1 Introduction

Dorsolateral striatum (DLS) is involved in the learning and execution of automatic stimulus-driven

behaviour (Pidoux et al., 2011) and DLS has been implicated in tactile representations (Hawking and

Gerdjikov, 2013, Mowery et al., 2011) and automatic stimulus-response behaviours (Yin et al., 2006).

DLS lesioned rats have difficulty learning tasks that involve precise motor movement whereas

general movement was left unimpaired (Devan et al., 2011) and several studies suggest DLS play a

crucial role in the fine tuning of precise motor responses which, through repeated training and

pairings of stimulus-outcome associations, optimises the rats motor-dependent behaviour toward

achieving a desired outcome (Balleine et al., 2009, Featherstone and McDonald, 2004, Featherstone

and McDonald, 2005, Tricomi and Lempert, 2015, Pisa and Schranz, 1988, Devan et al., 2011). In rats

trained to nose poke in response to an auditory cue signalling reward availability, neurons that

responded to movement showed increased firing when movement was paired with reward than

when it was unrewarded (Kimchi et al., 2009), suggesting that reward expectation also contribute to

DLS evoked responses.

Figure 2-1 Illustration of rat somatosensory cortex and apparatus used in the experimental setup. A. Arrangement of the cortical columns (barrels) in the left somatosensory cortex of a rat. Whiskers in the D row are shown full length with their corresponding barrels highlighted in the cortical map (schematic from Diamond & Arabzadeh,. 2013). B. Example of a microdrive, proportions are identical to the drives used in the current study (Haiss et al., 2010). C. Rats were trained to run through a fixation tunnel and to be fixed by the headpost to eliminate head movement during electrophysiological recordings.

DLS receives strong projections form primary somatosensory cortex (Mowery et al., 2011,

Alloway et al., 1999, Hoffer et al., 2005). In rats and mice, individual whiskers are represented in

18

somatosensory cortex in segregated cortical columns in a map-like fashion (the barrel cortex)

(Miyashita and Feldman, 2013, Diamond and Arabzadeh, 2013) Figure 2-1A), - an arrangement

unparalleled in other systems. Stimulation of neurons in barrel cortex can be linked directly to

activity in the DLS (Mowery et al., 2011, Hawking and Gerdjikov, 2013). Repeated whisker

stimulation evokes neuronal responses in DLS in anaesthetised rats which is consistent with the

extensive projections from somatosensory cortex to the DLS (Syed et al., 2011, Mowery et al., 2011).

Anterograde labelling of projections from individual cortical columns in barrel cortex to DLS have

shown significantly greater overlap between projections into DLS from cortical columns representing

whiskers positioned within the same horizontal row on the rat’s head compared with projections

from cortical columns representing whiskers in different rows (Alloway et al., 1999). These

corticostriatal projections likely affect striatum-mediated regulation of sensory stimulus-driven

responses (Mowery et al., 2011, Pidoux et al., 2011) and may work as a relay station modulated by

the salience of the input stimulus, so that the most salient input is selectively disinhibited. This

disinhibition of the input signal may in turn permit salient sensory signals to dominate the input to

targeted motor systems, thereby facilitating the appropriate behavioural response (Redgrave et al.,

2010, 2011, Pidoux et al., 2011). Several studies suggest DLS play a crucial role in the fine tuning of

precise motor responses which, through repeated training and pairings of stimulus-outcome

associations, optimised the rat’s motor response toward achieving a desired outcome (Featherstone

and McDonald, 2004, 2005, Balleine et al., 2009, Tricomi and Lempert, 2015, Pisa and Schranz, 1988,

Devan et al., 2011). While some studies have found DLS neuronal activity during reward-directed

behaviour to mainly respond to motor aspects of the task (Tang et al., 2007, 2009) other findings

suggest that differences in value associated with the behavioural responses may also modulate firing

(Samejima et al., 2005, Kimchi et al., 2009).

Characterizing the dynamic modulation of behaviour imposed by the salience of sensory input

in behaving animals will be a key step to understanding the normal function of corticostriatal

projections. However, this “salience model” of striatal regulation has not been tested

experimentally.

2.1.1 Whisker stimulation in head fixed rats

Head fixation in awake rodents it a well-established preparation in which a rat is trained to allow

fixation of its head, successfully prohibiting head movement, thus enabling precise stimulation

(deflection) of individual whiskers (Schwarz et al., 2010). Within this setup, the head fixed rat can be

trained to produce a simple instrumental behaviour, such as lever pressing or licking, in response to

single whisker stimulation (Stuttgen and Schwarz, 2010). The projections from barrel cortex to DLS

19

(Alloway et al., 1999, Mowery et al., 2011) are well in accord with the role of that part of striatum in

stimulus-response learning (Balleine et al., 2007) and with findings that the whisker system supports

reward-related learning (Schwarz et al., 2010). As a model, whisker stimulation in the head fixed rat

is uniquely suited to assessing response properties in discrete corticostriatal projections. The head

fixed preparation enables recording of tactile evoked neuronal responses in awake animals without

the contamination of movement artefacts which can complicate analysis of recordings obtained

from freely moving animals (Schwarz et al., 2010). Behaviourally, the system allows the concurrent

conditioning of discrete barrel cortical columns by stimulating individual whiskers in different rows

and stimulation of one whisker may be paired with reward and stimulation of a second whisker with

reward omission. Thus discrete sensory cortical columns will receive identical sensory information

but each corticostriatal channel will have fundamentally different motivational significance. It is

therefore ideally suited to determining whether presentation of a stimulus engaging a given column

biases cortical input to the striatum towards that column.

2.1.2 Aims

By implementing a novel tactile discrimination task, in which sensory stimulation to one whisker is

associated with a reward whereas stimulation of another whisker is associated with reward

omission, this study aims to assess whether the level of salience of sensory input to DLS affects the

sensory representation in this structure.

Hypothesis

In animals overtrained on the discrimination task, DLS evoked tactile responses are expected

to be stronger in response to stimulation of the reward-paired whisker compared with

stimulation of the whisker paired with reward omission.

2.2 Methods

2.2.1 Animals

6 male Sprague-Dawley rats were purchased from Charles Rivers at bodyweight 250-300g. All

animals were kept on reversed light/dark cycle (12:12h; lights on 7.00h). Animals had access to food

(LabDiet 5LF5, PMI Nutrition Intl, Brentwood, MO) ad libitum and animal welfare was monitored

daily. On training and test days water was removed 14-17h before the first training/test session on

the following day. All experiments were carried out under institutional ethical approval and with

project and personal licence approved by the UK Home Office.

20

2.2.2 Initial behavioural screening

Upon arrival animals were housed in pairs and left undisturbed for 4 days. After this they were

handled and given rodent treats (Pitti Heimtierprodukte GmbH, Willich, Germany) daily. A week

after arrival the animals were introduced to a custom made black acrylic fixation box that

simulates a tunnel (height 11cm, depth 21 cm, width at back end 7cm, width at front end 5cm,

Biomedical Workshop, University of Leicester) (Figure 2.1C), secured to the floor of sound-

attenuated aluminium-plated chamber, which also served as a Faraday cage during

electrophysiological recordings. The rats were trained to run through the tunnel, first for treats and

once this had been learned, for water administered through a 1ml syringe. These early habituation

sessions served as a screening process to remove anxious and untrainable animals from the

experiment. In addition to monitoring the animal’s behaviour a bat detector was used during

sessions to ascertain if the animal was emitting 22kHz ultrasonic distress calls (Litvin et al., 2007).

22kHz ultrasonic vocalisation was used to adjust the training to the pace of the individual rat – if a

rat emitted distress calls, the training session was terminated and training started at an earlier

training stage in the next session.

During this process one rat was deemed unsuitable for further testing due to an unusually high

level of anxiety both in and outside the test box and was subsequently removed from the

experiment. Animals that passed the initial behavioural screening were implanted with head posts

and microelectrodes before commencing their training (see “Surgery” for details).

2.2.3 Construction of electrode microdrives

Electrodes: Electrodes were constructed from quartz glass insulated tungsten wire with an outer

diameter of 80μm and a metal core diameter of 20μm (Thomas Recording, Giessen, Germany). The

electrodes were pulled in a vertical puller equipped with a heater element inside an argon filled

chamber and further ground on a rotating grinding disk (Narishige Co Ltd, Tokyo, Japan) to generate

fine tipped electrodes with an impedance of 1.5-3mΩ. The quartz glass on the back end of the

electrode was cracked to expose the tungsten core which was then attached to a Teflon insulated

silver wire (diameter of silver core 125µm, Science Products GmbH, Hofheim, Germany) either

through soldering or with silver epoxy glue (ITW Chemtronics, Kennesaw, Georgia USA). The exposed

joint was insulated and strengthened with nail varnish and dental cement.

Microdrives: Custom microdrives were constructed to enable vertical movement of the

electrodes after implantation, as described in Haiss et al. (2010) (Figure 2.1B). The thread at the tip

of a stainless M1.2 steel screw was removed and a circular groove was inserted into the smooth part

(Biomedical Workshop, University of Leicester). The screw was then placed between two stainless

21

steel guiding rods (1 mm diameter, 5 mm length; Cooper’s Needleworks Ltd., Birmingham, UK)

and the rods were secured to the screw at either end with light curing dental cement (Henry Schein

Inc, Melville, NY USA). This construction enables the top block (rider) of dental cement to be moved

up or down along the screw, whereas the blunted screw tip only allows sideways rotation, thereby

stopping the screw from penetrating through the bottom dental cement block (anchoring block).

Three microelectrodes were each threaded through a polyimide tubing guide (outer diameter

0.163mm, inner diameter 0.125mm, Cole-Palmer, Vernon Hills, Illinois USA) which were aligned and

secured using epoxy glue (Evo-stik Epoxy Express, Bostik Ltd, Strafford, UK). The epoxy secured

tubings were then attached vertically to the anchoring block. The back part of the electrode was

cemented onto the rider block. Before implantation the electrode tips were cleaned with ethanol.

2.2.4 Surgery

Rats were anaesthetised with 4% v/v isofluorane (Schering-Plough) in O2, and maintained between

2-3%. An intramuscular injection of Glucopyrronium Bromide (40μl/kg bodyweight) was given to

slow down gastrointestinal mucus secretion. A sc injection of Baytril (0.2ml/kg bodyweight) was

given at the beginning of surgery. The animal’s rectal temperature was controlled automatically by a

feedback circuit composed of a rectal probe and a heating pad (Harvard Apparatus, Boston,

Massachusetts, USA) set to 37 oC. During surgery the animal received glucose/saline sc infusion

(3ml/hour) and Lacri-Lube Eye Ointment (Allergan, Wesport, Ireland) was applied to the eyes to

prevent corneal desiccation. The animal was placed in a stereotactic frame and an incision was made

along the midline and the periosteum was retracted to expose the skull. 12% hydrogen-peroxide

solution (Vet Way Ltd; York, UK) was applied to the exposed skull to enable identification of bregma

and lambda. The skull was then treated with light curing etching gel (Henry Schein Inc, Melville, NY

USA) to improve bonding of dental cement to the skull. 11 stainless steel anchoring screws (Morris

Co., Southbridge, Massachusetts, USA, part number 0X 1/8 flat)) were affixed to the cranium (3

screws to the frontal plate, 4 screws to the side of and 3 screws to the top of the parietal plate and 1

screws to the interparietal plate) to enable secure placement of the implant which was built up using

light-curing dental cement (Henry Schein Inc, Melville, NY USA). A silver wire (Science Products

GmbH, Hofheim, Germany) was connected with silver paint to one skull screw in the interparietal

plate and one skull screw in the frontal plate, to ground the animal during the electrophysiological

recordings. A craniotomy was made on the right side of the cranium and the dura was removed

immediately before insertion of 3 recording electrodes into the DLS; AP from bregma: -1.4mm,

ML/DV from bregma: +3.2mm/-3.4mm, +4.0mm/-3.7mm and +4.6mm/-5.0mm (Paxinos and

Watson, 2007). The exposed dura and the cavity around the inserted electrodes were covered with

22

antibacterial ointment (Fuciderm). The anchoring block on the microdrive was secured to the skull

cap with dental cement and a custom made aluminium tower (outer diameter 8mm, inner diameter

6.5mm, Biomedical Workshop, University of Leicester) was placed around the Microdrive to protect

it from grooming. The top of the tower was closed with a custom made screw-cap (Biomedical

Workshop, University of Leicester) to protect the drive from dirt, yet still provide access to the drive.

The grounding wires and the silver wires connected to the recording electrodes were soldered to the

male side of a micro plug microplug (Mill-Max Mfg. Corp, Oyster Bay, NY, USA), which was secured

to the skull cap with dental cement. A head post in the form of a custom made aluminium

(Biomedical Workshop, University of Leicester) was secured to the back part of the skull cap with

dental composite (Henry Schein Inc, Melville, NY USA). The skin and muscle layer around the

exposed skull and skull cap were cleaned with Povidone-Iodine (Animalcwere Ltd, York, UK) and a

layer of Fusiderm (Dechra Veterinary Products A/S, Uldum, Denmark) and the incision at the front

and back of the skull cap was sutured together. Analgesia was administered 2-3 hours before the

end of surgery (Carprieve, 0.1ml/kg bodyweight, s.c.; Norbrook Laboratories, Carlisle, UK). The

animal was removed from the stereotactic frame. However, heating and oxygen were provided until

it recovered from the anaesthesia after which it was returned to its home cage.

The animal was giving analgesia (Carpofen, 0.1ml/kg bodyweight per day) for 3 days and

antibiotics (Baytril, 0.2ml/kg bodyweight per day) for 5 days post op. In addition animals were given

intraperitoneal injections of glucose/saline solution for 3-5 days until the animal was eating dry diet

and gaining bodyweight. Training commenced minimum 7 days after surgery once the animal had

regained its pre-operation body weight.

2.2.5 Behavioural training and testing

Habituation to head fixation apparatus: The rats were reintroduced to the sound attenuated

chamber and to the fixation tunnel and trained to run through the tunnel for water administered

through a 1ml syringe. The rats were habituated to being fixed by the head post upon exiting the

tunnel in incremental stages: Initially the head post was gently post touched by the experimenter

while the rat received water upon exiting the tunnel, incrementally the gentle touch was replaced

with a firmer hold of the head post by the experimenter, briefly limiting head movement and finally

the head post was secured into a custom made bracket (Biomedical Workshop, University of

Leicester) (Figure 2.1C), completely preventing movement of the head. Initially, this head fixation

lasted less than a second while the rat was given water continuously. As the training progressed, the

duration of the head fixation was slowly increased and single droplets of water, with an increasing

waiting period between droplets, replaced the delivery of continuous water during head fixation.

23

Once the rats were habituated to head fixation, the licking spout was introduced. The licking spout

was constructed from a plastic pipette with a steel end, to prevent the plastic from being chewed.

Licks were detected as deflections of the spout recorded by a miniature piezo film sensor (Part nr.

FS-2513P; Farnell, Leeds, UK) glued to the underside of the plastic pipette. The spout was connected

to a container filled with 50ml water attached to the side of the attenuated sound chamber

approximately 20cm above the spout and the amount of water released was controlled by the

opening time of a magnetic valve (Takasago Electric Inc, Nagoya, Japan; WTA-2R-N3F). In the first

training session with the spout, the rats were allowed to explore and drink freely from the spout. In

the following training session, and after, the rat was head fixed as described above and water

rewards were delivered thought the licking spout, with the tip of the spout positioned 3-5 mm in

front of the rat’s lower lip.

Whisker stimulation: The whisker stimulators were constructed from a glass capillary glued

to a piezo actuator (Physik Instrumente, Karlsruhe, Germany). Whiskers on the right side of the rat’s

head were trimmed and maintained at a length of 1cm for the duration of the project. The whisker

stimulator was advanced slowly toward the right side of the rat’s head and the rat was habituated to

the vibration of the stimulator touching its whiskers before threading of the whiskers were

attempted. Once the rat was habituated to the stimulator, a whisker was threaded into the capillary

of the stimulator and the rat continued onto Pavlovian conditioning.

Pavlovian conditioning: Animals were trained to associate single whisker stimulation with

liquid reward delivered from the spout positioned immediately in front of the rat. Each stimulation

was delivered at 60hz and with an amplitude (whisker deflection) of 0.3mm and was always

presented for 1.5 seconds. The stimulation of a single whisker signalled a 1.5 sec response interval

during which licking the spout was rewarded. At the beginning of the Pavlovian conditioning training

the rat was rewarded with a drop of water even if it did not lick during the response interval (the

reward was provided at the end of the response interval) to strengthen the association between the

whisker-stimulation and the reward. Once the rat had learned the association between licking and

obtaining the water reward, only licking within the response interval triggered a reward. Random

licking outside the response interval was discouraged by introducing a dead time before each

stimulus. This entailed delaying the next stimulus by 5-20 seconds if the rat licks outside the

response interval.

Discrimination task: Animals were trained to discriminate between stimulation of two

different whiskers, where licking during stimulation of one whisker (Go) triggered the water reward,

whereas licking during stimulation to the other whisker (NoGo) triggered a LED light (Maplin

Electronics, Wombwell, UK) consisting of 9 LEDs, which served as negative feedback on false

24

alarms (Figure 2-2). In the early training sessions the amplitude of the Go stimulus was high (0.5mm)

compared with the NoGo stimulus (0.1mm). The two target whiskers differed between rats but were

always the same for each rat. During the session the difference in amplitude between the Go and

NoGo stimulation was decreased in small increments until the amplitude reaches 0.3mm for both Go

and NoGo. Once the rat learned to discriminate between stimulation of the two whiskers, sessions

were started with a smaller difference in amplitude (Go = 0.4; NoGo = 0.2) to indicate which whisker

signals reward after which the session was continued with the same amplitude of Go and NoGo. The

rats were considered to be discriminating at criterion when Hit rate (no. correct responses to Go

cue/total no. Go trials) was above 0.75 and False Alarm rate (no. incorrect responses to NoGo

cue/total no. NoGo trials) was below 0.25 within a session for 3 consecutive sessions.

Reversal task: Only one rat continued through to reversal training. The paradigm for the

reversal sessions were the same as used in the discrimination task, except the whisker that signalled

Go cue in the discrimination task now signalled NoGo cue and vice versa.

Figure 2-2 Discrimination task Rats were trained to discriminate between rewardable (Go) and non-rewardable (NoGo) vibrotactile stimulation delivered to individual whiskers.

Online control of the hardware and analysis of the animals' behaviour during the experimental

paradigms were implemented via in-house software written in LabView (National Instruments,

Austin, TX, USA) and a standard multi-purpose AD/DA board (model NI 6229). Animals were tested in

darkness and were constantly monitored by the experimenter via an infrared USB camera (Maplin

Electronics, Wombwell, UK).

2.2.6 Electrophysiological recordings

15-30 minutes prior to each recorded session, each electrode was lowered approximately 0.32mm

(corresponding to a 45 degree turn of the drive screw) in an attempt to sample neuronal responses

from different locations in the target structure. The head fixed rat was connected through a flexible

wire, to a 16 channel head stage (Plexon Inc., Dallas, TX, USA) immediately before recording. During

the discrimination task, wideband signals were acquired continuously via an op-amp based head-

stage amplifier (HST/8o50-G1-GR, 1x gain, Plexon Inc., Dallas, TX, USA), passed through a

preamplifier (PBX2/16wb, 1000x gain; Plexon Inc., Dallas, TX, USA) and digitized at 40,000 Hz.

25

All data processing was done offline. Recorded field potentials were down sampled to 5,000 Hz and

evoked responses extracted from the raw data using a 200 Hz low-pass Butterworth filter.

Timestamps for cue onsets and licking responses were synchronised in neuroexplorer (Nex

Technologies, Madison, AL, USA). Further analyses were calculated using Neuroexplorer and custom-

written Matlab routines.

2.2.7 Technical challenges with obtaining single unit recordings

The headpost and microdrives were implanted in one surgery and the majority of the behavioural

training occurred after the microdrives had been implanted. As the electrophysiological recordings

were made in over-trained animals, this meant that the implanted electrodes were embedded in the

brain of the rat for months before recordings could be obtained. In the presented dataset no single

units were recorded, which we attribute to this long period between implantation and recording

(Prasad et al., 2012). In order to increase the quality of the recording electrodes by decreasing the

duration they were imbedded in tissue before recording, a second group of 8 rats were trained and

implanted using a modified surgical procedure, in which only the skull cap and head post were fixed

after the initial behavioural screening and tetrode tungsten electrodes1 were implanted in a second

surgery, after the rat had successfully learned the discrimination task. However, this change in

procedure caused the skull cap to become structurally unstable and no electrophysiological

recordings were obtained from these rats. Data from this later group of rats will not be presented

here.

2.3 Results

2.3.1 Behaviour

Three rats successfully learned to discriminate between stimulation of the Go and NoGo paired

whisker (Figure 2-3). In addition, one rat (rat 1 in Figure 2-3) also learned to discriminate between

Go and NoGo stimulus after reversal, i.e., when the whisker previously associated with Go cue

instead signalled NoGo cue and the whisker previously associated with NoGo cue instead signalled

Go cue (Reversal) (Figure 2-3C).

1 For details on manufacturing of tetrode electrodes, see Chapter 3, Methods.

26

Figure 2-3 Rats trained to discriminate between rewardable (Go) and non-rewardable (NoGo) whisker stimulation licked more during Go vs. NoGo paired stimulation. A. example licking response. Period of tactile stimulation is marked with yellow. B. average lick ratio for Go trials (no. correct responses to Go cue/total no. Go trials and NoGo trials (no. incorrect responses to NoGo cue/total no. NoGo trials), respectively during discrimination trials in rat 1 - 3. C. average lick ratio during reversal trials in rat 1. Error bars indicate SEM.

2.3.2 Electrophysiological recordings

I attempted electrophysiological recordings in all three rats that successfully learned the task. The

first rat that successfully learned to discriminate between stimulation the two whiskers was used to

test and optimise the parameters for the electrophysiological recordings. However, by the time

recordings commenced, this rat could no longer be head fixed and was, therefore, removed from the

experiment. In another rat no activity could be registered in DLS after training, potentially due to a

damaged electrode connection. Therefore the data presented here comes from a single rat which

learned the behavioural task and had functioning recording electrodes. In addition, potentially due

to the amount of time required for training, no spike activity was recorded in this animal and the

analyses presented below are based exclusively on local field potentials. Therefore the data reported

in this chapter has to be viewed as preliminary and is primarily used to set the stage for the

experiments carried out for subsequent chapters. The technical challenges encountered in the

current experiment were successfully overcome in these later experiments.

Local field potential (LFP) responses to Go and NoGo cues during discrimination and reversal was

recorded from one rat (rat 1 in Figure 2-3) and sessions with high discrimination (Go trial lick ratio ≥

0.85 & NoGo trial lick ratio ≤ 0.25) were analysed for cue-evoked potentials (Figure 2-4A and B). 5

discrimination sessions; with 224 Go and 225 NoGo trials and 2 reversal sessions with 67 Go and 67

NoGo trials were included in the analysis. In this rat, cue evoked potentials in DLS were enhanced in

response to the Go cue compared with the NoGo cue both during discrimination (Figure 2-4A) and

27

reversal (Figure 2-4B), suggesting that sensory evoked responses in DLS were modulated by reward

expectation.

Figure 2-4 DLS cue-evoked responses during discrimination and reversal in one animal. A & B. Tactile evoked potentials in DLS were enhanced in response to Go cue (red) compared with NoGo cue (black) in discrimination (A) and reversal (B) trials with Go trial lick ratio ≥ 0.85 & NoGo trial lick ratio ≤ 0.25. C & D. Separating Go and NoGo responses in to Hit (licking during Go cue) (red), Miss (no licking during Go cue) (green), Correct rejection (no licking during NoGo cue) (black) and False alarm (licking during NoGo cue) (blue), suggests that cue evoked responses in DLS may be modulated by motor preparation. Error bands indicate SEM. Period of tactile stimulation is marked with yellow.

To investigate the role of motor component in DLS response during discrimination, Go trial

responses were separated into Hits (trials in which the rat responded correctly, i.e. licked the spout)

and Misses (trials in which the rat did not respond correctly, i.e. did not lick the spout) and NoGo

trial responses were separated into Correct rejections (trials in which the rat responded correctly,

i.e. did not lick the spout) and false alarms (trials in which the rat did not respond correctly, i.e.

licked the spout). This analysis included 8 discrimination sessions (including the 5 sessions used for

analysis of responses in sessions with high discrimination), with a total of 375 Hit, 48 Miss, 314

correct rejection and 107 False alarm trials (Figure 2-4C). This separation of cue responses revealed

enhanced DLS cue evoked responses not only in response to Go cues followed by licking (Hit), but

also in response to NoGo cues followed by licking (False alarms), although to a smaller degree than

28

in Hit trials, suggesting that motor preparation may constitute a significant component to reward

paired cue evoked responses in DLS.

The observed enhanced responses to Go stimulation compared with NoGo stimulation was

consistent throughout discrimination and reversal sessions in the one recorded. When trials were

further divided into correct and incorrect behavioural response, the enhanced response observed to

stimulation in Hit and False alarm trials were consistent throughout the analysed discrimination

sessions.

2.4 Discussion

To assess whether the level of salience of the sensory input to DLS affects the sensory

representation in this structure, a novel tactile discrimination task was implemented, in which

sensory stimulation to one whisker is associated with a reward whereas stimulation of another

whisker was associated with reward omission. Rats were trained to lick a spout in response to

stimulation of the rewardable (Go) whisker while abstaining from licking when the non-rewardable

(NoGo) whisker was stimulated (Figure 2-2). All three rats presented in this chapter successfully

learned to discriminate between the two stimulated whiskers and to adjust their behaviour to

optimise the outcome (Figure 2-3). A similar two whisker discrimination task was recently

implemented by Ollerenshaw et al. (2014) to examine the role of adaptation on stimulus detection

and discrimination. In their setup, rats were also trained to discriminate between stimulation of two

distinct whiskers associated with either reward or reward omission. However, in their experiment,

these cue-paired discriminative stimulations were either preceded by stimulation of both whiskers

(adaptation) or not stimulated prior to cue-paired discriminative stimulation (no adaptation) and this

adaptation was found to improve discrimination (Ollerenshaw et al., 2014). The above study, along

with the findings presented here, demonstrate the versatility of using two whisker stimulation in

behaving rodents to examine the role of additional factors affecting the processing of sensory input,

be it pre-cue stimulation as in the above study or reward-value as in the current study. The current

study is the first to use this tactile Go-NoGo discrimination task to address the role of salience on

sensory processing in DLS neuronal ensembles.

In terms of the neurophysiological data obtained in the current study, due to technical

challenges no spike activity was detected and I was only able to record local field potential data from

a single rat as detailed in the results section. The data obtained was largely consistent with my

hypotheses and the observed effect of trial type on stimulus-evoked DLS responses was consistent

throughout discrimination and reversal sessions. Given the very limited amount of data however,

29

the current results can only be viewed as preliminary. Here they are used to motivate and set the

stage for the experiments reported in subsequent chapters where the technical challenges

encountered here were overcome successfully.

Analysis of LFP response to stimulus onset in sessions with high accuracy of execution

revealed enhanced evoked potentials in Go trials compared with NoGo trials, suggesting that DLS

evoked potentials are influenced by differences in reward-value (Figure 2-4A). However, when

sessions with lower accuracy were included into the analysis and stimulus-evoked DLS responses

were also divided into correct and incorrect behavioural response, an enhanced evoked response to

stimulus onset was not only observed in Go trials with correct response (Hits), but also in NoGo trials

with incorrect response (False Alarms) (Figure 2-4C). In both Hit and False Alarm trials the rat licked

the spout in response to stimulus onset, suggesting that the enhanced LFP response in DLS may be

associated with movement initiation. In comparison, only a very small response was observed in

NoGo trials in which the rat correctly suppressed licking (Correct Rejections), and no response was

seen in Go trials where the rat failed to lick (Misses) (Figure 2-4D), further suggesting that DLS

response to stimulus onset were not affected by differences in reward-value associated with the two

stimulations. Indeed, previous work examining T-maze choice behaviour in rats have observed an

increase in DLS activity during execution of the task as result of training (Barnes et al., 2011, Root et

al., 2010, Thorn et al., 2010, Kimchi et al., 2009). However, this increased DLS activity was more

strongly associated with movement onset, rather than instructional cue. The cue-evoked DLS

response observed in the current study may be more strongly related to the initiation of movement

immediately after presentation of the cue rather than to the cue itself. In the T-maze studies

mentioned above, the instructional cue was associated with increased activity in dorsomedial

striatum, rather than DLS, as a result of training (Root et al., 2010, Thorn et al., 2010, Horvitz, 2009).

In the T-maze choice task the instruction cue signals to the animal to initiate a motor

response as well as the availability of reward in every trial, thereby making it difficult to analyse the

contribution of reward-value to responses in neurons that are also associated with motor response.

In Monkeys performing a reward-directed motor task, motor-related neurons in putamen

(homologous of DLS in rodents) were found to be modulated by reward probability immediately

before and during initiation of reward-directed movement (Pasquereau et al., 2007, Hassani et al.,

2001), and findings from another study in monkeys executing a similar reward-directed motor task,

suggest that some motor-related neurons in putamen encode the value associated with action

rather than the action itself (Samejima et al., 2005). In rats trained to nose poke in response to an

auditory cue signalling reward availability, neurons that responded to movement showed increased

firing when movement was paired with reward compared with unrewarded movement (Kimchi et

30

al., 2009). Similarly, in the current study, a greater LFP response was observed in Hit trials than in

False alarm trials, suggesting that reward expectation also contribute to DLS evoked responses.

Thus, it is possible that the rat licked in response to the NoGo stimulus in False Alarm trials because

it mistook the NoGo stimulus for a Go stimulus, in which case the enhanced LFP response in False

Alarm trials may still be influenced by reward expectation.

In the standard Go-NoGo task, cues signalling reward availability also signal to the animal to

make a motor response, whereas cues signalling the animal to suppress motor response are not

rewarded. Therefore, it is not possible to separate motor and reward component of striatal

responses to reward-paired sensory stimuli using the Go-NoGo paradigm in its original form. To

separate these two components, I propose a modification of the standard Go-NoGo task, wherein

not only correct responses to Go cue (Hit) are rewarded but also correct responses to NoGo cue

(Correct rejection) are rewarded. With this modification, reward availability would be signalled by

both cue types but only Go cues would signal to the rat to initiate a motor response, which would

enable examination of reward expectation and motor preparatory components of cue-evoked

striatal responses. Previous work suggests that distinct subregions of striatum contribute differently

to reward and motor components of learned reward-directed behaviour (Balleine et al., 2009,

Liljeholm and O'Doherty, 2012). Electrophysiological recording of neurons in DMS and nucleus

accumbens as well as DLS during execution of the proposed modified Go-NoGo task may provide

information about subregional differences in cue-evoked responses a well as information about the

role of communication between striatal subregions during execution of a complex reward-driven

behavioural task.

2.4.1 Conclusion

LFP response to stimulus onset was found to be enhanced in trials in which cue onset was

immediately followed by initiation of motor response, suggesting the observed response is

associated with motor initiation. However, the enhanced response in False Alarm trials may still be

caused by the rat, incorrectly, expecting a reward for its response. Because of the restricted dataset,

the current findings should be viewed as preliminary, however they do support the significance of

considering not only the valence of the conditioned cue (here the tactile stimulus) but also the

associated behavioural response when interpreting cue-triggered neural responses in Go-NoGo

tasks. Further research is needed to investigate the extent to which DLS responses reflect the reward

value of the conditioned cue vs. response initiation associated with reward retrieval. To this end, a

modified version of the standard Go-NoGo task is proposed in the next chapter. To avoid the

technical challenges encountered here the next chapter also moves away from the head-fixed

31

preparation and successfully implements tetrode recordings in freely moving animals using tones

rather than tactile stimuli as conditioned cues.

32

Chapter 3: Exploring motor and reward components of striatal

responses to reward-paired auditory cues

3.1 Introduction

3.1.1 The role of striatum in reward-directed behaviour

Adaptive behaviour in a natural environment requires the ability to associate multiple cues with a

variety of possible outcomes and subsequent implementation of an appropriate behavioural

response. Successful behaviour necessitates integration of reward processing, associative learning

and motor planning and thus interaction between brain regions maintaining these processes (Haber

and Knutson, 2010, Joel and Weiner, 2000). The striatum is part of the reward circuitry (Haber, 2003,

Tricomi and Lempert, 2015), and is associated with cognitive and motivational processing (Tricomi

and Lempert, 2015, Basar et al., 2010, Baldo and Kelley, 2007), as well as with the execution of

motor responses (Costa et al., 2004, Pisa and Schranz, 1988, Haber, 2003). Rat striatum consists

dorsally of dorsomedial striatum (DMS), homologue to caudate nucleus in humans, and dorsolateral

striatum (DLS), homologue to putamen in humans, and ventrally of nucleus accumbens (NAc) and

the olfactory tubercle (Devan et al., 2011, Balleine et al., 2009, Haber, 2003). Together, DMS, DLS

and NAc maintain a range of functions crucial for assessing stimulus-outcome contingencies and

optimising the individual’s responses to these stimuli (Balleine et al., 2009, Liljeholm and O'Doherty,

2012). However, region-specific lesion studies suggest that motivational, motor and cognitive

components of reward-directed behaviour are represented differently in each striatal sub-region

(Balleine et al., 2009, Devan et al., 2011, Hart et al., 2014, Yin et al., 2005, 2006).

In my previous study (presented in chapter 2) I established a tactile Go-NoGo task (Figure 2-

2), in order to assess whether the level of salience of sensory input to DLS affects the sensory

representation in this structure. The initial findings in this study suggested that DLS responses are

related to motor initiation, but may also be modulated by the behavioural significance of sensory

input. However, in a standard Go-NoGo task, cues signalling reward availability also signal to the

animal to make a motor response, thereby making this behavioural paradigm unable to separate

motor and reward component of striatal responses to reward-paired sensory stimuli. To distinguish

between these two processes, I have developed a modified version of the standard Go-NoGo task, in

which correct responses in both trial types were rewarded (Figure 3-1). With this modification,

reward availability was signalled by both cue types but only Go cues signalled to the rat to initiate a

33

motor response, thereby enabling examination of reward expectation and motor initiation

components of cue-evoked striatal responses. Previous work suggests that DLS, DMS and NAc

contribute differently to specific components of learned reward-directed behaviour and adaptive

behaviour requires successful integration of input from all three striatal sub-regions (Balleine et al.,

2009, Liljeholm and O'Doherty, 2012). Examining the activity of DLS, DMS and NAc simultaneously in

the non-lesioned brain may increase our understanding of the function of these sub-regions as well

as how network activity between the subregions relates to behavioural choice.

3.1.2 Dorsomedial striatum

DMS is strongly associated with goal-directed learning (Shiflett et al., 2010, Shan et al., 2014, Yin et

al., 2005, Balleine et al., 2009, Devan et al., 2011, Hilario et al., 2012), and updating of stimulus-

response-outcome contingencies (Devan et al., 2011, Yin et al., 2005). In a conditional discrimination

task where rats relied on visual cues to determine which arm in a maze was rewarded in the

following block of 3-5 trials, inactivation of DMS increased perseverance errors, that is, the rat

required more trials within a new block to switch arm or completely failed to switch arm (Baker and

Ragozzino, 2014). Similarly, when rats were trained to discriminate sets of odours and bedding type

paired with reward from unrewarded odour/bedding-sets in a foraging task, rats with lesions in DMS

failed to modify their behaviour when previously unrewarded odour/bedding sets became rewarded

and vice versa (Lindgren et al., 2013). Recently, single unit activity in DMS during the action selection

phase in a rat decision making task, has been found to code for behavioural response in previous

trial (Ito and Doya, 2015). In fact, numerous studies have demonstrated that lesions or inactivation

of DMS in rats, either before or after training, reduce sensitivity to changes in response-outcome

contingency, as well as post-training outcome devaluation, thus, making action choice habitual and

rigid (Shiflett et al., 2010, Shan et al., 2014, Yin et al., 2005, Balleine et al., 2009, Devan et al., 2011,

Hilario et al., 2012, Hart et al., 2014) strongly implicating DMS processing in the ability to switch

behaviour in response to changes in context-outcome associations (Devan et al., 2011, Lindgren et

al., 2013, Baker and Ragozzino, 2014). DMS and DLS function independently of each other but also

compete for control over stimulus-response behaviour (Hilario et al., 2012, Balleine et al., 2009). As

a behavioural response is learned, and becomes habitual, control of this behaviour shifts from DMS

to DLS (Dias-Ferreira et al., 2009, Balleine et al., 2009, Yin et al., 2005). However, control reverts

back to DMS if DLS function is disrupted or if increased vigilance or reassessment of behaviour

becomes advantageous (Balleine et al., 2009, Yin et al., 2005).

In humans and monkeys, activation of the caudate nucleus, equivalent of DMS in rats have

been found to increase in response to higher probability of reward and decreased when reward was

34

omitted, suggesting activity in the DMS is sensitive to reward expectancy (Tricomi and Lempert,

2015, Fanelli et al., 2013, Kawagoe et al., 1998).

Lesions of DMS do not significantly affect forelimb reaching movement in rats, whereas

lesions of DLS have been found to impair forelimb reaching (Pisa and Schranz, 1988), suggesting that

DMS is not directly involved in execution of motor response. However, rats trained to nose poke for

reward to olfactory cues decreased the vigour of their response when lesioned in the DMS,

suggestion that DMS modulates the force of learned motor-dependent reward-directed responses

(Wang et al., 2013).

3.1.3 Dorsolateral striatum

DLS is associated with sensory processing (Mowery et al., 2011) and learning and execution of

automated stimulus-response behaviour (Devan et al., 2011, Costa et al., 2004, Mowery et al., 2011,

Schmitzer-Torbert et al., 2015, Fanelli et al., 2013, Yin et al., 2006). As a task is learned and becomes

automated, responding becomes dependent on the DLS (Balleine et al., 2009, Dias-Ferreira et al.,

2009, Tang et al., 2009). Rats over-trained on a lever pressing task become insensitive to changes in

outcome value, that is, they continue pressing the lever even when the reward is devalued (Yin et

al., 2006, Balleine et al., 2009). This training induced insensitivity to changes in outcome value is

coupled to changes to plasticity in DLS and post training lesions in DLS reinstate the sensitivity to

outcome value (Balleine et al., 2009).

Previous work has shown the majority of neurons in DLS to be movement-related (Tang et

al., 2007, 2009) and these neurons were found to decrease their firing rate in response to repeated

training on a movement-dependent task while the smaller population of non-movement related

neurons increased or maintained their firing rate (Tang et al., 2007, 2009). This suggests that as an

automated response is learned the response moves from being facilitated by a large number of

neurons to being modulated by a smaller population of stronger firing neurons. DLS lesioned rats

have difficulty learning tasks that involve precise motor movement whereas general movement was

left unimpaired (Devan et al., 2011) and several studies suggest DLS play a crucial role in the fine

tuning of precise motor responses which, through repeated training and pairings of stimulus-

outcome associations, optimised the rats motor-dependent behaviour toward achieving a desired

outcome (Balleine et al., 2009, Featherstone and McDonald, 2004, Featherstone and McDonald,

2005, Tricomi and Lempert, 2015, Pisa and Schranz, 1988, Devan et al., 2011).

Single neurons in DLS have also been found to increase their activity during movement

triggered by external cues (Devan et al., 2011, graybiel et al., 1994). In rats trained to nose poke in

response to an auditory cue signalling reward availability, neurons that responded to movement

35

showed increased firing when movement was paired with reward than when it was unrewarded

(Kimchi et al., 2009), suggesting that reward expectation also contribute to DLS evoked responses.

3.1.4 Nucleus Accumbens

NAc has been shown to regulate motivational (Tricomi and Lempert, 2015, Basar et al., 2010, Baldo

and Kelley, 2007) and reward-related components of behaviour (Tricomi and Lempert, 2015).

In humans, NAc activation has been shown to be unaffected by variation in reward

probability, but is decreased when reward is devalued, suggesting NAc is sensitive to motivational

value of reward (Tricomi and Lempert, 2015). In rats, trained to respond to an auditory cue for

reward, NAc single units responded during subsequent exploration of the reward receptacle

regardless of whether the reward was delivered or withheld, whereas uncued entries to the reward

receptacle, which were never rewarded, did not produce excitation in NAc single units (Nicola et al.,

2004b). This suggests that NAc reward response may be driven by conditioned stimuli associated

with the reward or be associated with reward-seeking motor response (Nicola et al., 2004b).

The choice of behaviour, e.g. initiate or suppress a motor response, is guided by the

assessment of the available cues, the expected outcome of responding to this cue and cost of

responding (Basar et al., 2010). In rats trained on a lever pressing task, optogenetic inhibition of NAc

MSNs after feedback improved responses in next trial (Aquili et al., 2014) implicating NAc in the

updating of response-outcome contingencies. Changes in NAc firing have been linked to reward-

directed motor response, (Roitman et al., 2005) and lesions in NAc, although to lesser extent than

lesions in dorsal striatum, have been found to impair movement (Pisa and Schranz, 1988, Wang et

al., 2013, Hart et al., 2014). A recent study examined the effect of motor and reward component on

NAc single unit responses (Roitman and Loriaux, 2014). This study also used a modified version of

the Go-NoGo task where correct responses in both trial types were rewarded. Rats were trained to

discriminate between light cues presented either above the extended lever (Go trial cue) or on the

opposite side of the pellet magazine (NoGo trial cue) and correct responses in both trial types (lever

press or withholding lever press, respectively) were rewarded with a sugar pellet. In addition to the

visual cue, an auditory cue (white noise) was presented along with either the Go or NoGo trial light

cue. In this paradigm NAc units displayed a greater increase in firing rate or a smaller decrease in

firing rate in response to trial cue in trials where the rats withheld lever press compared to trials

where the rats pressed the lever, regardless of trial type, suggesting that NAc single unit activity

correlates with initiation of motor response rather than outcome expectancy (Roitman and Loriaux,

2014).

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3.1.5 Interaction between striatal sub-regions

Together the sub-regions of striatum maintain a range of functions crucial for assessing stimulus-

outcome contingencies and optimising the individual’s responses to these stimuli (Balleine et al.,

2009, Liljeholm and O'Doherty, 2012). Although lesion studies suggest consistent functional

differences between striatal subregions (Devan et al., 2011, Balleine et al., 2009), axons and

dendrites within each sub-region often cross into other subregions (Haber, 2003), which in

conjunction with its position in Cortico-striatal-thalamic circuits, may facilitate cross-regional

integration of information (Haber, 2003), and organisation of adaptive behavioural output (Liljeholm

and O'Doherty, 2012).

In a recent study, Ito and Doya (2015) recorded single unit activity simultaneously in DLS,

DMS and ventral striatum in rats during performance of a choice task. In their paradigm, rats were

trained to nose poke into a central port and await another tone cue indicating which of two ports

(“left” or “right”) offered the highest probability of reward. In a subset of trials a third “choice” tone

cue was presented, which offered no information about the probability of reward in the left and

right port (Ito and Doya, 2015). Striatal subregions were found to respond differently to distinct

elements of the task, with ventral striatum displaying the highest firing rate response at the

initiation of the trial, DLS and DMS responding to onset and offset of the cue tone, respectively, and

all three regions responding as the rat approached the left or right port (Ito and Doya, 2015). In this

task all choices involved initiation of motor response and, thus, makes it difficult to distinguish

between striatal activity related to motor initiation and reward expectation. However, this study

demonstrates how recording single units in DLS, DMS and NAc simultaneously in animals that are

executing a complex behavioural task, provide information about how activity within each sub-

region relate to distinct elements of a specific task.

The execution of optimal behaviour in complex tasks such Go-NoGo tasks, requires the

integration of learned stimulus-response-outcome contingencies, flexible switching between motor

initiation and suppression as well as motivational processes, which likely involve contribution from

dorsal striatal subregions as well as NAc and may also require communication between subregions

to facilitate adaptive behavioural outputs. Region-specific lesion studies provide clues on whether

striatal subregions are necessary for reward-related behaviour. However examining the activity of

the three main subareas in the non-lesioned brain allows comparisons between structures within

animal and trial as well as an assessment of how network activity between the subregions relates to

behavioural choice.

37

3.1.6 Study aims

Characterizing the dynamic modulation of behaviour imposed by the reward expectation as well as

motor preparation in behaving animals will be a key step to understanding the normal function of

striatal sub-regions. However, in most standard behavioural paradigms, cues signalling reward

availability also signal to the animal to make a motor response, thereby making standard

behavioural paradigms unable to separate motor and reward component of neural responses to

reward-paired sensory stimuli. To distinguish between these two components of striatal sensory

responses, one group of rats were tested in a modified version of the Go-NoGo task, in which rats

had to press a lever in response to a Go cue and supress responding to the NoGo cue. In the

modified task (hereafter referred to as the “Go-NoGo Plus” task), correct responding in both trial

types were rewarded with a sugar pellet. A second group of rats were trained in a standard Go-NoGo

task (hereafter referred to as the “Go-NoGo Minus” task) where only correct responses to Go cues

were rewarded and correct suppression of lever press in NoGo trials had no programmed

consequence. Evoked responses in DLS, DMS and NAc were recorded simultaneously during these

discrimination tasks in overtrained rats.

Through comparison the single unit responses to cue onset in these two tasks, the current study

aimed to examine the role of individual striatal sub-regions, as well as communication between

subregions, on reward expectancy and preparation of motor response during conditioned

discrimination.

3.1.7 Hypothesis

Striatal sub-regions associated with motor preparation, such as DLS and to lesser extend

NAc, were expected to produce a stronger response to cues signalling motor initiation

compared with DMS.

Striatal sub-regions modulated by reward expectancy, most notably NAc, were expected to

produce a stronger response to cues signalling the opportunity to obtain a reward compared

with non-rewarded trials.

38

3.2 Methods

3.2.1 Animals

Male Lister Hooded rats (n = 7; Charles River, Cambridge, UK) weighing 225-250g on arrival were

kept on reversed light/dark cycle (12:12h; lights on 19.00h). Animals had access to water ad libitum

and access to food (LabDiet 5LF5, PMI Nutrition Intl, Brentwood, MO) for at least 2h per day. All

experiments were carried out under institutional ethical approval and with project and personal

licence approved by the UK Home Office.

3.2.2 Apparatus

All behavioural training and testing was conducted in four identical operant chambers [30 x 31 x 24

cm (height x width x depth); Med Associates Inc., St Albans, VT]. In each chamber a magazine for

delivery of sugar pellets (Dustless Precision Pellets, Bio Serv, Sheffield UK) was fitted in the middle of

one of the chamber walls with a retractable lever positioned to the left of the magazine. Both

magazine and lever were positioned 2.5 cm above the metal grid floor of the chamber and had a

light positioned immediately above each. A speaker was positioned above the magazine just below

the ceiling of the box and a house light was positioned at the top of the opposite wall of the

chamber.

For the electrophysiological recordings a modified operant chamber was used. The modified

operant chamber differed from the standard operant chamber in that all metal surfaces (walls and

grid floor) were covered by Plexiglas sheets to minimise electrical noise. In addition the wall-fitted

magazine was replaced by a custom made square receptacle (2 x 5 x 3 cm (height x width x depth);

Rob Hemmings, School of Psychology, University of Leicester) attached to the grid floor 3.5 cm from

the wall. The magazine light signalling reward was placed on the wall behind and above the floor-

fitted magazine. This alteration was made as previous work in our group had shown that the

Neuralynx tetrode drive restricted the rat’s access to the wall fitted magazine.

3.2.3 Behavioural training

Upon arrival rats were pair housed and left undisturbed for 4 days, followed by 1-2 days of

habituation to handling by the experimenter before beginning the behavioural training. After

handling a handful of sugar pellets (Dustless Precision Pellets, Bio Serv, Sheffield UK) was left in the

home cage, the same sugar pellets were used as reinforcement throughout training. Behavioural

training consisted of the following stages:

Magazine training. On the first training day each rat was allowed to explore the operant

chamber. During the magazine training session no levers were presented but the house light and

39

magazine light were illuminated. Exploring the area around the magazine was rewarded with a sugar

pellet until the rat spent most of its time near the magazine.

Lever press training. Rats were initially trained to press a lever for sugar pellets using

standard shaping techniques. Briefly, the training the lever was extended into the chamber and lever

light was illuminated. Initially any exploration of the lever was reinforced with the delivery of a sugar

pellet. Once the rat started pressing the lever on a regular basis, only actual presses of the lever

were rewarded, on a fixed ratio 1 schedule (FR1). Reinforcement learning continued until the rat

performed 100 lever presses within 30 minutes in two consecutive sessions. This level of responding

was usually achieved in 2-3 sessions and all rats were responding to criterion at the end of 4th

session.

Discrimination tasks. Rats were trained to either respond (Go trials) or suppress (NoGo trials)

responding to auditory cues of different frequencies (1 vs. 10 kHz (75dB): counterbalanced). Each

trial started with the presentation of either the Go or NoGo tone. Four seconds after tone onset the

lever was presented allowing the rat a 4 second response interval to press the lever. Upon lever

press or, if the rat did not press the lever, at the end of the 4 second response interval, the lever

retracted and the tone was switched off. This was followed by a 60 second inter trial interval (ITI)

(Figure 3-1). In the Go-NoGo plus task rats (n = 4) were rewarded with a sugar pellet for both correct

lever press (Hit) and correct omission of lever press (Correct Rejection) trials. In the Go-NoGo minus

task (n = 3) only correct lever press (Hit) trials were rewarded whereas correct rejections had no

programmed consequence. In both tasks incorrect lever press (False Alarm) resulted in a 60 second

time-out with lights out. Incorrect omission of lever press (Miss) had no programmed consequence

(Figure 3-1).

Figure 3-1 Behavioural paradigm Rats were trained to either respond (Go trials) or supress (NoGo trials) responding to auditory cues of different frequencies (1 or 10 kHz, counter-balanced). In the Go-NoGo plus task (left panel) rats were rewarded in both Hit and CR trials. In the Go-NoGo minus task (right panel) only Hit trials were rewarded.

In the first 1 – 2 weeks of training each session consisted of 25 Go trials and 25 NoGo trials

presented in a semi-randomised fashion (max. 2 of the same trial type in succession). NoGo tones

40

were played at a lower volume than Go tones (60-65dB vs. 80-85dB). Once the rat showed a higher

level of lever pressing (minimum 3 days in a row) in Go trials compared to NoGo trials, the protocol

was modified so that incorrect responding were always followed by a trial of the same trial type

(“Miss” in Go trials and “False Alarm” in NoGo trials). All rats successfully learned to discriminate

between Go and NoGo tones (Figure 3-2). For each training session Hit (number of hits divided by

the total number of Go trials) and False Alarm no. (number of false alarms divided by the total

number of NoGo trials) response rates were calculated for each rat. The rats were considered to be

discriminating at criterion when Hit rate was above 0.8 and False Alarm rate below 0.25 within a

session for 3 consecutive sessions. At this stage the volume of both Go and NoGo tone was set to

75dB, the rats retrained to criterion at which point training was moved to the modified operant

chamber used for electrophysiological recordings where the rats were once again trained to criterion

(see Figure 3.2A for example of training performance), before the tetrodes were implanted.

Figure 3-2 Response ratios in the Go-NoGo Plus and Go-NoGo Minus task All rats successfully learned to discriminate between Go and NoGo tones in both Go-NoGo Plus and Go-NoGo minus task. Response s to Go tone (Hit: black) increased and responses to NoGo tones (FA: red) decreased within 20 sessions of discrimination training. A. Example response rates from beginning of Go-NoGo Plus discrimination training to the end of the experiment from one rat. Dashed lines indicate a) first training session with Go and NoGo tones played at same volume (75dB) and b) first training session in modified operant chamber and c) surgery and recovery period. Although these changes to the rats training environment initially led to decreased discrimination, all rats quickly returned to criterion discrimination levels. B and C. Mean response rate for the first 21 sessions for B) Go-NoGo Plus (n=4) and C) Go-NoGo Minus (n=3) task. Error bars indicate SEM.

41

After surgery, the rats were habituated to being tethered to the headstage before testing,

first by plugging in and removing a dummy plug and subsequently attaching the head stage and

allowing the rat to freely explore the inside of an open-top black Plexiglass arena (52 cm wide × 52

cm long  × 40 cm high). This part of the habituation was also used to check the quality of the

electrophysical signal. Tetrode drives

Tungsten wire (H-Formvar insulation with Butyral bond coat, diameter: 12μm, California Fine Wire

Company, CA, USA) was folded twice and wound using a metal clip attached to the folded wire and a

magnetic stirring plate. Recording with tetrode offers an advantage compared to single wire

recording, by making it easier to separate spikes from closely positioned neurons (Figure 3-3). To

ensure that the tetrode would travel in a straight line upon insertion into the brain, it was stabilised

by gently threading the tetrode through a segment of fused silica tube (ID/OD 110μm/170μm, SGE

Analytical Science, SGE Eurone LTD, Milton Keynes, UK) and secured with epoxy glue. Approximately

1cm of the silica-threaded tetrode was left protruding from the drive.

Additional guide holes were drilled into the Neuralynx drives (Versadrive Neuralynx, Bozeman;

Montana, USA) to accommodate the range of the AP and ML coordinates and tetrodes were

attached to the drive. After insertion into the Neuralynx drives the tip of the tetrodes were cut to

leave approximately 0.5mm exposed. The resistance of each wire was measured and wires with a

resistance above 300kΩ were gold-plated to decrease resistance to below 300kΩ.

Figure 3-3 Tetrode based spike sorting. Because the tips of the four wires in each tetrode will be positioned at different distances from the neurons they record, spikes from different neurons are more easily separated into separate clusters, when sorting the spikes collected from all four channels in a tetrode simultaneously. The above example show the waveform from two neurons recorded by four tetrode wires. Whereas the waveforms from the two recorded neurons closely resemble each other in channel 1 the difference in amplitude observed in channel 2-4, likely caused by the difference in distance from the recording wire to the neurons, clearly shows that the waveforms originates from two separate neurons.

3.2.4 Surgery

To minimise discomfort from post-operative injections, all post-operative medication was

administered orally mixed with strawberry jelly (Harley’s, UK). The rats were habituated to the taste

of strawberry jelly in their home cage for three days before surgery. On the day of surgery, animals

were anesthetised with Isoflurane (Schering-Plough) and placed in a stereotactic frame.

Glycopyrronium bromide (0.06-0.08mg/kg bodyweight, i.m.; Anpharm; Warsaw, Poland) or Atropine

42

Sulphate (0.04mg/kg bodyweight, s.c.; Hameln Pharmaceuticals Ltd; Gloucester, UK) were given to

reduce respiratory tract secretions. Lacri-Lube Eye Ointment (Allergan; Wesport, Ireland), was

applied to the eyes to prevent corneal desiccation. Non-steroidal anti-inflammatory analgesia

(Carprieve, 5mg/kg; S.C; Norbrook Laboratories Ltd; Corby, UK) and antibiotics (Baytril: 0,2ml/kg

bodyweight, cs.; Bayer; Leverkusen, Germany) were given minimum 15 minutes before incision.

During the surgery the rat was placed on a homeostatic heat pad (Harvard Apparatus, Boston,

Massachusetts, USA) and its body temperature was monitored and kept constant at 36-37oC. 5%

glucose/saline solution (3ml/hour, sc.) was administered via an infusion pump (Intec, K.D. Scientific,

Holliston, Massachusetts, USA). An incision was made along the sagittal line, the periosteum was

retracted and 12 stainless steel anchoring screws (Morris Co., Southbridge, Massachusetts, USA, part

number 0X 1/8 flat) were affixed to the cranium (3 screws to the frontal plate, 4 screws to the side

of and 3 screws to the top of the parietal plate and 2 screws to the interparietal plate) to enable

secure placement of the dental cement cranium cap (Henry Schein Inc, Melville, NY USA). A right

side craniotomy was performed and the dura was removed immediately before insertion of the

tetrodes. Tungsten tetrodes were implanted unilaterally to target the following structures: DLS,

DMS, NAc shell and NAc core (see Table 3-1 for target coordinates based on Paxinos and Watson

(2007)). The tetrodes were sealed with paraffin wax and the implant was built up using layers of light

curing dental cement (Flowable Composite, Henry Schein; Gillingham, UK). A silver wire (Science

Products GmbH, Hofheim, Germany) inserted into the cerebellum served as a ground. In two animals

where a heart rate artefact was apparent in the recordings after recovery an extra silver wire was

inserted under the scruff and used as ground. Antibiotic ointment (Fuciderm; Uldum, Denmark) was

applied to the wound and the skin was sutured. A non-steroidal anti-inflammatory analgesic

(Carprieve, 5mg/kg; S.C; Norbrook Laboratories Ltd; Corby, UK) was given in jelly for 3 days post-

surgery. Oral antibiotics (Baytril, 2.5%, 0.2ml/kg; S.C., Bayer; Leverkusen, Germany) were given in

jelly twice daily for 5 days after surgery. The animals were given a week to recover from the surgery

before behavioural testing. They remained individually housed for the remainder of the experiment

to prevent damage to the implants.

Target structure Coordinates relative to Bregma (mm)

AP ML DV

DLS +0.8 +3.6 -4.0

DLS +0.4 +4.0 -4.5

DMS -0.4 +2.6 -3.5

DMS 0.0 +2.4 -4.4

NAc shell +1.2 +1.1 -7.0

NAc core/shell* +1.6 +1.3 -6.4

NAc core/shell* +1.6 +2.3 -6.8

Table 3-1 Coordinates targeted for recording of single unit responses in striatal subregions. *The tetrode tip progressed 0.125mm downwards between recordings, resulting in tetrodes targeting NAc core and shell to record NAc core in early sessions and shell in later sessions.

43

3.2.5 Electrophysiological recordings

15-30 minutes prior to each recorded session, each tetrode was lowered approximately 0.125mm

(corresponding to a 180 degree turn of the drive screw) to ensure that different neurons were

recorded in each session. The rat was connected through a flexible wire, allowing unimpaired

movement, to a 32 channel head stage (Plexon Inc., Dallas, TX, USA) immediately before recording

and placed in an operant chamber positioned in a sound-attenuated aluminium-plated box, which

served as a Faraday cage. During the discrimination task, wideband signals were acquired

continuously at a sampling rate of 25 kHz via an op-amp based head-stage amplifier (HST/32o25-

36P-GR, 1x gain, Plexon Inc., Dallas, TX, USA), and passed through a preamplifier (PBX2/32wb, 1000x

gain; Plexon Inc., Dallas, TX, USA). For spike sorting the raw signal was band-pass filtered offline

300-3,000Hz and single channel recordings were referenced to the average of all recorded channels.

For one rat in the Go-NoGo Minus task a lower signal/noise ratio was found when referencing to a

single channel not showing any spikes, therefore, referencing to a single channel was used in this rat.

Artefacts (identified as events occurring simultaneously in 8 channels) were removed using a custom

made Matlab code supplied by Manuel Molano, Systems Neuroscience Group, University of

Leicester. Spikes from each session with amplitudes above 5x the SD of background noise were

sorted as tetrodes using the Matlab-based Wave_clus software to yield single-unit spike trains

(Quiroga et al., 2004). Timestamps from single unit spike trains and timestamps for cue onset and

lever press obtained from the operant chambers were synchronised in Neuroexplorer (Nex

Technologies, Madison, AL, USA). Further analyses were calculated using Neuroexplorer and custom-

written Matlab routines. All statistical analysis was calculated using SPSS 22 Statistics (IBM SPSS,

Somers, NY, USA).

3.2.6 Verification of tetrode placement

After the last recording session, the rats were anaesthetised with Isoflurane. The area

around each tetrode tip was lesioned by passing a 30µA current through each electrode wire for 15

seconds. The rats were then perfused with 5% formal saline, the brains were removed and kept

refrigerated (5o C) in formal saline for 24 hours, then transferred into a 30% sucrose solution and

kept refrigerated for a further 2-3 days after which they were rapidly frozen using dry ice and hexane

and stored at -20o C. Tetrode placement was verified visually while cutting the frozen brains in 30μm

slices on a cryostat (Figure 3-4). Not all tetrodes could be identified from the brain slices. In these

cases, the medial-lateral (ML) and anterior-posterior (AP) position of the non-observed tetrode was

calculated based on the observed position of neighbouring tetrodes. To verify if the tip of each

observed tetrode had been identified, the observed dorsal-ventral (DV) position of the presumed tip

44

Figure 3-4 Verification of tetrode placement. Tetrode placements were verified visually while cutting the frozen brains in 30μm slices on a cryostat. Only sessions where the tetrode tips were in the target areas of DLS (marked in dark blue), DMS (marked in green) or NAc (marked in red) were included in the analysis of firing rate responses to cue onset.

was compared to the expected DV position of the tetrode tip and to the relative position of other

observed tetrode tips on the same drive. If the observed position of a presumed tip was markedly

dorsal compared to its expected position it was assumed that only the tetrode tract and not the tip

had been observed. In these cases, the position of the tip was calculated relative to the DV position

of identified tips on the same drive. In one rat in the Go-NoGo Minus task one tetrode targeting DMS

were verified to be positioned lateral of DMS (but dorsal of NAc) and was therefore removed from

the analysis. In one rat in the Go-NoGo Plus task the position of one tetrode targeting NAc could not

45

be verified and the identity of the remaining two NAc tetrodes could therefore not be confirmed.

Therefore, all three NAc tetrodes in this rat were excluded from the analysis. Statistical analysis

Behavioural and electrophysiological data were not normally distributed and did not pass

Levine’s test for homogeneity of variance, and were log transformed to allow the use of parametric

tests (Buzsaki and Mizuseki, 2014). Log transformed firing rates for inhibited neurons were

multiplied by -1 for ease of visualisation. Firing rates and coherence were analysed for effect of trial

type and structure using ANOVAs and post hoc LSD test where appropriate. All statistical analysis

was calculated using SPSS 22 Statistics (IBM SPSS, Somers, NY, USA). P values below 0.05 were

considered statistically significant.

Figure 3-5 Striatal neuron population respond transiently to cue onset. A & B top panels: . Firing rate responses from -3s to +9s relative to cue onset in neurons significantly affected by cue onset measured over 100ms bins a transient change in firing rate was observed in the 100ms immediately after cue onset. Colour bar chart indicate firing rate (spikes/s) per bin. A & B lower panels: The dataset was analysed to find the time interval immediately after cue onset yielding the highest number of neurons that significantly changed their firing rate in response to cue onset, compared with baseline (3 seconds before cue onset). The highest number of significantly responding neurons was found when measuring the interval from 0 – 100ms after cue onset in all four trial types in both tasks.

Analysis of behaviour: Mean response rates to Go and NoGo tones as well as mean latency

to lever press in Hit and FA trials were calculated from all the sessions included in

electrophysiological analyses (sessions where tetrode tips were positioned outside the target

structure were excluded).

46

Figure 3-6 Example spike rasters and waveforms from neurons in DLS, DMS and NAc in both tasks. Neurons in all striatal subregions in both task showed a transient increase (A, B, C, D & E) or decrease (F) in firing immediate after cue onset.

Analysis of single unit responses. Baseline neuronal activity was measured as the firing rate

(spikes/sec) during the 3 seconds before cue onset. Neurons with a baseline firing rate below 6

spikes/sec were included for further analysis and the waveform of the neurons were examined and

found to be consistent with medium spiny neurons (MSNs) (Yael et al., 2013, Berke et al.,

2004)(Figure 3-6).The highest number of neurons showing significant responding above baseline (3

seconds before cue onset) was found in the first 100 msec after cue onset in all four trial types in

both tasks (Figure 3-5): therefore this interval was chosen for further analysis. Analysis was only

47

performed on neurons that displayed a significant change (Wilcoxon’s signed rank test) in mean

firing rate (spikes per second) in at least one trial type (Hit, Miss, CR or FA).

To examine the role of coherence between striatal subregions on cue onset responses, cross-

spectrum based spike coherence between neurons in the different striatal subregions were

calculated during baseline (-3 to 0 sec before cue onset) and in the cue response phase (0 to 3

seconds after cue onset) (Halliday, 2015) (Matlab code available online at

http://www.neurospec.org).

Analysis of single unit responses. The highest number of neurons showing significant

responding above baseline (3 seconds before cue onset) was found in the first 100 msec after cue

onset in all four trial types in both tasks (Figure 3-4): therefore this interval was chosen for further

analysis. Analysis was only performed on neurons that displayed a significant change (Wilcoxon’s

signed rank test) in mean firing rate (spikes per second) in at least one trial type (Hit, Miss, CR or FA).

Trial type dependent differences in firing rate responses were analysed with all four trial types

included, using Kruskal-Wallis one-way analysis of variance. Data sets that showed significant

differences between trial types with all four trial types included were further analysed with the

Mann Whitney U test.

Event synchronisation between spikes was calculated during the 2 seconds following cue onset

(Quiroga et al., 2002). For each neuron pair, events (spikes) occurring in both neurons within the

same 100ms bin were considered to be in synchrony. Comparison of neuron synchronisation

between trial type within and between subregions was calculated using Kruskal-Wallis one-way

analysis of variance. Data sets that showed significant differences between trial types with all four

trial types included were further analysed with the Mann Whitney U test. P values below 0.05 were

considered statistically significant.

Go-NoGo Plus Go-NoGo Minus

Structure significant response Structure

Significant response

DLS 108 82 59 57

DMS 87 67 50 33

NAc 122 93 95 78

Table 3-2 Number of analysed neurons from each structure. Number of analysed neurons from each structure in the two tasks and the proportion of these neurons that significantly altered their firing rate in the first 100ms after cue onset compared with baseline.

3.3 Results

Single unit responses to cue onset and during behavioural responding were recorded in 49 sessions

from 4 rats in the Go-NoGo Plus task, and in 39 sessions from 3 rats in the Go-NoGo Minus task.

After controlling for correct placement of the tetrode tips within the targeted structures, 317 and

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204 neurons recorded during performance of the Go-NoGo Plus and Go-NoGo Minus task,

respectively, were included for further analysis (Table 3-2). Behaviour

All rats trained in either the Go-NoGo Plus or the Go-NoGo Minus task successfully learned to

discriminate between the Go and NoGo tone and maintained a high average level of discrimination,

i.e. Go trial Hit rate above 0.75 and NoGo trial FA rate below 0.25, until the end of the experiment

(Figure 3-7A and B). Latency to lever press was not normally distributed and variance and sample

size was different between Hit and FA trials, thus analysis was performed using non-parametric

tests. Mean latency to lever press was longer in FA trials than Hit trials in both tasks (p < 0.001

Mann-Whitney U test) (Figure 3-7C and D).

Examining the distribution of response latencies (Figure 3-7E and F) revealed that Hit trials peaked

within the 1st second of lever presentation, whereas the distribution of response latencies in FA

trials was spread out throughout the 4 second response interval. A higher proportion of Hit

responses were performed during the first second of lever presentation in the Go-NoGo Minus

(73.9%) compared to the Go-NoGo Plus task (43.9%) and comparison between the tasks showed

latency to respond in Hit trials to be significantly different (p < 0.001, Mann-Whitney U). Comparison

between tasks showed no significant difference in latency to respond in FA trials (p = 0.362, Mann-

Whitney U).

3.3.1 Firing rate responses

Intervals of increasing duration after cue onset were analysed to find the interval yielding the

highest number of neurons that significantly changed their firing rate in response to cue onset,

compared with baseline (3 seconds before cue onset). The analysed interval ran from 0- 100ms after

cue onset to 0-4sec after cue onset, in incremental steps of 50ms. The highest number of

significantly responding neurons was found in the first 100 msec after cue onset in all four trial types

in both tasks (Figure 3-5). A transient change in firing rate was also observed shortly after lever

presentation (4 seconds after cue onset) (Figure 3-5). This time interval was subject to a lot of

electrical artefact caused by the rat touching the lever and the pellet magazine, particularly in Hit

and FA trials. Therefore, analysis of spikes isolated from this time interval is unlikely to give a true

representation of neuronal responses to lever presentation. Thus, analysis was limited to the time

interval immediately after cue onset. Neurons showing a significant change in firing rate within the

first 100ms after cue onset, in at least one trial type, were selected for further analysis of effect of

trial type (Table 3-2). Significantly responding neurons were divided into two groups depending on

whether they increased or decreased their firing in response to cue onset. Consecutive 100 msec

windows were examined for effect of trial type during the first 4 sec of cue presentation (before

49

Figure 3-7 Behavioural performance. A & B. Mean response rates (no. hits/total no. Go trials; no. FA/total no. NoGo trials) for discrimination sessions included in the analysis of single unit responses for the Go-NoGo Plus task (49 sessions from 4 rats) and the Go-NoGo Minus task (39 sessions from 3 rats). All rats learned to discriminate between the tones and retained a high level of discrimination throughout the experiment. The dashed line represents response ratio at chance level. Inserted pie charts depict the proportion of Hit, Correct rejection (CR), Miss and False Alarm (FA) trials. C & D. Latency to lever press was significantly higher in FA trials compared with Hit trials in both tasks. *** p = 0.000 (Mann-Whitney U), error bars indicate +/- SEM. E & F. Distribution of latency to respond from presentation of the lever in 100ms bins. Whereas response time in Hit trials (grey) had a maximum below 1 second, the distribution of response latency in FA trials was spread out throughout the 4 second response interval.

50

lever extension). The highest number of neurons showing significant responding above baseline (3

seconds before cue onset) was found in the first 100ms after cue onset in all four trial types in both

tasks (Figure 3-5). Therefore, this interval was chosen for further analysis.

3.3.2 Baseline firing rates

As expected, baseline activity did not differ between trial types in either of the tasks in neither

excited neurons (F(3,381)= 0.559, p = 0.642 (Go-NoGo Plus)) and F(3,181)= 1.020, p=0.385 (Go-NoGo

Minus)) nor inhibited neurons (F(3,491)= 2.041, p= 0.107 (Go-NoGo Plus)) and F(3,337)=1.658,

p=0.176 (Go-NoGo Minus)) (Appendix – Table 1). However, in both tasks baseline firing rate

significantly differed between the striatal subregions. In the Go-NoGo Plus task, NAc exhibited a

higher baseline firing rate than DMS in neurons excited by cue onset (F(2,381)=6.374, p=0.002 -

p<0.001 (Post Hoc LSD)), and higher baseline firing rate than both DMS and DLS in neurons inhibited

by cue onset (F(2,491)=8.212, p<0.001 (ANOVA) - p<0.02(Post Hoc LSD)). In the Go-NoGo Minus,

baseline firing rate was higher in DLS than NAc in neurons excited by cue (F(2,181)=3.076, p=0.049 –

p=0.002 (Post Hoc LSD)), whereas NAc displayed a higher baseline firing rate than DMS in neurons

inhibited by cue onset (F(2,337)=3.395, p=0.035 – p=0.035 (Post Hoc LSD)). Firing rate response to

cue onset

Examining the firing rate in the first 100ms following cue onset suggested that firing rate responses

were greater in response to cue onset in trials where the rat subsequently produced an incorrect

behavioural response (Miss and FA) than in trials where the rat subsequently produced an correct

behavioural response (Hit and CR) (Figure 3-8) .

Go-NoGo Plus: In the Go-NoGo Plus task this pattern was particularly clear in inhibited

neurons which displayed significantly greater reduction of firing rate in error trials than correct trials.

Statistical analysis confirmed that both excited (F(3,381)=6.452, p<0.001) and inhibited

(F(3,491)=8.644, p<0.001) neurons responded differently to cue onset depending on trial type

(Figure 3-8A). Pairwise post hoc analysis further revealed that inhibited neurons, indeed, showed a

stronger decrease in firing rate to cue onset in error trials (Miss and FA) than correct trials (Hit and

CR) (p<0.002). Excited neurons displayed a significantly greater response in FA than CR trials

(p=0.038) and also a greater response in Miss trials than in other trial types (p<0.043).

The firing rate response to cue onset did not differ significantly between subregions in

excited neurons (F(2381)=2.494, p=0.084), but a significantly stronger inhibition was observed in

inhibited neurons in NAc compared with inhibited neurons in DMS (F(2,491)=6.820, p=0.001)

(Appendix – Table 2).

Go-NoGo Minus: A significant difference between trial type in response to cue onset was

found in both excited (F(3,181)= 9.108, p<0.001) and inhibited neurons (F(3,337)=10.376, p<0.001),

51

with the neuron population showing greater excitation (p<0.01) and greater inhibition (p<0.002) in

error trials compared with correct trials (Figure 3-8B). Firing rate response to cue onset did not differ

significantly between subregions in neither excited (F(2,181)=1.837, p=0.162) nor inhibited

(F(2,337)=20448, p=0.088) neurons (Appendix - Table 2).

Overall, the examination of firing rate suggested that the magnitude of firing rate response

to cue onset was associated with whether the rats subsequently produce a correct or incorrect

behavioural response.

Figure 3-8 Log transformed firing rate responses to cue onset. Change in firing rate in response to cue onset in striatum in the Go-NoGo Plus (A) and Go-NoGo Minus (B) task for neurons that increased (top) or decreased (bottom) their firing in the first 100ms interval after cue onset. Asterisks refer to significance levels of post hoc analysis of effect of trial type. *p < 0.05; **p < 0.01; ***p < 0.001 (LSD) Error bars indicate +/- SEM.

3.3.3 Effect of previous trial response

To further investigate the role of behavioural response on cue onset neuronal response, firing rate

responses to cue onset were analysed for effects of previous trial outcome; correct (rewarded)

behavioural response (Hit or CR) vs. incorrect (unrewarded) behavioural response (Miss or FA) trials.

Go-NoGo Plus: In excited neurons, activity was higher after previous error than after correct

response in all trial types (F(1,306)=41.427, p=0.000) with a significant interaction between previous

trial response and trial type (F(3,306)=3.039, p=0.029) (Figure 3-9A). Post hoc analysis of the

52

previous trial response x trial type interaction revealed that firing rate responses only differed

significantly between trial types following a previous incorrect response, with firing rate responses

being greater in Miss trials than CR (p=0.041) and FA (p=0.005) trials and firing rate response being

greater in Hit than FA trials (p=0.046). In inhibited neurons, previous trial type outcome did not

significantly affect firing rate to cue onset (F(1,612)=3.661, p=0.056) nor was interaction between

trial type and previous trial outcome affected (F(3,612)=0.705, p=0.549)(Figure 3-9A). Excited

neurons showed no effect of structure (F(2,306)=0.279, p=0.757) or structure x previous trial

response interaction (F(2,306)=0.254, p=0.776) (Appendix - Table 3). Although the overall firing rate

inhibition was observed in NAc (F(2,612)=7.441, p=0.001 compared to DMS (p=0.001) and DLS

(p=0.008), no interaction between structure and previous trial response was observed

(F(2,612)=1.435, p=0.239 (Appendix - Table 3).

Figure 3-9 Effect of previous trial response on cue-induced firing. Change in firing rate in response to cue onset in striatum in the Go-NoGo Plus (A) and Go-NoGo Minus (B) task in relation to correct (Hit & CR) or incorrect (Miss & FA) behavioural response in the previous trial. Asterisks refer to significance levels of post hoc analysis of effect of trial type. *p < 0.05; **p < 0.01; ***p < 0.001 (LSD) Error bars indicate +/- SEM.

Go-NoGo Minus: Cue response was greater after previous error than after correct response

in all trial types in both excited (F(1,481)=25.598, p<0.001) and inhibited (F(1,210)=41.738, p<0.001)

neurons. However, no interaction between previous trial response and trial type was found in either

excited (F(3,210)=0.670, p=0.572) nor inhibited (F(3,481)=1.750, p=0.156) neurons (Figure 3-9B).

53

Excited neurons displayed no significant difference between the two tasks (F(1,516)=0.024,

p=0.878) nor interaction between task and previous trial response (F(1,516)=2.495, p=0.115).

However, cue induced inhibition was significantly different between the two tasks

(F(1,1093)=38.537, p<0.001) and there was an interaction between task and previous trial response

(F(1,1093)=6.697, p=0.010). Post hoc analysis revealed that cue induced inhibition was greater in the

Go-NoGo Plus task than the Go-NoGo Minus task both after previous error (p=0.021) and previous

correct (p<0.001) trials (Figure 3-9B). Excited neurons showed no effect of structure

(F(2,210)=1.177), p=0.310) or previous trial response x structure interaction (F(2,210)=2.007,

p=0.137) (Appendix - Table 3). Likewise, no effect of structure (F(2,481)=1.728), p=0.120) or previous

trial response x structure interaction (F(2,481)=1.432, p=0.240) was found in inhibited neurons

(Appendix - Table 3).

Overall, firing rate response to cue onset was greater after previous incorrect response than

after previous correct response in both the Go-NoGo Plus and Go-NoGo Minus task.

3.3.4 Coherence between striatal subregions

Coherence between pairs of neurons in different striatal subregions was analysed during the

baseline (-3 – 0 seconds before cue onset) and the cue response period (0 – 3 seconds after cue

onset).

Go-NoGo Plus: Baseline coherence was strongly affected by trial type (F(3,2237)=81.421,

p<0.001), structure (F(2,2237)=6.944, p=0.001), and trial type x structure interaction (F(6,2237)=

8.052, p<0.001) (Figure 3-10A). Baseline coherence between NAc and DMS was significantly greater

before Miss trials than all other trial types (p<0.001, LSD post hoc) and greater before FA trials than

before Hit and CR (p<0.001, LSD post hoc), whereas baseline coherence was only significantly

greater before CR than Hit trials (p=0.019, LSD post hoc) (Figure 3-10A). Baseline coherence between

NAc and DLS was significantly greater before trials with incorrect behavioural response (Miss and FA)

than before trials with correct behavioural response (Hit and CR) (p<0.001, LSD post hoc) (Figure 3-

10A). Baseline coherence between DMS and DLS was significantly greater before Miss trials than all

other trial types (p<0.001, LSD post hoc) (Figure 3-10A).

Go-NoGo Minus: Baseline coherence was strongly affected by trial type (F(3,1120)=49.368,

p<0.001), structure (F(2,1120)=12.607, p=0.001), and trial type x structure interaction (F(6,1120)=

8.031, p<0.001) (Figure 3-10B). Baseline coherence between NAc and DMS was greater before FA

trials than all other trial types (p<0.013, LSD post hoc) and greater before Miss trials than before Hit

and CR (p<0.004, LSD post hoc) (Figure 3-10B). Baseline coherence between NAc and DLS was

significantly greater before trials with incorrect behavioural response (Miss and FA) than before

54

trials with correct behavioural response (Hit and CR) (p<0.001, LSD post hoc) (Figure 3-10B). Baseline

coherence between DMS and DLS was significantly greater before Miss trials than all other trial

types (p<0.001, LSD post hoc) and greater before FA trials than before Hit and CR (p<0.027, LSD post

hoc) (Figure 3-10B).

Figure 3-10 Log transformed baseline coherence between striatal subregions. . A strong association between baseline coherence (-3 to 0 sec relative to cue onset) and behavioural response after cue onset was present between all three striatal subregions. Asterisks refer to significance levels of post hoc analysis of effect of trial type. *p < 0.05; **p < 0.01; ***p < 0.001 (LSD) Error bars indicate +/- SEM.

Coherence between structures in the 3 seconds following cue onset (Appendix - Table 4)

showed the same relationship between trial types as baseline coherence. Statistical analysis

confirmed that coherence after cue onset did not differ from baseline coherence in neither the Go-

NoGo Plus (F(1,4373)=2.327, p=0.127) nor the Go-NoGo Minus (F(1,2328)=0.168, p=0.682) task.

Overall, trials with incorrect behavioural responses were associated with greater coherence

between striatal subregions compared with trials in which the cue was followed by correct

behavioural responses. Coherence both before and after cue onset was particularly high in Miss

trials between DMS and DLS in both tasks and between NAc and DMS in the Go-NoGo Plus task.

55

3.3.5 Differences between tasks

Although trial type did not affect baseline firing rates in neither the Go-NoGo Plus nor the Go-NoGo

Minus task, baseline firing rate did differ between tasks, with baseline firing rate being significantly

greater in the Go-NoGo Plus than in the Go-NoGo Minus task both in neurons excited by cue onset

(F(1, 562)=20.414, p<001) and neurons inhibited by cue onset (1, 828)=43.170. p<001).

In excited neurons, firing rate response to cue onset did not differ significantly between the

two tasks (F(1,562)=0.607, p=0.436). However, in inhibited neurons a greater reduction in firing rate

in response to cue onset was observed in the Go-NoGo Plus than the Go-NoGo Minus task

(F(1,828)=30.732. p<0.001). Similarly, when examining the role of task on the effect of previous trial

response, no effect of task (F(1,559)=36.331, p=0.853) or was found in excited neurons, whereas

significant differences in firing rate was found in inhibited neurons (F(1,1136)=77.846, p<0.001).

Baseline coherence was also significantly higher in the Go-NoGo Plus than the Go-NoGo

Minus task (F(1,3294)=22.327, p<0.001), and interaction between task, trial type and structure was

also highly significant (F(6,3294)=11.597, p<0.001).

In summary, the rats in the two tasks displayed significantly different baseline firing rates.

This task effect persisted after cue onset in inhibited neurons and was also manifested in differences

in striatal coherence between the two tasks.

3.4 Discussion

3.4.1 Behaviour

All rats maintained a high level of accuracy in their responses after tetrode implantation in both

tasks, with response rate in Go trials above 75% and NoGo trial response rate below 25% (Figure 3-

5), suggesting that rats were able to learn and retain stimulus-response-outcome contingencies and

to switch successfully between them in both tasks. In both tasks latency to respond was significantly

longer in NoGo trials than Go trials (Figure 3-7C-E). This observation is consistent with previous

studies using the standard Go-NoGo paradigms (Harding et al., 2004, Curzon et al., 1999). Latencies

to respond in Go trials were shorter in the Go-NoGo Minus task than the Go trial latencies in the Go-

NoGo Plus task, whereas no difference in latencies in the NoGo trials were observed between the

Go-NoGo Plus and Go-NoGo Minus task. In the Go-NoGo Minus task, a reward could only be won in

Go trials, as correct suppression of lever press in the NoGo trials had no programmed consequence.

Incorrect lever press to the NoGo cue in the Go-NoGo Minus only resulted in a mild error cue (a one

56

minute timeout), whereas incorrect lever press to the NoGo cue in the Go-NoGo Plus resulted in the

loss of a sugar pellet as well as the timeout. Therefore, pressing the lever in the Go-NoGo Minus may

be perceived as having a lesser adverse consequence than in the Go-NoGo Plus task, which could

create a stronger bias to lever press in the rats trained in the Go-NoGo Minus task compared with

the rats trained in the Go-NoGo Plus task. Interestingly, there was no significant difference between

tasks in latency to respond in FA trials and latency to respond was shorter in Hit compared to FA

trials in both tasks. This suggests that the differences in latency between trial types and between

tasks were not due to deficits in memory or cognitive processing, the rats in both tasks were able to

distinguish between the two stimuli-response contingencies (as suggested by the high level of

accuracy in both tasks). Rather, the rats were less likely to suppress motor initiation to Go cue in the

Go-NoGo Minus than the Go-NoGo Plus task. Studies in monkeys and humans performing a stop-

signal task have showed that movements were initiated if and only if the neural activity in motor

cortex reached a certain activation level. In the majority of neurons recorded in rhesus monkeys,

activity was less likely to reach the necessary threshold in trials where the monkey suppressed

movement compared to trials where movement was initiated (Hanes and Schall, 1996). An fMRI

study in humans showed that efficient response inhibition was associated with greater activation of

inhibitory motor areas in frontal cortex and were negatively correlated with stop-signal reaction

times (Li et al., 2006). This process has been modelled as an interactive race between “go” and

“stop” neurons, which interact though inhibitory connections to control motor initiation in response

to learned stop cues (Boucher et al., 2007, Verbruggen and Logan, 2009, Schall and Godlove, 2012).

Upon hearing the cue tone, the rats in the current study have to decide to either initiate or suppress

motor response. Due to the difference in reward contingencies between the Go-NoGo Plus and Go-

NoGo Minus task, the rats trained in Go-NoGo Plus task stand to lose more than those trained in the

Go-NoGo Minus task, if they incorrectly press the lever. The shorter response latency in Go trial in

the Go-NoGo Minus task may reflect stronger contribution from motor inhibiting units as suggested

in the interactive race model (Boucher et al., 2007, Verbruggen and Logan, 2009), leading to longer

response latencies in the Go-NoGo Plus task.

3.4.2 Baseline single unit activity

Although trial type did not affect baseline firing rates in either of the two tasks, baseline firing rate

was significantly greater in the Go-NoGo Plus than in the Go-NoGo Minus task (Appendix - Table 1)

and this task-effect persisted after cue onset in inhibited neurons. The difference in cue-induced

inhibition is likely influenced by the overall difference in baseline firing which makes comparison of

task effects on cue-induced neuronal responses difficult to interpret. Therefore, the following

57

discussion will mainly focus on trial type and structure dependent differences in firing rate response

within each task. The differences in baseline firing rates may be due to individual differences

between the rats (Cohen, 2007) which have become apparent because of the low sample size.

However, examining the impact of individual differences in neuronal responses falls outside the

scope of the current study.

3.4.3 Single unit responses to cue onset

Overall, the examination of firing rate suggested that the magnitude of firing rate responses to cue

onset in both tasks were associated with whether the rats subsequently produced a correct or

incorrect behavioural response, regardless of whether the response required motor initiation or

suppression. (Figure 3-8). This pattern was particularly clear in the Go-NoGo Minus task, where both

the excitatory and inhibitory response to cue onset was greater in error trials compared with trials

where the rat subsequently responded correctly, as well as in the inhibitory response in the Go-

NoGo Plus task, where neurons displayed significantly greater reduction of firing rate in response to

cue onset in error trials than correct trials.

Previous research in rats has shown that discrimination training in at T-maze paradigm

causes changes in firing patterns in dorsal striatum (Barnes et al., 2011, Barnes et al., 2005). During

the early stages of training, firing rate response to cues signalling location of the reward increased,

but as the rats became overtrained on the task the firing rate response shifted towards, and peaked

at, onset of motor response following the initial cue (Barnes et al., 2011, Barnes et al., 2005). These

paradigms required the rat to always initiate a motor response, and therefore, cannot elaborate on

the role of motor initiation vs. motor suppression. However, they suggest that as a task is learned

and executed with a high level of accuracy, the neuronal ensembles involved in the initial acquisition

of specific elements of the task decrease their activity to cue onset, which could account for the

lower firing rate response observed in the correct trials in the current study. Cue onset in error trials,

where the rat’s behaviour contradicts the overtrained correct stimulus-response contingency, were

associated with greater firing rate response compared with correct trials. This increase in activity

may be an effect of modulation from upstream projection areas such as medial prefrontal cortex

(mPFC). In a conditional fear paradigm, mPFC neuron that responded to initiation or inhibition of

movement showed tonically elevated activity already before the conditioned stimulus, suggesting

that tonic firing rates in these mPFC neurons may bias the rat’s choice to either initiate or inhibit

movement (Halladay and Blair, 2015). In addition, some laboratory rodents trained to run through a

maze for reward have been observed to continue to examine alternative, never rewarded, routes

through the maze, even after the task has been learned (Coppens et al., 2010, Benus et al., 1990).

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This behaviour is considered a characteristic of a reactive coping style (Coppens et al., 2010, Benus

et al., 1990), which may seem counterproductive in a laboratory setting, when stimulus-response-

outcome contingencies are kept constant, but would be adaptive in a natural environment where

stimuli and outcome may be less constant. It is possible that the comparative increase in firing rate

response in error trials, may reflect a switch from the overtrained habitual response to more goal-

directed approach, like in the early stages of acquisition, in order for the rats to test learned

stimulus-response contingencies. A recent fMRI study in humans performing a rule learning and set-

shifting task, found that whereas activity in ventral striatum, increased in the first trial after a rule

switch had been announced, the “hypothesis testing” phase (after the subject had received error

cues to indicate the direction of the new rule) was associated with increased activity in the posterior

dorsal parts of striatum (Liu et al., 2015), further suggesting that testing known stimulus-response-

outcome contingencies is associated with increased activity in distinct striatal regions.

Although single unit cue responses in the Go-NoGo Minus task and inhibitory cue responses

in the Go-NoGo Plus task were associated with whether the rats subsequently produced a correct or

incorrect behavioural response, regardless of whether the response required motor initiation or

suppression, this pattern was not as clear in excited neurons in the Go-NoGo Plus task. In the Go-

NoGo Plus task, cue induced excitation was greater in Miss trials compared to all other trial types

and excitation in FA trials was only significantly greater than in CR trials (Figure 3-8A). Previous work

has shown that the majority of neurons in DLS are movement-related and change their firing rate in

response to repeated training on movement-dependent task (Tang et al., 2007, 2009). However,

lesion studies also implicate NAc and DMS in the initiation and vigour of movement (Pisa and

Schranz, 1988, Wang et al., 2013, Hart et al., 2014). In the Go-NoGo Plus task in the current study, a

similar excitatory response was observed in Hit and FA trials, and the smallest and largest excitatory

response was found in CR and Miss trials, respectively. Whereas Hit and FA trials involved

movement, no movement was required in Miss and CR trials. A possible explanation for the

observed excitatory responses may be that in overtrained rats, striatal neurons entrained on

movement mask the effect of reward expectation or processing involved in behavioural choice. In

rats trained in a three tone Go-NoGo task, where tone onset signalled the rat to either go left, right

or stay immobile to win a reward, dopamine release in NAc was attenuated until movement was

initiated, suggesting that dopamine release within this structure was triggered by movement rather

than reward expectation (Syed et al., 2016). However, more research is needed to understand the

interaction of reward expectation, motor preparation and behavioural choice on single neuron

responses within the striatum.

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Interestingly, cue evoked responses appear highly sensitive to variation in Go-NoGo

paradigms as well as the duration of the analysed cue response period. When analysing the first

1000 ms after cue onset in a Go-NoGo paradigm where both Hit and CR responses were rewarded

(similar to the Go-NoGo Plus task in the current study), NAc single unit showed greater increase in

excited units and a smaller increase in inhibited units in CR and Miss trials (where the rats withheld

lever press) than in Hit and FA trials (where the rats pressed the lever) (Roitman and Loriaux, 2014).

The findings in this study suggest that cue responses correlate with initiation of motor response

rather than outcome expectancy. However, another study analysed NAc single unit responses during

the first 500 ms after cue onset in a traditional Go-NoGo paradigm (where only Hit responses were

rewarded – comparable to the Go-NoGo Minus task in the current study) and this study found the

greatest excitation and inhibition in Hit trials compared with other trial types (Nicola et al., 2004a).

Because only correct responses in Go trials yielded a reward in this study, the greater firing rate

response in Hit trials suggests that cue responses are influenced by both motor initiation and reward

expectation. In agreement with the findings of Roitman and Loriaux (2014) the current study found

greater excitation after cue onset in Miss than Hit trials and greater inhibition in FA than CR trials in

both tasks and greater excitation in Miss than FA trials in the Go-NoGo Plus task. However,

excitation was also greater FA than CR in both tasks in the current study whereas Roitman and

Loriaux (2014) reported the opposite. Equally, in agreement with the findings of Nicola et al. (2004a)

the current study found greater excitation in Miss and Fa trial compared with CR trials and no

significant difference between excitation in Hit and CR trials and inhibition in Miss and FA trials in

both tasks as well as no significant difference between Miss and FA trials in the Go-NoGo Minus task.

In contrast, the current study found greater excitation in Miss than Hit trials and greater inhibition in

Miss and FA trials compared with Hit trials in both tasks, where Nicola et al. (2004a) observed the

opposite. Whereas Roitman and Loriaux (2014) and Nicola et al. (2004a) both found greater

inhibition in Hit than Miss trials, they found different relationship between all examined trial type

pairs in excited neurons. In both these studies, the lever was presented at the beginning of the trial,

simultaneously with the cue indicating whether the trial was a Go or NoGo trial, whereas in the

current study the Go vs NoGo trial discrimination cue was always presented four seconds before

presentation of the lever (Figure 1A). Loriaux (2014) and Nicola et al. (2004a) analysed the first

100ms and 500ms after cue onset whereas the current study focused on the first 100ms interval as

this interval yielded the largest number of significantly responding neurons. Together these three

studies underline how subtle differences in paradigms and analysis may in turn reveal different

aspects of neuronal responses associated with behaviour.

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3.4.4 Recent behavioural experience predicts neuronal response to cue

To further investigate the role of behavioural response on cue onset neuronal response, firing rate

responses to cue onset were analysed for effects of previous trial outcome; correct (rewarded)

behavioural response (Hit or CR) vs. incorrect (unrewarded) behavioural response (Miss or FA) trials.

Excited neurons were found to display a significantly greater response to cue onset in trials following

incorrect response trials (Miss and FA) than correct response trials (Hit and CR) in both tasks (Figure

3-9). This observation was consistent across both tasks, regardless of whether correct rejection of

the NoGo cue was rewarded or not, suggesting that the neuronal response to cue onset were

modulated by recent behavioural experience rather than recently reward experience. Previous work

has also examined the role of previous response-outcome experience on neuronal responses in

striatum. In a decision making task in rats, DMS was found to code for action in previous trial during

action selection phase in current trial but not immediately after cue (Ito and Doya, 2015).In rats

trained on visual discrimination task, neurons in ventral striatum were found to modulate their

activity according to the rat’s actions in the previous trial (Kim et al., 2009). The ability to integrate

recent response-outcome experiences into the planning of future responses is imperative to

optimising behaviour in a changing environment. The observed increased firing rate response to cue

onset following error trials could indicate increased attention in trials following previous

unsuccessful behavioural response. Lesion of DMS decrease accuracy and increase response time in

attentional tasks suggesting that this structure is a vital component organisation of adaptive

behaviour particularly in task requiring flexible responding (Lindgren et al., 2013, Rogers et al.,

2001). Similarly, contralateral lesion of mPFC and NAc (Christakou et al., 2001) or contralateral

inactivation of PrL and DMS (Baker and Ragozzino, 2014) have been show to disrupt planning and

responding in tasks that require switching between multiple stimulus-outcome contingencies. A

recent study found greater firing rate response to cue onset in a directional choice task in rat frontal

cortex after previous correct trial than error trial (Yuan et al., 2015) suggesting that recent

experience is associated with firing rate response to cue onset in both cortex and striatum, although

with opposite effect.

Whereas cue onset in trials following previously correct response showed no differences

between trials in either of the tasks, in the Go-NoGo Plus task trials following previous error

responses were greater to Go cues compared with NoGo cues (Figure 3-9A), suggesting greater

striatal sensitivity to cues following recent incorrect and unrewarded responses which is consistent

with previous findings (Stalnaker et al., 2012, Oyama et al., 2015).

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3.4.5 Striatal subregions collectively respond to cue onset

Whereas previous studies have recorded single units in NAc in rats performing Go/NoGo tasks

(Roitman and Loriaux, 2014, Nicola et al., 2004a), this study simultaneously recorded single units in

DMS, DLS and NAc to enable comparison of cue responses between these structures. In the current

study, NAc was found to have a greater baseline firing rate compared with dorsal striatum (Appendix

– Table 1) and this difference was maintained after cue onset in inhibited neurons in the Go-NoGo

Plus task, where NAc showed stronger inhibition than DMS (Appendix - Table 2). However, the

current study found no interaction of structure x trial type, suggesting that although overall firing

activity differed between NAc and dorsal striatum, the three striatal subregions did not differ in their

immediate response to cue onset in the current study.

Lesion studies suggest consistent functional differences between striatal subregions (Devan

et al., 2011, Balleine et al., 2009). However, task related information is likely integrated across the

striatal subregions in order to collectively organise adaptive behavioural output and the level of

functional segregation and coordination between subregions may depend on the nature and

complexity of the task (Liljeholm and O'Doherty, 2012). In the current study no structure dependent

differences in cue responses were observed. However, a recent study recording single units in DLS,

DMS and ventral striatum in rats during performance of a choice task showed DLS to respond more

strongly to cue onset than DMS and ventral striatum (Ito and Doya, 2015). In the above experiment

cues always required the rat to initiate a motor response and reward was delivered probabilistically,

whereas in the current study the rat had to either initiate or inhibit a motor response to successfully

complete a trial. Together, these studies indeed suggest that the striatal segregation depend on

distinct elements of the task such as motor requirement and outcome contingencies.

The similarity in cue response between the striatal subregions may suggest that the joint

striatal response to cue in the current task was coordinated by modulatory input from outside the

striatum. MPFC would be a likely candidate area to be exerting such higher level control. Strong

afferents from mPFC connect to both ventral and dorsal regions of striatum (Gabbott et al., 2005,

Groenewegen et al., 1999, Hart et al., 2014, Balleine et al., 2007, Balleine and O'Doherty, 2010,

Heidbreder and Groenewegen, 2003) and several studies have shown mPFC to be involved in the

organisation and planning of complex behaviour (Groenewegen and Uylings, 2000, Dalley et al.,

2004). The findings in this study further emphasise the relevance of recording activity in multiple

structures within relevant neuronal networks in behaving animals to fully understand the role of

these structures in modulating outcome oriented behaviour.

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3.4.6 Coherence between striatal subregions

When examining the coherence between neurons in different subregions, coherence in both tasks

was found to vary significantly between trial types in the three seconds prior to cue onset (as well as

in the three seconds following cue onset) between neurons in the three striatal subregions (Figure 3-

10) although firing rates showed no effect of trial types during the same time interval. In freely

moving rats, theta band coherence within the striatum and between primary motor cortex and

striatum has been found to be greater during periods of wakefulness than when the rats were

resting (Lepski et al., 2012). In the current study, Miss trials (where the rats failed to respond to the

Go cue) were associated with greater pre-stimulus coherence between striatal subregions compared

with correct response trials (Figure 3-10). Increased coherence may reflect low levels of attention to

external stimuli, causing the rat to miss the cue (Gusnard and Raichle, 2001, Herzog et al., 2014).

Several studies in humans and monkeys have demonstrated that particularly alpha band coherence

between different cortical regions before cue onset affects stimulus detection (Boly et al., 2007,

Sadaghiani et al., 2010, Forstmann et al., 2010, Melloni et al., 2007, Shulman et al., 2002), with some

studies reporting greater coherence to be associated with low detection (Hanslmayr et al., 2007,

Mazaheri et al., 2011, van Dijk et al., 2008), and others report stronger coherence between

structures to predict high stimulus detection (Boly et al., 2007, Super et al., 2003). More recently, a

study in rats performing an auditory detection task found increased theta, alpha and beta band

coherence between frontal and parietal cortex before cue onset in trials where the rats failed to

detect the tone (Herzog et al., 2014), whereas another study in rats found increased phase

synchrony between single unit activity to local field potentials (LFPs) in prelimbic cortex and anterior

cingulate cortex before cue onset in Hit trials compared to Miss trials in a sustained visual attention

task (Totah et al., 2013). These studies use a variety of methods to quantify interaction between

neuronal populations and focus on different combinations of mainly cortical areas. While some

studies show that coherence between their target areas enhance stimulus detections, other studies

show that activity in their target areas decrease stimulus detection, they all demonstrate a

relationship between pre-stimulus coherence and stimulus detection. Fluctuation in activity and

network connectivity has long been linked to attentional state and previous work suggests that low

detection rates following high coherence likely represent functional inhibition within the target

network diverting attention away from external stimuli to focus attention on internal

representations such as working memory (Hanslmayr et al., 2007, Mazaheri et al., 2011, Cooper et

al., 2003, Gusnard and Raichle, 2001, van Dijk et al., 2008). Strong afferents from medial prefrontal

cortex (Gabbott et al., 2005, Groenewegen et al., 1999, Hart et al., 2014, Balleine et al., 2007,

Balleine and O'Doherty, 2010, Heidbreder and Groenewegen, 2003), motor and sensory cortex (Van

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Waes et al., 2012, Redgrave et al., 2011), and thalamus connect to both ventral and dorsal regions of

striatum. Thus, the striatum is intricately connected to both task-positive and task-negative

networks implicated in the regulation of attention to external stimuli (Sadaghiani et al., 2010). The

high prestimulus coherence between striatal subregions observed in Miss may originate from

modulatory input from task-negative regions of cortex or thalamus.

Overall, prestimulus coherence was greater in FA trials as well as in Miss trials compared

with correct response trials, suggesting that coherence is associated with future behavioural choice.

However, coherence between NAc and DMS and between DMS and DLS differed significantly

between FA and Miss trials in both tasks, suggesting that striatal prestimulus coherence does not

merely code for future correct or incorrect behavioural responses (Figure 3-10). Furthermore, the

relationship between Miss and FA trial coherence also differed greatly between structure pairs, with

Miss coherence being higher than FA coherence between DMS and DLS but similar between NAc and

DLS in both tasks, whereas Miss trial coherence between NAc and DMS being higher than FA trial

coherence in the Go-NoGo Plus task but lower in the Go-NoGo Minus task. This suggests that

coherence between different striatal subregions contributes differently to the modulation of

behavioural choice. However, more research is needed to clarify the role of striatal coherence on

reward directed choice behaviour.

Retrograde and anterograde tracing studies suggest that the information flow within

striatum runs in a spiral from NAc through DMS to DLS (Haber et al., 2000). Coherence between NAc

and dorsal striatum may facilitate integration of motivational aspects associated with NAc function

(Tricomi and Lempert, 2015, Basar et al., 2010, Baldo and Kelley, 2007)with goal directed processes

(Shiflett et al., 2010, Shan et al., 2014, Yin et al., 2005, Balleine et al., 2009, Devan et al., 2011,

Hilario et al., 2012) in DMS and with motor processes behaviour (Devan et al., 2011, Costa et al.,

2004, Mowery et al., 2011, Schmitzer-Torbert et al., 2015, Fanelli et al., 2013, Yin et al., 2006) in DLS

in order to execute the task. Previous research suggests that both dorsal and ventral regions of

striatum contribute to the processing and execution of complex behaviour (Liljeholm and O'Doherty,

2012) and high trial type dependent coherence between the striatal subregions observed in the

current study further suggest that cue responses in this current task to be maintained by interaction

between striatal subregions. The high striatal prestimulus coherence observed in Miss trials is well in

accord with previous studies examining the role of cortical coherence and stimulus detection (Boly

et al., 2007, Sadaghiani et al., 2010, Forstmann et al., 2010, Melloni et al., 2007, Shulman et al.,

2002, Herzog et al., 2014) and the coherence between striatal regions may reflect coherence in

cortical areas projecting to the striatum, with mPFC being a prime candidate for the exertion of such

top down control (Riga et al., 2014, Balleine et al., 2009, Christakou et al., 2004, Stefanik et al., 2015,

64

Baker and Ragozzino, 2014, Thorn and Graybiel, 2014). Future research into the influence of mPFC

modulation on striatal processing during complex behavioural tasks may offer insight into the

precise role of cortico-striatal regulation of complex reward-directed behaviour.

3.4.7 Conclusions

Overall, firing rate response to cue onset was greater in error trials compared with trials with

subsequent correct response. This finding emphasises the importance of contribution of subject’s

choice when analysing effect of reward-paired cues on neuronal activity. We hypothesised that firing

rate response to CR trials compared to other trial types would be different between tasks, as this

was the only trial type for which the outcome differed between tasks. However, firing rate response

to cue onset did not appear to be influenced by differences in reward expectancy. Error responses

during performance of an overtrained task may signal trials in which the animal tests the consistency

of the learned stimulus response contingencies and thus engage striatal networks associated with

goal-directed rather than habitual behaviour. In both tasks, cue induced excitation and inhibition in

Miss trials were greater than in trials with correct behavioural response. However, more research is

needed to elucidate the role of motor- and reward related aspects of neuronal responses associated

with behaviour. Firing rate response to cue onset was significantly greater in trials following error

trials, which may signify increased attention in trials following previous unsuccessful behavioural

response.

Although overall firing rate differed between striatal structures, no interaction between

structure and trial type was found, suggesting that although overall firing activity differed between

NAc and dorsal striatum, the three striatal subregions did not differ in their immediate response to

cue onset. The similarity in cue response between the striatal subregions may suggest that the joint

striatal response to cue in the current task was coordinated by modulatory input from outside the

striatum.

Coherence between striatal subregions was found to be particularly high before and during

Miss trials. Fluctuations in prestimulus coherence between cortical regions have been linked to

attention and the high prestimulus coherence between striatal subregions observed in Miss trials

suggests that the striatum may originate from modulatory input from task-negative regions of cortex

or thalamus. Overall, prestimulus coherence was greater in FA trials as well as in Miss trials

compared with correct response trials, suggesting that coherence is associated with future

behavioural choice. However, the relationship between coherence in Miss and FA trials differed

significantly between structures and tasks, suggesting that striatal prestimulus coherence does not

merely code for future correct or incorrect behavioural responses and that the striatal subregions

65

contribute differently to the modulation of behavioural choice. However, more research is needed

to clarify the role of striatal coherence on reward directed choice behaviour.

Previous research suggests that both dorsal and ventral regions of striatum contribute to the

processing and execution of complex behaviour (Liljeholm and O'Doherty, 2012) and the current

study further suggest that cue responses in this task to be maintained by interaction between

striatal subregions. However, further research on the interaction between striatal subregions during

complex behaviour is warranted to illuminate the role of such interactions on reward-directed

choice behaviour.

The high striatal prestimulus coherence observed in Miss trials is well in accordance with

previous studies examining the role of cortical coherence and stimulus detection (Boly et al., 2007,

Sadaghiani et al., 2010, Forstmann et al., 2010, Melloni et al., 2007, Shulman et al., 2002, Herzog et

al., 2014) and the coherence between striatal regions may reflect coherence in cortical areas

projecting to the striatum, with mPFC being a prime candidate for the exertion of such top down

control (Riga et al., 2014, Balleine et al., 2009, Christakou et al., 2004, Stefanik et al., 2015, Baker and

Ragozzino, 2014, Thorn and Graybiel, 2014). Future research into the influence of mPFC modulation

on striatal processing during complex behavioural tasks may offer insight into the precise role of

cortico-striatal regulation of complex reward-directed behaviour.

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Chapter 4: Corticostriatal contribution to reward-directed behaviour

4.1 Introduction

4.1.1 Prelimbic cortex and behavioural control

Medial Prefrontal cortex (mPFC) plays a crucial role in the organisation of previously acquired

information and in subsequent integration of this information into the planning and execution of

complex behaviour (Groenewegen and Uylings, 2000, Dalley et al., 2004). MPFC is thought to exert

an influence on appetitive behaviour (Riga et al., 2014) via top down control of downstream areas in

nucleus accumbens (NAc) (Riga et al., 2014, Balleine et al., 2009, Christakou et al., 2004, Stefanik et

al., 2015) and medial parts of dorsal striatum (Christakou et al., 2001, Baker and Ragozzino, 2014,

Thorn and Graybiel, 2014). Whereas infralimbic cortex (IL), in ventral mPFC, is associated with habit

formation (Maier, 2015, Smith and Graybiel, 2013), prelimbic cortex (PrL), in dorsal mPFC, is involved

in goal-directed behaviour and complex behaviour that requires flexible switching between different

context-dependent strategies (Riga et al., 2014, Heidbreder and Groenewegen, 2003, Funamizu et

al., 2015).

Lesion of PrL impairs acquisition but not expression of instrumental learning tasks (Ostlund

and Balleine, 2005, Tran-Tu-Yen et al., 2009, Dalley et al., 2004) and PrL inactivation does not impair

reward seeking behaviour (Burgos-Robles et al., 2013), suggesting that although PrL is involved in

the regulation of appetitive behaviour it may not be directly involved in reward seeking. PrL lesion

impairs choice accuracy, specifically by increasing perseverance errors (Dalley et al., 2004) and also

cause impairment in tasks with delayed response contingencies (Heidbreder and Groenewegen,

2003), suggesting that PrL is not directly involved in storing and maintaining information long term

but more likely contributes to organisation and planning of flexible behaviour, based on previously

acquired information (Dalley et al., 2004, Hart et al., 2014).

Several studies implicate PrL in the encoding of stimulus-response-outcome associations and

in successful switching between behavioural strategies depending on context (Mulder et al., 2003,

Hosking et al., 2015, Halladay and Blair, 2015, Moorman and Aston-Jones, 2015). When rats were

trained in a two-lever Go-NoGo task, PrL firing rate was significantly affected during correct lever

press after the light cue and subsequent entry into the reward receptacle (Mulder et al., 2003).

However, neither light cue alone, nor incorrect lever press or nose poke (which were never

rewarded) was associated with a change in firing rate (Mulder et al., 2003). This suggests that PrL

contribute to the encoding of stimulus-response-outcome associations and may be involved in the

formation and control of behavioural strategies (Mulder et al., 2003).

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In a cognitive effort task in rats, where the rats had to choose between performing an low

effort task (with low attentional demands) for low reward and a high effort task (with high

attentional demands) for high reward, inhibition of PrL increased the choice of low effort task and

increased errors in both tasks. This suggests a role for PrL in attentional processes, particularly when

the task involves a choice between behavioural strategies (Hosking et al., 2015). In a conditional fear

paradigm, mPFC neurons that responded to initiation or inhibition of movement showed tonically

elevated activity already before the conditioned stimulus, suggesting that tonic firing rates in these

mPFC neurons may bias the rat’s choice to either initiate or inhibit movement (Halladay and Blair,

2015), further implicating PrL in the planning and execution of behavioural strategies.

In a two lever Go-NoGo task, where rats were trained to press one lever in response to a

visual cue to obtain a reward but where responding to another visual cue by pressing the other lever

had no consequence, PrL firing rate was affected by both reward-paired and no-reward-paired cues

(Moorman and Aston-Jones, 2015). However, the strength of PrL signalling immediately after cue

onset was greater when the rat subsequently responded correctly (Hit and correct rejection (CR)

trials) compared to trials where the rat responded incorrectly (Miss and false alarm (FA) trials)

(Moorman and Aston-Jones, 2015), suggesting that PrL incorporates contextual information into the

regulation of behaviour, rather than strictly promote or inhibit behavioural responding.

4.1.2 Prelimbic modulation of striatal processes

PrL sends strong projections to both core and shell of the NAc (Ding et al., 2001, Hart et al., 2014,

Gabbott et al., 2005, Balleine et al., 2009, Groenewegen et al., 1999, Balleine et al., 2007,

Heidbreder and Groenewegen, 2003) as part of the limbic cortico-striatal-thalamic circuit and to

dorsomedial striatum (DMS) as part of the associative cortico-striatal-thalamic circuit (Gabbott et al.,

2005, Groenewegen et al., 1999, Hart et al., 2014, Balleine et al., 2007, Balleine and O'Doherty,

2010, Heidbreder and Groenewegen, 2003).

The limbic cortico-striatal-thalamic circuit maintains motivational aspects of reward-seeking

behaviour (Yin et al., 2008, Balleine, 2005) and interaction between mPFC and NAc is likely to be

involved in the updating of response-outcome contingencies. Neither lesion of mPFC nor NAc core

alone affect the ability of rats to stop an already initiated motor response in a stop-signal reaction

time task (Eagle and Robbins, 2003). Similarly, NAc core lesions alone had no significant effect on

incorrect responding in the 5 choice serial reaction time task (5-CSRTT) (Christakou et al., 2004).

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Contralateral2 lesions of mPFC and NAc core increased the number of incorrect responses in the 5-

CSRTT but only in trials following trials with correct response, whereas trials following incorrect

response were unaffected (Christakou et al., 2004). Furthermore, these errors were unaffected my

manipulation of stimulus discriminability (Christakou et al., 2004), suggesting that these errors were

not attributable to attentional impairment but rather that disruption of mPFC-NAc core connectivity

interfered with the planning of responding in subsequent trials immediately after positive

reinforcement. In a recent study, single units were recorded in either mPFC or NAc in rats during

performance in the 5-CSRTT. In this study ramping activity was observed in both structures between

nose poke initiated trial start and subsequent nose poke into one of the 5 ports, regardless of

whether the second nose poke was correct, incorrect or premature, whereas ramping activity was

absent in mPFC and reduced in NAc in omission trials when trial start was not followed by a nose

poke (Donnelly et al., 2015). This suggests a role for both structures in response initation, with the

ramping activity representing internal time representation, that begins too early in trials with

premature responses, and the absence of ramping activity in mPFC in conjunction with reduction in

NAc in omission trials, suggests this activity may be correlates with task management, possibly

imposed top down from mPFC to NAc (Donnelly et al., 2015).

As part of the associative cortico-striatal-thalamic circuit, communication between mPFC and DMS

likely modulate cue-guided behavioural shifting during tasks that require discrimination between

sets of different stimulus-outcomes, particularly when context increases attentional demands

(Christakou et al., 2004, Christakou et al., 2001, Baker and Ragozzino, 2014). In humans, mPFC –

DMS synchronisation have been found to predict successful performance in an active coping task,

where mild electric shock was given in some error trials (Collins et al., 2014). In rats, contralateral

lesions of mPFC and DMS increased the number of errors and latency to respond in the 5-CSRTT, but

left locomotion and Pavlovian approach behaviour intact, while premature responses returned to

control level when the duration of the stimulus was increased. Taken together, these observations

suggest impairment of attentional rather than motivational processes (Christakou et al., 2001).

Interaction between PrL and DMS may coordinate switching between behavioural strategies

in response to context. In a conditional discrimination task where rats relied on visual cues to

determine which arm in a maze was rewarded in the following block of 3-5 trials, inactivation of

DMS increased perseverance errors, i.e. the rat required more trials within a new block to switch

2 As mPFC projections to striatum are primarily ipsilateral, unilateral lesion of mPFC and NAc leaves the

connection between these structures unimpaired in one hemisphere, whereas contralateral lesions disconnects the structures in both hemispheres (Christakou et al. 2001).

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arm or completely failed to switch arm, whereas inactivation of PrL led to turn bias, where rats

consistently chose the right or left arm, ignoring visual cues (Baker and Ragozzino, 2014). In this

study, ipsilateral inactivation of PrL and DMS together did not affect performance. However,

contralateral inactivation of communication between structures impaired performance specifically in

trials when rats have to switch from one arm to another, whereas performance within trial blocks,

where no switching was required, was unaffected (Baker and Ragozzino, 2014). This suggests that

ipsilateral communication between PrL and DMS modulate cue-guided behavioural shifting during

tasks that require discrimination between sets of different stimulus-outcomes. In a choice task,

where trials switched between variable and fixed reward conditions, single neurons in both PrL and

DMS were found to track action-reward associations across trial types (Funamizu et al., 2015),

further implicating both Prl and DMS processing in the ability to switch behaviour in response to

changes in context-outcome associations.

Dorsolateral striatum (DLS) receives projections from primary motor and sensory cortex (Van Waes

et al., 2012, Redgrave et al., 2011) as part of the sensory-motor cortico-striatal-thalamic circuit and

labelling studies suggest that there are no direct anatomical connection between these structures

(Voorn et al., 2004). Thus, the research on PrL to striatum interaction has so far focused on

interaction effects with DMS and NAc. However, complex behavioural strategies often require

repeated training over time making the behaviour increasingly automated, drawing on increasing

contribution from DLS (Dias-Ferreira et al., 2009, Balleine et al., 2009, Yin et al., 2005, Hilario et al.,

2012). Complex behavioural tasks that involve switching between behavioural strategies in response

to context likely rely on DMS processing (Funamizu et al., 2015) as well as DLS processing (Balleine et

al., 2009, Tang et al., 2009) even in over-trained animals. Successful management of a task with

multiple stimulus-response-outcome contingencies requires some level of executive control of DMS

vs. DLS modulation of the behavioural output (Baker and Ragozzino, 2014, Funamizu et al., 2015,

Riga et al., 2014, Heidbreder and Groenewegen, 2003, Christakou et al., 2004). MPFC, with its

extensive efferents to both ventral and dorsal striatum is in a key position to exert such control

(Christakou et al., 2001, Baker and Ragozzino, 2014, Thorn and Graybiel, 2014), and projections

between dorsal and ventral part of mPFC allows PrL and IL to coordinate mPFC regulation of habitual

vs. goal-directed striatal processes (Riga et al., 2014, Moorman and Aston-Jones, 2015). However, to

fully elucidate mPFC contribution to striatal processing during complex behaviour a more integrated

approach is needed, ideally examining mPFC interaction with distinct dorsal and ventral striatal sub-

regions simultaneously.

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4.1.3 Study aims

Corticostriatal communication from PrL to both ventral and dorsal striatum likely play a role in

appetitive behaviour, particularly when tasks are demanding and involve shifts between several

stimulus-response-outcome contingencies (Baker and Ragozzino, 2014, Funamizu et al., 2015, Riga

et al., 2014, Heidbreder and Groenewegen, 2003). Successful behaviour in such tasks likely require

executive control exerted by discrete mPFC regions, such as PrL, onto striatal subregions, most

notably NAc and DMS (Christakou et al., 2001, Baker and Ragozzino, 2014, Thorn and Graybiel,

2014).

The study presented in this chapter examined the contribution of PrL single unit activity and

synchronisation between PrL and DMS, DLS and NAc during execution of two comparable

conditioned discrimination tasks; the Go-NoGo Plus and the Go-NoGo Minus task. In both tasks rats

were trained to press a lever in response to an auditory Go cue and to suppress responding to an

auditory NoGo cue. Correct lever press was rewarded with a sugar pellets in both tasks, whereas

correct response suppression (in NoGo trials) was only rewarded in the Go-NoGo Plus task (Figure 3-

1). In both tasks, the rats have to discriminate between stimulus-outcome contingencies and initiate

or suppress motor response accordingly, enabling us to study mPFC-striatal interaction in response

to such cognitive demands. In the standard Go-NoGo (Minus) task cues signalling reward availability

also signal to the animal to make a motor response: therefore, the modified Go-NoGo Plus task was

introduced to enable separation of motor and reward component of cue evoked responses.

Hypothesis

Because mPFC projects directly to DMS and NAc but not to DLS, greater task-related

synchronisation was expected between PrL and DMS and PrL and NAc compared with

synchronisation between PrL and DLS in response to trial onset cues.

4.2 Methods

The data presented in this chapter were collected from the same animals as in chapter 3. Therefore

the majority of the methods employed are identical, and are only summarised briefly. Full details of

the methodology can be found in Chapter 3.

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4.2.1 Animals

Male Lister Hooded rats (n = 7; Charles River, Cambridge, UK) weighing 225-250g on arrival were

kept on reversed light/dark cycle (12:12h; lights on 19.00h). Animals had access to water ad libitum

and access to food for at least 2h per day. All experiments were carried out under institutional

ethical approval and with project and personal licence approved by the UK Home Office.

4.2.2 Apparatus

All behavioural training and testing was conducted in four identical operant chambers, comprising a

metal grid floor, with a magazine for delivery of sugar pellets and a retractable lever positioned to

the left of the magazine. A light was positioned immediately above each and a speaker was

positioned above the magazine and a house light was positioned at the top of the opposite wall of

the chamber (for full details see paragraph 3.2.2). Electrophysiological recordings were carried out in

a similar chamber, with minor modifications to facilitate recordings (Plexiglas cover and custom

made food receptacle: see paragraph 3.2.2 for details).

4.2.3 Behavioural training

Behavioural procedures were as described in Chapter 3 – paragraph 3.2.3). Briefly, rats were trained

to either respond (Go trials) or suppress (NoGo trials) responding to auditory cues of different

frequencies (1 vs. 10 kHz (75dB): counterbalanced). Each trial started with the presentation of either

the Go or NoGo tone. After 4 seconds the lever was presented allowing the rat a 4 second response

interval to press the lever. Upon lever press, or at the end of the 4 second response interval, the

lever retracted and the tone was switched off. There was a 60 second inter trial interval (ITI)

between trials (Figure 3-1). In the Go-NoGo plus task rats (n = 4) were rewarded with a sugar pellet

for both correct lever press (Hit) and correct omission of lever press (Correct Rejection) trials. In the

Go-NoGo minus task (n = 3) only correct lever press (Hit) trials were rewarded whereas correct

rejections had no programmed consequence. In both tasks incorrect lever press (False Alarm)

resulted in a 60 second time-out with lights out. Incorrect omission of lever press (Miss) had no

programmed consequence (Figure 3-1). The rats were considered to be discriminating at criterion

when Hit rate (number of Hit trials divided by number of Go trials) was above 0.8 and False Alarm

rate (number of False Alarm trials divided by number of NoGo trials) below 0.25 within a session for

3 consecutive sessions.

4.2.4 Surgery

Once rats were discriminating at criterion, a, affixed to Neuralynx drives, was implanted to target PrL

in the left hemisphere at +3.2AP, +1.1ML and -2.6DV. In addition 7 tungsten tetrode recording

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electrodes were implanted to target DLS, DMS, NAc shell, NAc core(for striatal coordinates, see

Table 3-1). The tetrodes were sealed with paraffin wax and the implant was built up using layers of

light curing dental cement. A silver wire inserted into the cerebellum served as a ground. Surgical

procedures and post-operative care are described in full in Chapter 3 – paragraph 3.2.5).

4.2.5 Electrophysiological recordings

15-30 minutes prior to each recorded session, each tetrode was lowered approximately 0.125mm

(corresponding to a 180 degree turn of the drive screw) to ensure that different neurons were

recorded in each session. The rat was connected through a flexible wire, allowing unimpaired

movement, to a 32 channel head stage (Plexon Inc., Dallas, TX, USA) immediately before recording

and placed in an operant chamber placed in a sound-attenuated aluminium-plated box, which

served as a Faraday cage. During the discrimination task, wideband signals were acquired

continuously at a sampling rate of 25000hz via an op-amp based head-stage amplifier (HST/32o25-

36P-GR, 1x gain, Plexon Inc., Dallas, TX, USA), and passed through a preamplifier (PBX2/32wb, 1000x

gain; Plexon Inc., Dallas, TX, USA).

Filtering, artefact removal, spike sorting and synchronisation of spike activity with behaviour

were carried out using Matlab-based routines, as previously described (paragraph 3.2.6) and

statistical analysis was calculated using SPSS 22 Statistics (IBM SPSS, Somers, NY, USA).

Figure 4-1 Verification of tetrode placement Tetrode placements were verified visually while cutting the frozen brains in 30μm slices on a cryostat. Only sessions where the tetrode tips were in the PrL (marked in red) were included in the analysis of firing rate responses to cue onset.

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4.2.6 Verification of tetrode placement

After the last recording session, the rats were anaesthetised, perfused and the brain removed using

the same procedure as described in chapter 3, Methods. Tetrode placement was verified visually

while cutting the perfused frozen brains in 30μm slices on a cryostat (Figure 3-4 and Figure 4-1). For

those tetrodes, for which precise location of the tip could not be identified visually, a presumed

position was calculated using the same criteria as described in chapter 3, Methods. One tetrode

targeting DMS (Go-NoGo Minus task), one tetrode targeting PrL and three tetrodes targeting NAc

(Go-NoGo Plus task) were excluded from the analysis due to incorrect placement.

4.2.7 Statistical analysis

Behavioural and electrophysiological data were not normally distributed and did not pass Levine’s

test for homogeneity of variance, and were log transformed to allow the use of parametric tests

(Buzsaki and Mizuseki, 2014). Log transformed firing rates for inhibited neurons were multiplied by -

1 for ease of visualisation. Firing rates and coherence were analysed for effect of trial type and

structure using ANOVAs and post hoc LSD test where appropriate. All statistical analysis was

calculated using SPSS 22 Statistics (IBM SPSS, Somers, NY, USA). P values below 0.05 were

considered statistically significant.

Analysis of behaviour: Mean response rates to Go and NoGo tones as well as mean latency

to lever press in Hit and FA trials were calculated from session included in electrophysiological

analyses (sessions where tetrode tips where positioned outside the target structure were excluded).

Analysis of single unit responses. The highest number of significantly responding neurons

compared with baseline (3 seconds before cue onset) was found in the first 100 msec after cue onset

in all four trial types in both tasks, wherefore this interval was chosen for further analysis. Analysis

was only performed on neurons that displayed a significant change (Wilcoxon’s signed rank test) in

mean firing rate (spikes per second) in at least one trial type (Hit, Miss, CR or FA). Baseline neuronal

activity was measured as the firing rate (spikes/sec) during the 3 seconds before cue onset. Neurons

with a baseline firing rate below 6 spikes/sec were included for further analysis.

To examine the role of coherence between striatal subregions on cue onset responses, cross-

spectrum based spike coherence between neurons in the different striatal subregions were

calculated during baseline (-3 to 0 sec before cue onset) and in the cue response phase (0 to 3

seconds after cue onset) (Halliday, 2015) (Matlab code available online at

http://www.neurospec.org).

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4.3 Results

Single unit responses to cue onset and during behavioural response were recorded in 49 sessions

from 4 rats in the Go-NoGo Plus task, and in 39 sessions from 3 rats in the Go-NoGo Minus task.

After controlling for correct placement of the tetrode tips within the targeted structures, 317 and

204 neurons in striatum (for details on included neurons from each sub-region, see Table 3-2) and 48

and 70 neurons in PrL (Table 4-1) recorded during performance of the Go-NoGo Plus and Go-NoGo

Minus task, respectively, were included for further analysis.

Go-NoGo Plus Go-NoGo Minus

Analysed neurons

significant response

Analysed neurons

Significant response

48 29 70 51

Table 4-1 Number of analysed and significantly responding units in PrL Number of analysed neurons recorded from PrL in the two tasks and the proportion of these neurons that significantly altered their firing rate in the first 100ms after cue onset compared with baseline.

4.3.1 Behaviour

Behavioural results are reported Chapter 3. In summary, all rats trained in either the Go-NoGo Plus

or the Go-NoGo Minus task successfully learned to discriminate between the Go and NoGo tone and

maintained a high average level of discrimination, i.e. Go trial Hit ratio above 0.75 and NoGo trial FA

ratio below 0.25, until the end of the experiment (Figure 3-7A & B).

4.3.2 Firing rate response to cue onset

Intervals of increasing duration after cue onset was analysed to find the interval yielding the highest

number of neurons that significantly changed their firing rate in response to cue onset, compared

with baseline (3 seconds before cue onset). The analysed interval ran from 0- 100ms after cue onset

to 0-4sec after cue onset, in incremental steps of 50ms. The highest number of significantly

responding neurons was found in the first 100 msec after cue onset in all four trial types in both

tasks (Figure 3-5 & 4-2). Therefore, neurons showing a significant change in firing rate within the first

100ms after cue onset, in at least one trial type, were selected for further analysis of effect of trial

type (Table 4-1). Significantly responding neurons were divided into two groups depending on

whether they increased or decreased their firing in response to cue onset. Consecutive100ms

windows were examined for effect of trial type during the first 4 seconds of cue presentation (before

lever extension). Trial type dependent differences in firing rate response to cue onset were only

statistically significant in the PRL within the first 100ms interval after cue. Therefore, only data from

the interval “0-100ms” from cue onset is presented here (Figure 4-2).

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Figure 4-2 PrL neuron population respond transiently to cue onset. A & E. Firing rate responses from -3s to +9s relative to cue onset in neurons significantly affected by cue onset measured over 100ms bins a transient change in firing rate was observed in the 100ms immediately after cue onset in both tasks. Colour bar chart indicate firing rate (spikes/s) per bin. B & F. The highest number of significantly responding neurons was found when measuring the interval from 0 – 100ms after cue onset in all four trial types in both tasks. C & G. Example waveforms from PrL neurons included in the analysis. D & H. Example spike rasters from neurons included in the analysis.

4.3.3 Striatal response to cue onset

The effect of trial type on single unit response to cue onset in DLS, DMS and NAc was

reported in chapter 3. In summary, a stronger response to cue onset was observed in in error trials

compared with trials where the rat subsequently responded correctly (Figure 3-8). Furthermore,

firing rate responses to cue onset were greater after previous incorrect response than after previous

correct response in both tasks. Coherence between the striatal subregions was found to be higher in

the 3 seconds before cue onset of trials where the rat produced an incorrect behavioural response

compared with trials in which the cue was followed by correct behavioural responses. Coherence

both before and after cue onset was particularly high in Miss trials between DMS and DLS in both

tasks and between NAc and DMS in the Go-NoGo Plus task.

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4.3.4 Prelimbic cortex baseline firing rates

Baseline activity did not differ between trial types in either of the tasks in neither excited neurons

(F(3,52)= 1.897, p = 0.142 (Go-NoGo Plus)) and F(3,73)= 1.303, p=0.280 (Go-NoGo Minus)) nor

inhibited neurons (F(3,48)= 0.472, p= 0.702 (Go-NoGo Plus)) and F(3,89)=0.450, p=0.718 (Go-NoGo

Minus)) (Appendix – Table 1).

4.3.5 Prelimbic cortex response to cue onset

No trial type dependent differences in firing rate response to cue onset was observed in the Go-

NoGo Plus task in excited (F(3,52)= 0.624, p = 0.603) or inhibited (F(3,48)= 2.201, p= 0.100) neurons

(Figure 4-3A).

Figure 4-3 Log transformed firing rate responses to cue onset in PrL. Change in firing during the first 100ms after cue onset in PrL in the Go-NoGo Plus (A) and Go-NoGo Minus (B) task for neurons that increased (top) or decreased (bottom) their firing in response to cue onset. Asterisks refer to significance levels of post hoc analysis of effect of trial type. *p < 0.05; **p < 0.01; ***p < 0.001 (LSD) Error bars indicate +/- SEM.

In the Go-NoGo Minus significant effect of trial type was found in excited neurons

(F(3,73)=6.053, p=0.001), where neurons displayed a significantly greater response in FA than CR

trials (p=0.020) and also a greater response in Miss trials than in other trial types (p<0.046) (Figure 4-

3A). Inhibited neurons were also significantly affected by trial type (F(3,89)=7.338, p<0.001) the

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neuron population showing greater inhibition (p<0.05) in error trials compared with correct trials

(Figure 4-3B).,

In summary, cue induced responses were only significantly different between trials in the

Go-NoGo Minus task, where inhibited neurons showed a stronger decrease in firing rate in error

trials compared to correct trials, similar to the responses observed in striatum (Figure 3-8). In the

Go-NoGo Minus task excitation was greater in Miss trials in all other trial types and greater in FA

than CR trials.

4.3.6 Effect of previous trial response

To further investigate the role of behavioural response on cue onset neuronal response, firing rate

responses to cue onset were analysed for effects of previous trial outcome; correct (rewarded)

behavioural response (Hit or CR) vs. incorrect (unrewarded) behavioural response (Miss or FA) trials.

A stronger excitation in response to cue onset was observed in both tasks (F(1,31)=9.068, p=0.005

(Go-NoGo Plus) and (F(1,114)=14.980, p<0.001 (Go-NoGo Minus)) after previous trial with incorrect

response than previous trial with correct response(Figure 4-4). Cue induced inhibition was

significantly stronger after previous incorrect than correct response trial in the Go-NoGo Minus task

(F(1,129)=8.750, p=0.004) (Figure 4-4B), whereas no effect of previous trial response on cue-induced

inhibition was observed in the Go-NoGo Plus task (F(1,78)=0.835, p=0.364) (Figure 4-4A).

Figure 4-4 Effect of previous trial response on cue-induced firing. Change in firing rate in response to cue onset in PrL in the Go-NoGo Plus (A) and Go-NoGo Minus (B) task in relation to correct (Hit & CR) or incorrect (Miss & FA) behavioural response in the previous trial. Asterisks refer to significance levels of post hoc analysis of effect of trial type. *p < 0.05; **p < 0.01; ***p < 0.001 (LSD) Error bars indicate +/- SEM.

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Overall, firing rate response to cue onset was greater after previous incorrect response than

after previous correct response in both tasks, showing the same response pattern as observed in

striatum (Figure 3-9).

4.3.7 Coherence between prelimbic cortex and striatal subregions

Coherence between pairs of neurons in different striatal subregions was analysed during the

baseline (-3 – 0 seconds before cue onset) and the cue response period (0 – 3 seconds after cue

onset).

Figure 4-5 Log transformed baseline coherence between PrL and DLS, DMS and NAc. A strong association between baseline coherence (-3 to 0 sec relative to cue onset) and behavioural response after cue onset was present between PrL and the striatal subregions. in both the Go-NoGo Plus (A) and the Go-NoGo Minus (B) task. Asterisks refer to significance levels of post hoc analysis of effect of trial type. *p < 0.05; **p < 0.01; ***p < 0.001 (LSD) Error bars indicate +/- SEM.

Baseline coherence: In the Go-NoGo Plus task was strongly affected by trial type

(F(3,1464)=55.439, p<0.001), with post hoc analysis revealing that baseline coherence was

significantly different between all trial types (p < 0.028, LSD) (Figure 4-5A). Overall baseline

coherence between PrL and DLS was higher than baseline coherence between PrL and DMS or NAc

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(F(2,1464)=6.852, p=0.001 (ANOVA) – p<0.001 (Post Hoc LSD). No interaction between structure and

trial type was observed (F(6,1464)=1.431, p=0.199).

Baseline coherence in the Go-NoGo Minus task was also strongly affected by trial type Figure

4–5B), whereas no significant effect was observed of neither structure (F(2,1268)=0.867, p=0.420) or

structure x trial type interaction (6,1268)=1.299, p=0.255). Post hoc analysis of trial type effects

revealed that baseline coherence was greater before trials where the rat would subsequently deliver

an incorrect behavioural response (Miss and FA) than before trials with correct behavioural response

(Hit and CR) (p<0.001, post hoc LSD).

Coherence response to cue onset: Overall, coherence between PrL and the striatal

subregions in the 3 seconds following cue onset (Table 4-2) was similar to that observed in the 3

seconds before cue onset. However, in addition to a strong effect of trial type a strong interaction

between structure and trial type was observed in both tasks (F(6,1414)=11.050, p<0.001 (Go-NoGo

Plus) and (F(6,1401)=6.266, <0.001 (Go-NoGo Minus).

Table 2-2 Log transformed coherence between PrL and DLS, DMS and NAc after cue onset. Log transformed coherence between neuron pairs in PrL and the striatal subregions measured between 0 to +3 sec relative to cue onset with significance levels for post hoc analysis of effect of trial type. P values <0.05 are marked in yellow.

To further analyse of the effect of trial cue on interaction between trial type and structure,

cue induced coherence response (baseline subtracted) was examined. Coherence between PrL and

the three different striatal subregions was found to respond differently to cue onset specifically in

Miss trials in both tasks. In the Go-NoGo Plus task coherence between PrL and NAc and between PrL

and DMS increased after cue onset in Miss trials, whereas only a slight decrease in coherence

between PrL and DLS was observed (F(6,1381)=4.519, p<0.001 (ANOVA), p<0.007 (Post hoc LSD))

(Figure 4–5A). However in the Go-NoGo Minus task, coherence between PrL and NAc did not change

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in response to cue onset in Miss trials whereas coherence between coherence between PrL and DMS

decreased and coherence between PrL and DLS increase after cue onset (F(6,1225)=2.272, p=0.018

(ANOVA), p<0.020 (Post hoc LSD)) (Figure 4-5B).

In summary, baseline coherence in the Go-NoGo Plus task showed significant differences

between all trial types, with the highest corticostriatal coherence being observed before Miss trials.

Baseline coherence in the Go-NoGo Minus task was significantly greater before error trials than

correct trials. Further analysis of cue induced changes in corticostriatal coherence revealed that

coherence between PrL and the three striatal subregions responded differently to cue onset in Miss

trials. Differences between tasks

Although trial type did not affect baseline firing rates in neither the Go-NoGo Plus nor the Go-NoGo

Minus task, baseline firing rate did differ between tasks, with baseline firing rate being significantly

higher in the Go-NoGo Plus task than the Go-NoGo Minus task in neurons inhibited by cue onset

(F(1,137)= 10.165, p = 0.002), whereas no different between tasks was found in neurons excited by

cue onset (F(1,125)= 1.267, p = 0.262).

After cue onset, overall excitation was significantly greater in the Go-NoGo Minus task

(F(1,125)= 5.520, p = 0.020), whereas overall inhibition was greater in the Go-NoGo Plus task

(F(1,137)= 4.222, p = 0.042). When examining the role of task on the effect of previous trial

response, no significant effect of task was found neither in excited neurons (F(1,145)=0.129,

p=0.721) nor in inhibited neurons (F(1,207)=3.369, p=0.068).

Baseline coherence was also significantly higher in the Go-NoGo Minus than the Go-NoGo

Plus task (F(1,2732)=12.151, p<0.001), and interaction between task and trial type was also highly

significant (F(3,2732)=17.619, p<0.001) whereas no interaction with structure (F(2,2732)=0.809,

p=0.445) or interaction between task, structure and trial type (F(6.2732)=1.454, p=0.190) was found.

Cue induced change in coherence differed between tasks (F(1,2606)=7.107, p=0.008 and also

showed a significant interaction between task, structure and trial type (F(6,2606)=5.691, p<0.001).

In summary, the rats in the two tasks displayed significantly different baseline firing rates.

This task effect persisted after cue onset in inhibited neurons and was also manifested in differences

in striatal coherence between the two tasks.

4.4 Discussion

4.4.1 Baseline single unit activity

Although trial type did not affect baseline firing rates in either of the two tasks, baseline firing rate

was significantly greater in the Go-NoGo Plus than in the Go-NoGo Minus task in neurons inhibited

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by cue onset (Appendix - Table 1) and this task-effect persisted after cue onset. Similarly, coherence

also differed between task, with both pre and peristimulus coherence being higher in the Go-NoGo

Minus task than in the Go-NoGo Plus task (Figure 4-3A & B and Table 4-2). The task related

differences in cue-induced inhibition and peristimulus coherence are likely influenced by the overall

difference in baseline values, which makes comparison of task effects on cue-induced neuronal

responses difficult to interpret. Therefore, the following discussion will mainly focus on trial type and

structure dependent differences in firing rate response and coherence within each task.

4.4.2 PrL single unit response to cue onset

Firing rate response to cue onset in PrL were only significantly different between trial types in the

Go-NoGo Minus task, where greater inhibition was observed in error trials compared with correct

trials (Figure 4-3B). The greater inhibitory response in PrL to error trials compared with trials with

correct response is similar to the inhibitory firing rate responses observed in striatal subregions

(reported in Chapter 3 – Figure 3-8), suggesting that mPFC and striatal subregions interact during

complex behaviour. This is in accord with lesioning studies implicating interaction between mPFC

and DMS and NAc in planning and execution of tasks that require switching between multiple

stimulus-outcome contingencies (Christakou et al., 2001, Baker and Ragozzino, 2014).

In the Go-NoGo Minus task, cue induced excitation was greater in Miss trials than in correct

trials, whereas excitation in FA trials was significantly greater than CR but smaller than Miss trial

excitation. Increased firing rate response to cue onset in error trials compared with correct trials

observed in the Go-NoGo Minus task, may be caused by increased attentional load associated with

the decision to contradict the overtrained correct stimulus-response contingency. A similar, but

opposite, relationship between PrL firing and trials with correct and incorrect response was

observed in rats trained in a two-lever Go-NoGo task. In this task the strength of PrL signalling

immediately after cue onset was greater in Hit trials compared with Miss trials and in CR trials

compared with FA trials (Moorman and Aston-Jones, 2015). The behavioural setup in the above

study is more complex than the Go-NoGo Minus task presented in this thesis, in that the rats

discriminated between two levers as well as two tones – with only one tone and one lever being

rewarded. The findings in the above study as well as in the current study suggest that PrL

incorporates contextual information into the regulation of behaviour, rather than strictly promoting

or inhibiting behavioural responding. However, more research is needed to determine if differences

in task complexity affect the direction of firing rate responses to context. Stronger excitation in Miss

than FA trials may indicate that in tasks where incorrect responses require motor inhibition, such

motor inhibition is associated with greater PrL activation than when the incorrect response requires

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motor initiation. Previous research has shown that inactivation of PrL in rats increases incorrect

responses, suggesting PrL activity contributes to inhibitory response control (Jonkman et al., 2009)

as well as organisation and planning of flexible behaviour, based on previously acquired information

(Dalley et al., 2004). However, more research is needed to elucidate the role of PrL single unit

responses in relation to reward-directed behaviour.

Cue induced excitation was greater in the Go-NoGo Minus task than in the Go-NoGo Plus

task, whereas no difference between tasks was observed at baseline in cue-excited neurons. This

difference in cue induced excitation may be related to the difference in reward contingency between

the two tasks. However, given that no significant effect of trial was found in the Go-NoGo Plus task,

it is difficult to draw conclusions about how differences in task set up may have influenced cue

induced neuronal responses in the current study.

4.4.3 Effect of previous trial response

To further investigate the role of behavioural response on cue onset neuronal response, firing rate

responses to cue onset were analysed for effects of previous trial outcome; correct (rewarded)

behavioural response (Hit or CR) vs. incorrect (unrewarded) behavioural response (Miss or FA) trials.

Excited neurons were found to display a significantly greater response to cue onset in trials following

incorrect response trials (Miss and FA) than correct response trials (Hit and CR) in both tasks (Figure

4-4). Inhibition was also found to be greater in trials following previous incorrect response than trials

following previous correct response, although this difference only reached significance in the Go-

NoGo Minus task (Figure 4-4). The similarity between responses in the two tasks suggests that the

neuronal responses to cue onset were modulated by recent behavioural experience rather than

recent reward experience. Furthermore, the same pattern was observed in the striatal subregions

(Figure 3-9), suggesting that PrL and striatum interact to update and fine tune behavioural responses

based on recent experience. Disruption of mPFC-NAc have been shown to interfere with the

planning of responding to reward-paired cues, but only in trials following immediately after a trial

with a rewarded correct response (Christakou et al., 2004), further implicating interaction between

mPFC and striatum in in the updating of response-outcome contingencies. A recent study in which

rats where trained on a directional choice task, found greater firing rate response to cue onset in the

agranular areas of frontal cortex after previous correct trials than after error trials (Yuan et al.,

2015), suggesting that recent experience is associated with firing rate response to cue onset in both

of these cortical regions, as well as striatum, although with opposite effect. Updating of behaviour

based on recent experience may be maintained by a network of cortical and subcortical regions,

requiring coordination of excitation and inhibition in different brain areas in order to optimise

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output. However, more research on the role of recent experience on activity in cortical and

subcortical networks is warranted to understand its implication on future behavioural choice.

4.4.4 Coherence between PrL and striatum

Overall, prestimulus coherence was higher before error trials than correct response trials in both

tasks and highest in Miss trials (where the rats failed to respond to the Go cue) (Figure 4-5).

Increased coherence may reflect low levels of attention to external stimuli, causing the rat to miss

the cue (Gusnard and Raichle, 2001, Herzog et al., 2014). Several studies in humans and monkeys

have demonstrated that particularly alpha band coherence between different cortical regions before

cue onset affects stimulus detection (Boly et al., 2007, Sadaghiani et al., 2010, Forstmann et al.,

2010, Melloni et al., 2007, Shulman et al., 2002). More recently, a study in rats performing an

auditory detection task found increased theta, alpha and beta band coherence between frontal and

parietal cortex before cue onset in trials where the rats failed to detect the tone (Herzog et al.,

2014). Fluctuation of activity and network connectivity has long been linked to attentional state and

previous work suggests that low detection rates following high coherence likely represent functional

inhibition within the target network diverting attention away from external stimuli to focus attention

on internal representations such as working memory (Hanslmayr et al., 2007, Mazaheri et al., 2011,

Cooper et al., 2003, Gusnard and Raichle, 2001, van Dijk et al., 2008). Through the associative,

sensory-motor and limbic cortico-striatal-thalamic circuits (Van Waes et al., 2012, Redgrave et al.,

2011), the PrL and striatal subregions are intricately connected to both task-positive and task-

negative networks implicated in the regulation of attention to external stimuli (Sadaghiani et al.,

2010). The high prestimulus coherence between striatal subregions observed in Miss may be driven

by modulatory input from task-negative regions of cortex or thalamus. More studies, recording from

multiple sites in cortical and subcortical networks, are needed to investigate the role of coherence

within these networks on future behavioural choice.

In both tasks, prestimulus coherence was greater in FA trials as well as in Miss trials

compared with correct response trials (Figure 4-5), suggesting that coherence is associated with

future behavioural choice (Figure 4-5), consistent with the overall pattern of coherence observed

between the striatal subregions (Figure 3-10). The high level of coherence between neurons in PrL

and in striatal subregions is well in accord with labelling studies showing strong projections from PrL

to ventral and dorsomedial parts of striatum (Gabbott et al., 2005, Groenewegen et al., 1999,

Heidbreder and Groenewegen, 2003), and further suggest that complex flexible behaviour require

interaction between mPFC and striatum. Both contralateral lesion of mPFC and NAc (Christakou et

al., 2001) or contralateral inactivation of PrL and DMS (Baker and Ragozzino, 2014) have been show

84

to disrupt planning and responding in tasks that require switching between multiple stimulus-

outcome contingencies. However, more research is needed to establish the exact role of this

interaction, in particular to establish if this interaction is strictly top down or if striatal subregions

also influence mPFC activity. Previous work on corticostriatal coherence in humans (Cohen et al.,

2012) and in anaesthetised rats (Sharott et al., 2005) found that cortical structures influenced basal

ganglia activity whereas basal ganglia exerted little or no control of cortical activity. However, a

recent study found that although coherence between primary motor cortex and dorsal striatum was

strictly unidirectional in resting and anesthetised rats, corticostriatal coherence was bidirectional in

awake rats (Nakhnikian et al., 2014) and analysis of causal connectivity in a MRI study in awake

monkeys found striatum to exert a stronger overall influence of striatum on PFC (rather than from

PFC to striatum) during category learning (Antzoulatos and Miller, 2014). These recent findings

suggest that corticostriatal interaction in awake behaving subjects may not exclusively be in the form

of top down control from cortex to striatum but may also involve bottom up modulation from

striatum to cortex. More research is needed to elucidate the precise role of mPFC-striatal interaction

during complex behaviour to establish the direction of this interaction and whether the direction

differs between striatal subregions.

The finding that baseline coherence between PrL and DLS was higher than baseline

coherence between PrL and NAc and DMS was unexpected, because labelling studies suggest that

there are no direct anatomical connection between these structures (Voorn et al., 2004). DLS

received strong projections from primary motor cortex (Koralek et al., 2013) and a recent

anterograde labelling study found evidence of projections from both PrL and IL to primary motor and

primary somatosensory cortex (Bedwell et al., 2014). In addition, similar task-bracketing single unit

activity developed in DLS and IL in rats as a result of training, while task-related activity in PrL

declined (Smith and Graybiel, 2013), which has been suggested to indicate functional connectivity

between IL and DLS (Riga et al., 2014). Such connectivity would enable PrL to influence DLS activity

either through projection via primary motor cortex or through simultaneous modulation from IL of

both areas (Riga et al., 2014, Moorman and Aston-Jones, 2015). The unexpected coherence between

PrL and DLS as well as the overall similarity between trial type dependent baseline coherence

between PrL and all three striatal subregions may indicate joint modulation by other brain areas.

However, more research is needed, incorporating single unit recordings from thalamic and cortical

regions that are part of the limbic, associative and motor cortico-striatal-thalamic circuits, as well as

striatum, to fully elucidate the role of corticostriatal interaction during complex behaviour.

Examining the effect of cue onset on corticostriatal coherence revealed that coherence

between PrL and the three striatal subregions was affected differently by cue onset in Miss trials. In

85

the Go-NoGo Plus task coherence between PrL and NAc and between PrL and DMS increased after

cue onset, whereas only a minor decrease in coherence between PrL and DLS was observed (Figure

4-3A). PrL projects directly to NAc (Ding et al., 2001, Hart et al., 2014, Gabbott et al., 2005, Balleine

et al., 2009, Groenewegen et al., 1999, Balleine et al., 2007, Heidbreder and Groenewegen, 2003)

and DMS (Gabbott et al., 2005, Groenewegen et al., 1999, Hart et al., 2014, Balleine et al., 2007,

Balleine and O'Doherty, 2010, Heidbreder and Groenewegen, 2003) but not to DLS (Voorn et al.,

2004) and the observed increased coherence between directly connected structures likely

represents increased top down regulation associated with task management. In the Go-NoGo Minus

the coherence response to cue onset also differed between structure pairs. However in this task, cue

onset did not appear to affect coherence between PrL and NAc, whereas coherence between PrL

and DMS decreased and coherence between PrL and DLS increased after cue onset (Figure 4-3B). In

the current study coherence differed between tasks already at baseline, which may have influenced

the magnitude and direction of the response to cue onset. However, the task dependent difference

in baseline coherence makes task dependent differences in coherence after cue onset difficult to

interpret. More research is needed to examine the role of behavioural task setup on corticostriatal

coherence.

4.4.5 Conclusions

Firing rate response to cue onset in PrL were only significantly different between trial types in the

Go-NoGo Minus task, where greater inhibition was observed in error trials compared with correct

trials and excitation in both error trials were greater than in CR trials but only excitation in Miss trials

was greater than in Hit trials(Figure 4-3B). Furthermore, both excitatory and inhibitory responses to

cue onset were greater in trials following incorrect response trials (Miss and FA) than correct

response trials (Hit and CR) in both tasks (Figure 4-4). The same pattern was observed in the striatal

subregions (Figure 3-9 & 3-9), suggesting that PrL incorporates contextual information and recent

experience into the regulation of behaviour, rather than strictly promoting or inhibiting behavioural

responses. However, more research is needed to elucidate the role and direction of corticostriatal

interaction in the planning and execution of complex tasks.

The current study is, to my knowledge, the first to simultaneously investigate the role of single

unit coherence between mPFC and DLS, DMS and NAc during execution of complex reward-directed

behaviour. Overall, prestimulus coherence was higher before error trials than correct response trials

in both task and highest in Miss trials (where the rats failed to respond to the Go cue) (Figure 4-5),

suggesting that corticostriatal coherence is associated with future behavioural choice (Figure 4-5),

consistent with the overall pattern of coherence observed between the striatal subregions (Figure 3-

86

10). The high level of coherence between neurons in PrL and in striatal subregions is well in accord

with labelling studies showing strong projections from PrL to ventral and dorsomedial parts of

striatum (Gabbott et al., 2005, Groenewegen et al., 1999, Heidbreder and Groenewegen, 2003). The

current study also found coherence between PrL and DLS, which together with the overall similarity

between trial type dependent baseline coherence between PrL and all three striatal subregions may

indicate joint modulation from other brain areas or interaction between different cortico-striatal-

thalamic circuits. The high prestimulus coherence between PrL and the striatal subregions observed

in Miss trials may reflect low levels of attention to external stimuli (Gusnard and Raichle, 2001,

Herzog et al., 2014) and may be driven by modulatory input from task-negative regions of cortex or

thalamus. However, more research is needed, incorporating single unit recordings from thalamic and

cortical regions that are part of the limbic, associative and motor cortico-striatal-thalamic circuits, as

well as striatum, to fully elucidate the role of corticostriatal interaction during complex behaviour.

87

Chapter 5: Final Discussion

Striatum function is pivotal to the learning and execution of reward-related tasks, with lesion studies

suggesting functional differences between DLS, DMS and NAc (Balleine et al., 2009, Redgrave et al.,

2011). In addition, corticostriatal communication from prelimbic cortex (PrL) to DMS and NAc likely

play a role in the regulation of appetitive behaviour (Groenewegen and Uylings, 2000, Dalley et al.,

2004). Characterizing the dynamic modulation of behaviour imposed by the reward expectation as

well as motor preparation in behaving animals will be a key step to understanding the normal

function of striatum and cortico-striatal projections. The aim of the presented thesis was to examine

single unit responses within PrL, DLS, DMS and NAc, as well as network activity between these

regions, are associated with salience, motor and reward components of cue-responses during

execution of a reward-directed behavioural task.

5.1.1 Summary of conclusions from experimental chapters

In chapter 2, stimulus-evoked LFP responses in DLS were recorded in head fixed rats during

execution of a tactile Go-NoGo task with the aim of assessing whether the level of salience of

sensory input to DLS affects the sensory representation in this structure. In this study, LFP response

to stimulus onset was found to be enhanced in trials in which cue onset was immediately followed

by initiation of motor response (Figure 2-4C), suggesting the observed response is associated with

motor initiation. This accords well with previous research showing that an increase in DLS activity in

rats during execution of the task as result of training (Barnes et al., 2011, Root et al., 2010, Thorn et

al., 2010, Kimchi et al., 2009). However, the enhanced response in False Alarm trials may still have

been caused by the rat, incorrectly, expecting a reward for its response, as previous research has

shown that DLS neurons that responded to movement increased their firing when movement was

paired with reward (Kimchi et al., 2009).

Due to technical challenges, only a limited dataset was collected in this setup. However, the

preliminary results obtained in this chapter did expose limitations of the standard Go-NoGo task: the

cue signalling reward availability also signals to the animal to initiate movement, motor and reward

component of cue-evoked neural responses cannot be distinguished. In addition, the findings in this

chapter emphasise the importance of considering the contribution of the subject’s choice when

analysing effect of reward-paired cues on neuronal activity.

In chapter 3, the observations made in chapter 2 were used to develop a modified version of

the standard Go-NoGo task (Figure 3-1), in which correct responses in both Go and NoGo trials were

88

rewarded (Go-NoGo Plus), allowing comparison of trials with rewarded motor initiation and

rewarded motor suppression, respectively. A second group of rats were trained in the standard Go-

NoGo task (Go-NoGo Minus), where only correct responses in Go trials were rewarded. Single unit

responses were obtained simultaneously from DMS, DLS and NAc to compare responses as well as

coherence between striatal subregions during execution of either of the two Go-NoGo tasks.

Through comparison of the single unit responses to cue onset in these two tasks, this study aimed to

examine the role of individual striatal sub-regions, as well as communication between sub-regions,

on reward expectancy and preparation of motor response during conditioned discrimination. We

hypothesised that neural responses to cue onsets on correct rejection trials compared to other trial

types would be different between tasks, as this was the only trial type for which the outcome

differed between tasks. However, firing rate response to cue onset did not appear to be influenced

by differences in reward expectancy. Instead, firing rate responses to cue onset were overall greater

in error trials compared with trials with subsequent correct response in both tasks (Figure 3-8). A

recent fMRI study in humans found that testing of known stimulus-response-outcome contingencies

is associated with increased activity in distinct striatal regions (Liu et al., 2015). It is possible that the

comparative increase in firing rate response in error trials may reflect a switch from an over-trained

habitual response to more goal-directed approach in order for the rats to test the consistency of

learned stimulus-response contingencies. Firing rate response to cue onset was significantly greater

in trials following error trials (Figure 3-9), which may signify increased attention in trials following

previous unsuccessful behavioural response.

Overall, prestimulus coherence was greater in FA trials as well as in Miss trials compared

with correct response trials, suggesting that coherence is associated with future behavioural choice

(Figure 3-10). Coherence between striatal subregions was found to be particularly high before and

during Miss trials (Figure 3-10). Fluctuations in prestimulus coherence between cortical regions have

been linked to attention (Gusnard and Raichle, 2001, Herzog et al., 2014). The high striatal

prestimulus coherence observed in Miss trials is well in accordance with previous studies examining

the role of cortical coherence and stimulus detection (Boly et al., 2007, Sadaghiani et al., 2010,

Forstmann et al., 2010, Melloni et al., 2007, Shulman et al., 2002, Herzog et al., 2014) and the

coherence between striatal regions may reflect coherence in cortical areas projecting to the

striatum, with mPFC being a prime candidate for the exertion of such top down control (Riga et al.,

2014, Balleine et al., 2009, Christakou et al., 2004, Stefanik et al., 2015, Baker and Ragozzino, 2014,

Thorn and Graybiel, 2014).

89

To address this issue, the study presented in chapter 4 examined the contribution of PrL single unit

activity and coherence between PrL and DMS, DLS and NAc during execution of the same two Go-

NoGo behavioural paradigms presented in chapter 3. Firing rate response to cue onset in PrL were

only significantly different between trial types in the Go-NoGo Minus task, where greater inhibition

was observed in error trials compared with correct trials and excitation in both error trials were

greater than in CR trials but only excitation in Miss trials was greater than in Hit trials (Figure 4-3B).

Furthermore, both excitatory and inhibitory responses to cue onset were greater in trials following

incorrect response trials (Miss and FA) than correct response trials (Hit and CR) in both tasks (Figure

4-4). The same pattern was observed in the striatal subregions (Figure 3-9 & 3-9), suggesting that

mPFC and striatal subregions interact during complex behaviour. This is in accord with lesioning

studies implicating interaction between mPFC and DMS and NAc in planning and execution of tasks

that require switching between multiple stimulus-outcome contingencies (Christakou et al., 2001,

Baker and Ragozzino, 2014).

Overall, prestimulus coherence was higher before error trials than correct response trials in

both task and highest in Miss trials (where the rats failed to respond to the Go cue) (Figure 4-3),

suggesting that corticostriatal coherence is associated with future behavioural choice (Figure 4-3),

consistent with the overall pattern of coherence observed between the striatal subregions (Figure 3-

10). The high level of coherence between neurons in PrL and in striatal subregions is well in accord

with labelling studies showing strong projections from PrL to ventral and dorsomedial parts of

striatum (Gabbott et al., 2005, Groenewegen et al., 1999, Heidbreder and Groenewegen, 2003). The

current study also found coherence between PrL and DLS, which together with the overall similarity

between trial type dependent baseline coherence between PrL and all three striatal subregions may

indicate joint modulation from other brain areas or interaction between different cortico-striatal-

thalamic circuits. The high prestimulus coherence between striatal subregions observed in Miss trials

may reflect low levels of attention to external stimuli (Gusnard and Raichle, 2001, Herzog et al.,

2014) and may be driven by modulatory input from task-negative regions of cortex or thalamus.

5.1.2 Comparison between findings in experimental chapters

Chapter 2 and 3 both examined the contribution of DLS to trial-cues in the Go-NoGo task. In the

study presented in chapter 2, clear trial type dependent differences in cue-evoked LFP responses

were observed, which could be related to either motor initiation or reward expectation. In chapter 3

a modified version of the standard Go-NoGo task was introduced with the expectation that this

modified task would enable verification of whether the response observed in chapter 2 was related

to the motor component of the task or to the reward component of the task. However, in the study

90

presented in chapter 3, trial type dependent differences in firing rate appeared more closely linked

to subsequent correct or incorrect behavioural response than to either motor or reward component.

The electrophysiological data presented in chapter 2 were collected from a single animal and

therefore should be viewed as preliminary. In addition, only LFPs were recorded in chapter 2,

whereas the electrophysiological data presented in chapter 3 and 4 were from single unit recordings,

which in itself limits the comparability between the two datasets (Kajikawa and Schroeder, 2011).

However, the contrasting patterns in trial type dependent differences in response to cue onset may

indicate important differences between the role of the cue in the Go-NoGo task presented in charter

2 and those presented in chapter 3 and 4. In the Go-NoGo task presented in chapter 2 the onset of

the cue signalling trial type, also signalled the beginning of the 1.5 second response interval.

Therefore the motor response (if the rat chose to respond) would always be initiated immediately

after cue onset. In contrast in both Go-NoGo tasks presented in chapter 3 and 4, the onset of the cue

signalling trial type was presented four seconds before the lever was presented, thus a 4 second

waiting period always preceded the 4 second response interval in these tasks. Therefore, vigorous

motor response was not required immediately after cue onset in the tasks presented in chapter 3

and 4. In fact, cue onset was almost always immediately followed by the rat rearing or becoming

immobile before eventually responding (if the rat chose to respond) (observation made by the

experimenter). Whereas the DLS response observed in chapter 2 likely reflects actual motor

initiation, the DLS response observed in chapter 3 may in fact reflect processing related to decision

making prior to initiation or suppression of motor response. In rats trained in a T-maze

discrimination task where direction of the reward was signalled at the beginning of each trial before

the rat was given access to the maze, firing rate response to the initial cues increased in the early

stages of training, but shifted towards the onset of motor response as the rats became over-trained

on the task (Barnes et al., 2011, Barnes et al., 2005). In this T-maze paradigm the rat was always

required to initiate a motor response, and therefore this paradigm cannot elaborate on the role of

motor initiation vs. motor suppression. However, these findings, along with the findings presented in

this thesis, demonstrate how temporal disassociation between cues requiring reward-directed

decision making and task elements associated with motor processing within the experimental design

is pivotal to understanding the neural substrate for reward-directed decision making and behaviour.

The electrophysiological data presented in chapter 3 and 4 were obtained within the same

behavioural task, with chapter 3 focusing on striatal responses and coherence and chapter 4

incorporating single unit recordings from PrL to examine the role of interaction between PrL and the

striatal sub-regions. In both PrL and the striatal sub-regions significant differences between trial

types were primarily observed between trials with subsequent correct and incorrect behavioural

91

response and firing rate responses to cue onset were greater in trials following incorrect response

trials (Miss and FA) than correct response trials (Hit and CR) in both PrL and the striatum. Overall,

prestimulus coherence was higher before error trials than correct response trials in both task and

highest in Miss trials both between striatal subregions (Figure 3-10) and between PrL and the striatal

subregions (Figure 4-3). The similarity in trial type dependent responses to cue onset as well as in

the coherence between PrL and the striatal subregions, which may indicate joint modulation from

other brain areas or interaction between different cortico-striatal-thalamic circuits. The high

prestimulus coherence observed in Miss trials may reflect low levels of attention to external stimuli

(Gusnard and Raichle, 2001, Herzog et al., 2014) and may be driven by modulatory input from task-

negative regions of cortex or thalamus. However, more research is needed, incorporating single unit

recordings from thalamic and cortical regions that are part of the limbic, associative and motor

cortico-striatal-thalamic circuits, as well as striatum, to fully elucidate the role of corticostriatal

interaction during complex behaviour.

The findings in this thesis emphasise the importance of considering the contribution of the

subject’s choice when analysing effect of reward-paired cues on neuronal activity. Preliminary

findings presented in chapter 2 indicated that LFP response in DLS were more strongly associated

with motor initiation than to trial type. Further investigation of single unit responses in DLS, DMS,

NAc and PrL showed that significant differences in response to trial onset cue were predominantly

found between trials with subsequent correct and incorrect behavioural response, possibly

suggesting modulation from other brain areas or interaction between different cortico-striatal-

thalamic circuits.

5.1.3 Future perspectives

Projections from cortex to striatum constitute key segments in the associative, sensory-motor and

limbic cortico-striatal-thalamic circuits (Van Waes et al., 2012, Balleine et al., 2009, Redgrave et al.,

2011). However, to fully understand the role of these circuits, more research into the contribution of

other structures in these circuits is warranted. Like in the striatum, the associative, sensory-motor

and the limbic circuits all project to and from sub-regions of the thalamus (Haber and Calzavara,

2009) and the thalamus may play an important role in modulation of the joint output of these

circuits, with distinct groups of thalamic nuclei likely contributing to different aspects of sensory,

motor, and cognitive processing (Haber and Calzavara, 2009). Traditionally the thalamus has been

regarded primarily as a passive relay station for sensory and motor signals (Fama and Sullivan, 2015).

However, the thalamus is now considered to also contribute to cognitive processes, including

attention, speed of information processing, and memory (Fama and Sullivan, 2015) and gamma

92

coherence between LFPs in mPFC and mediodorsal thalamus in relation to reward delivery has been

found to increase in rats as a result of instrumental learning (Yu et al., 2012). Simultaneous

electrophysiological recordings in striatal sub-regions and thalamic nuclei during execution of

reward-directed learning tasks may provide information on the role of this segment of the cortico-

striatal-thalamic circuits and improve our understanding of how cortico-striatal-thalamic circuits

contribute to the processing of salient sensory input and to the optimisation of behavioural

responses.

Although the majority of striatal cells are medium spiny neurons (MSNs) (Gonzales and

Smith, 2015), acetylcholine release from cholinergic interneurons within striatum is thought to

modulate dopamine transmission by acting at both muscarinic and nicotinic acetylcholine receptors

(Threlfell and Cragg, 2011). The subtypes of muscarinic and nicotinic acetylcholine receptors differ

between dorsal striatum and NAc, thus enabling cholinergic interneurons to modulate dopamine

transmission differently in specific striatal sub-regions (Threlfell and Cragg, 2011), which in turn may

underlie differences between striatal sub-regions in their contribution to behaviour (Aoki et al.,

2015). In rats trained in a set-shifting task, cell type specific lesion of cholinergic interneurons in

either DMS or ventral striatum differentially affected the behaviour of the rats, with DMS lesions

particularly impairing the rat’s ability to pay attention to previously irrelevant cues and ventral

striatum lesions specifically impairing the rat’s ability to pay attention to novel cues (Aoki et al.,

2015). This finding suggests that i) cholinergic interneurons may modulate the behavioural output in

tasks that require retention and shifting between multiple stimulus-response-outcome

contingencies and ii) this modulation may play different roles within distinct striatal sub-regions. The

findings presented in this thesis suggest that changes in neural activity striatal sub-regions are

associated with behavioural choice. However, because relationships between behaviour and

electrophysiological data such as those presented in this thesis are correlational, they cannot

provide definite evidence of causality between the neural activity and behaviour. A logical follow up

to the studies presented in the previous chapters would be to use an optogenetic approach to

specifically deactivate cholinergic interneurons within distinct striatal sub-regions in animals during

choice behaviour, while also recording single unit activity in striatal sub-regions. Selectively

deactivating cholinergic interneurons through optogenetic stimulation in a subset of trials in each

behavioural session would allow examination of cholinergic modulation on both behaviour and cue-

evoked single unit responses.

93

Appendix

Appendix Table 1 Log transformed baseline firing rates Mean baseline firing rates (-3 to 0 sec relative to cue onset) for neurons excited or inhibited by cue onset for each of the three striatal subregions.

Appendix Table 2 Log transformed firing rate responses to cue onset Mean firing rate responses (baseline substracted) in the first 100ms after cue onset for neurons excited or inhibited by cue onset for each of the three striatal subregions.

94

Appendix Table 3 Effect of previous trial response on cue-induced firing Change in firing rate in response to cue onset for DLS, DMS and NAc in the Go-NoGo Plus (A) and Go-NoGo Minus (B) task in relation to correct (Hit & CR) or incorrect (Miss & FA) behavioural response in the previous trial.

95

Appendix Table 4 Log transformed coherence after cue onset. Log transformed coherence between neuron pairs in the striatal subregions measured between 0 to +3 sec. relative to cue onset with significant levels for post hoc analysis of effect of trial type. P values <0.05 are marked in yellow.

96

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