CNPCNP Frontal lobe activation mediates the activation of the amygdala during cognitive-emotional learning : an

effective connectivity study

Branislava Ćurčić-Blake,Branislava Ćurčić-Blake, Marte Swart Marte Swart and André Alemanand André Aleman

Cognitive Neuropsychiatry group, Cognitive Neuropsychiatry group, Neuroimaging center (NIC), University Neuroimaging center (NIC), University

medical center Groningen (UMCG), The medical center Groningen (UMCG), The NetherlandsNetherlands

CNPCNPOverview

• Quick introduction and key points regarding DCM• Our emotional learning study

• Questions and suggestions welcome at any point

CNPCNPPhenomenon of brain connectivity?

• Anatomical : The connections between brain areas by means of white matter tracts (groups of axons)

•Functional :Analyses of inter-regional effects: what are the interactions between the elements of a given neuronal system? How functionally specialised regions interact with each other

•a) Functional connectivity:

the temporal correlation between spatially remote neurophysiological events

•b) Effective connectivity

the influence that the elements of a neuronal system exert on each other

A B A B

CNPCNP DCM

• Neat method to establish effective connectivity (as defined by Friston!)

• A well-defined model or set of models is required• The fMRI data dynamics are modeled

• Make inferences about processes that underlie measured time series

• Idea is to estimate parameters of a reasonably realistic neuronal system model such that predicted BOLD corresponds as close as possible to measured BOLD

From Burkhard Pleger, Functional Imaging Lab, University College London

CNPCNP What DCM can do and what cannot

• DCM can make inferences about how much the activity in area A can induce change of activation in area B!

• DCM cannot make inferences about speed of the processes, nor timing.

CNPCNP

hemodynamicmodel

effective connectivity

modulation ofconnectivity

The bilinear model CuzBuAz jj )(

λ

z

y

integration

Neural state equation ),,( nuzFz DCMConceptual overview

Friston et al. 2003, NeuroImage

u

z

u

FC

z

z

uuz

FB

z

z

z

FA

jj

j

2

CNPCNPImportant coefficients

• A –Effective connectivity

• B – modulatory effects

• C - Inputs

BOLDy

y

y

Inputu(t)

activityz2(t)

activityz1(t)

activityz3(t)

direct inputs

c1 b23

a12

neuronalstates

CNPCNP

• Combining the neural and hemodynamic states gives the complete forward model.

• An observation model includes measurement error e and confounds X (e.g. drift).

• Bayesian parameter estimation

• Result:Gaussian a posteriori parameter distributions, characterised by mean ηθ|y and covariance Cθ|y and posterior covariance of noise Ce .

How it works in practice: parameter estimation

ηθ|y

)(xy )(xy

eXuhy ),(

observation model

)()|()|( pypyp )()|()|( pypyp posterior likelihood ∙ prior

CNPCNP Choosing the model

Bayes Theorem

Bayes factor

Akaike information criterion (AIC):

Bayesian information criterion (BIC):

pmaccuracymyAIC )()|(

Penny et al. 2004, NeuroImage

SNp

maccuracymyBIC log2

)()|(

)|(

)|(),|(),|(

myp

mpmypmyp

)|(

)|(

jmyp

imypBij

Here p is the number of parameters and Ns is the number of data points

CNPCNPThe DCM cycle

Design a study thatallows to investigatethat system

Extraction of time seriesfrom SPMs

Parameter estimationfor all DCMs considered

Bayesian modelselection of optimal DCM

Statistical test on parameters

of optimal model

Hypothesis abouta neural system

Definition of DCMs as systemmodels

Data acquisition

CNPCNPCognitive-Emotional learning study

• What is known about emotional learning

• Our idea

• Our experiments

• Results and Conclusions

CNPCNP Emotional learning

• “Emotional memories constitute the core of our personal history” (La Bar 2006)

• Learning is enhanced or inhibited by emotions (Phelps 2004, Richter-Levin 2004)

• Emotions can • Enhance memory (Learning emotional words or faces;

Kensinger 2004)

• Modulate memory (LeDeux)

• Inhibit memory (spatial learning followed by stress – rats in water maze: reviewed in Richter-Levin 2004)

1. LaBar,K.S. & Cabeza,R. Cognitive neuroscience of emotional memory. Nat Rev Neurosci 7, 54-64 (2006). 2. Phelps,E.A. Human emotion and memory: interactions of the amygdala and hippocampal complex. Curr. Opin. Neurobiol. 14, 198-

202 (2004).3. Richter-Levin,G. The amygdala, the hippocampus, and emotional modulation of memory. Neuroscientist. 10, 31-39 (2004). 4. Kensinger,E.A. & Corkin,S. Two routes to emotional memory: distinct neural processes for valence and arousal. Proc. Natl. Acad.

Sci. U. S. A 101, 3310-3315 (2004). 5. LeDoux,J. The emotional brain: misterious underpinnings of emotional life. Simon & Schuster, New York (1996).

CNPCNP Emotional learning• Amygdala and Hippocampus complex are anatomically

connected (Ameral 1992; Stefanacci 1996);• Emotional enhancement of learning:

– Amygdala modulates encoding and storage of Hippocampal memories.

– Hippocampal complex (episodic representations, interpretations of events) can influence the amygdala response to emotional stimuli.

• Hippocampus – Amygdala effective connectivity is modulated by positive and negative emotions during emotional retrieval (Smith et al. 2006).

• Amygdala modulates parahippocampal and frontal regions during emotional memory storage (Kilpatric 2003) and encoding item for + and – stimuli (Kensinger 2006) etc.

1. D. G. Amaral, J. L. Price, A. Pitkänen, S. T. Carmichael, in The Amygdala: Neurobiological aspects of emotion, memory and mental dysfunction, J. P. Aggleton, Ed. (Wiley Liss, New York, 1992).

2. L. Stefanacci, W. A. Suzuki, D. G. Amaral, J.Comp Neurol. 375, 552-582 (1996).3. E. A. Phelps, Curr.Opin.Neurobiol. 14, 198-202 (2004).4. E. A. Kensinger and D. L. Schacter, J.Neurosci. 26, 2564-2570 (2006).5. L. Kilpatrick and L. Cahill, Neuroimage. 20, 2091-2099 (2003).

CNPCNP

• The emotional situations influence memory on its every stage:– Encoding (LeDeux 1996; Kensinger 2006)– Consolidation (Richter-Levin 2004)– Storage (Kilpatric 2003; Phelps 2004)– Retrieval (Smith 2006)

1. LeDoux,J. The emotional brain: misterious underpinnings of emotional life. Simon & Schuster, New York (1996). 2. E. A. Kensinger and D. L. Schacter, J.Neurosci. 26, 2564-2570 (2006).3. Richter-Levin,G. The amygdala, the hippocampus, and emotional modulation of memory. Neuroscientist. 10, 31-39 (2004). 4. E. A. Phelps, Curr.Opin.Neurobiol. 14, 198-202 (2004).5. L. Kilpatrick and L. Cahill, Neuroimage. 20, 2091-2099 (2003).6. Smith,A.P., Stephan,K.E., Rugg,M.D. & Dolan,R.J. Task and content modulate amygdala-hippocampal connectivity in emotional

retrieval. Neuron 49, 631-638 (2006).

CNPCNPBrain areas involved

Functional MRI (fMRI) activation is monitored while healthy adults encode high-arousing negative words, low-arousing negative words

(valence only) and neutral words.

Data pooled across nine experiments consistently show haemodynamic changes evoked by conditioned fear stimuli in the amygdala and subjacent periamygdaloid cortex (coronal sections, left), and the thalamus and anterior cingulate/dorsomedial prefrontal cortex (ACC/DMPFC, mid-sagittal section, right).

LaBar,K.S. & Cabeza,R. Cognitive neuroscience of emotional memory. Nat Rev Neurosci 7, 54-64 (2006).

CNPCNP MFcortex and IFcortex• Emotional memory studies show also involvement

of MFC and IFC• MFC is sensitive to tasks involving emotions,

mental state attribution (1), monitoring for and detecting errors (2), and mentalizing (3).

• IFC is engaged in emotion regulation, processing semantic aspects of face recognition, and language tasks. The left IFG selects the task-relevant information (emotional connotation as target information from specific competing semantic alternatives; 4).

1. Olsson,A. & Ochsner,K.N. The role of social cognition in emotion. Trends Cogn Sci. 12, 65-71 (2008).2. Summerfield,C. et al. Predictive Codes for Forthcoming Perception in the Frontal Cortex. Science 314, 1311-1314 (2006). 3. Amodio,D.M. & Frith,C.D. Meeting of minds: the medial frontal cortex and social cognition. Nat Rev Neurosci 7, 268-277 (2006).4. Ethofer,T. et al. Cerebral pathways in processing of affective prosody: A dynamic causal modeling study. NeuroImage 30, 580-587

(2006)

CNPCNP Model of Amygdala involvement in Emotional Learning

• Potential mechanisms by which the amygdala mediates the influence of emotional arousal on memory.

LaBar,K.S. & Cabeza,R. Cognitive neuroscience of emotional memory. Nat Rev Neurosci 7, 54-64 (2006).

CNPCNPOur aim

• how the emotions and cognition interact during cognitive emotional learning ?

• whether the emotions revealed by activation of the amygdala modulate the way in which the cognition works during an associative emotional learning task that engages HIGHER COGNITIVE PROCESSES during the learning of emotional stimuli.

• We incorporate both positive and negative emotional stimuli in order to see whether these circles differ and if so, how.

CNPCNPStarting point

• Data obtained from experiments by Marte Swart• Students: 20 LOW score on BVAQ (Bermond-

Vorst Alexithymia Questionnaire).• An emotional picture-word associate learning task

(ALT) • Thus cognitive emotional processing

CNPCNP The task

RoomijsRoomijs(= ice-cream in English)(= ice-cream in English))

DoDo picture and word fit?picture and word fit?MemorizeMemorize

2-8sec2-8sec3sec3sec

Task ALT

• An emotional picture (International Affective Picture System) and a word were displayed for 3 seconds.

• 2-8 seconds to decide if the word and picture fitted together AND to remember them

CNPCNP Results

• bilateral amygdala (AMY), • inferior frontal gyrus (IFG),• medial frontal gyrus (MFG), and • fusiform gyrus (FG) during the ALT.

RFX analysis RFX analysis

ALT emotional > neutral ALT emotional > neutral

for low-alexithymia subjects for low-alexithymia subjects

(p<0.005, T>2.92, unc.). (p<0.005, T>2.92, unc.).

Crosshair Crosshair [12,-16,-14], MNA.[12,-16,-14], MNA.

FGR

AmyR

AmyR

IFGR

MFGR

CNPCNP The DCM ROI selection

Fig. 3 Contrast as it is Fig. 3 Contrast as it is used to define VOI’s: ALT used to define VOI’s: ALT emotional >fixation point emotional >fixation point (random effects t-test) for (random effects t-test) for

20 subjects. The IFG, 20 subjects. The IFG, MFG and Amy are circled MFG and Amy are circled for illustration (p<0.001, for illustration (p<0.001,

T>3.3, unc.). T>3.3, unc.). Crosshair [-22,-4,-16], Crosshair [-22,-4,-16],

MNI.MNI.

AmyL

AmyL AmyL

IFGL

IFGLMFGL

CNPCNPThe maximum activation per ROI

  BA x y z Z

AMY L   -22 -4 -16 4.45

AMY R   22 -4 -18 4.8

IFG L 45 -56 22 14 7.01

IFG R 45 56 28 18 4.63

MFG L 10 -6 8 50 7.28

MFG R 10 6 8 50 6.2

CNPCNPThe creation of VOI’s

• The VOI’s for each subject !• created by choosing the closest supra-threshold

(p < 0.05) voxel • within the Maximum Probability Maps (of the

Anatomical Toolbox in SPM5) • Belongs to the region (visual inspection)• Sphere of 4 mm drawn around• 10-33 voxels• Time series extracted• 1st Principal Component (PA)

CNPCNP Checking for input

Fig 4. Full DCM models with different areas of input for the selection of input area(s). Input is illustrated by black arrows, and effective connectivity by grey arrows.

Model M

MF IF

AMY

Model I

MF IF

AMY

Model A

MF IF

AMY

Model MI

MF IF

AMY

Model AI

MF IF

AMY

Model MA

MF IF

AMY

Model ABf PER

MI/M L 4.6*1011 8/4

MI/M R 26.8 6/2

MI/I L 2.6*1012 7/6

MI/M R 3.06 7/5

MI/A L 3.8*1011 8/4

MI/A R 14.4 8/2

MI/MA L 20.4 10/7

MI/MA R 74 13/3

MI/AI L 16.2 11/4

MI/AI R 135 14/2

CNPCNPChoosing the best connectivity mod

Fig 5.Illustration of models of effective connectivity during an ALT. Input consisting of positive, negative and neutral conditions goes parallel to the IFG and MFG. In Model #1 the IF and MF communicate directly and with the Amy as opposed to #2 and #3 where

the IF and MF communicate through the Amy. Models #4,5 and 6 are variations of model #1. The winning model #1 was also compared to the full MI model. The results

are presented in Table 3.

Model #1

IF MF

AMY

Model #2

IF MF

AMY

Model #3

IF MF

AMY

Model #4

IF MF

AMY

Model #6

IF MF

AMY

Model #5

IF MF

AMY

CNPCNPThe resulting connectivities and

modulatory effects

Mean SD t Sig.

MFG to Amy .16 .12 5.9 <.00

MFG to IFG .27 .33 3.4 .003

IFG to MFG .21 .35 2.6 .02

IFG to Amy .16 ,15 4.6 <.00

Pos MFG to Amy -.01 .08 -.7 .5

Pos MFG to IFG .01 .08 0.4 .6

Pos IFG to MFG .01 .06 1 .3

Pos IFG to Amy -.015 .07 -0.9 0.4

Neg MFG to Amy -.03 .08 -1.6 .1

Neg MFG to IFG .04 .07 2.52 .02

Neg IFG to MFG .04 .08 2.45 .03

Neg IFG to Amy -.03 .08 -1.5 .2

neu MFG to Amy -.02 .097 -.9 .35

neu MFG to IFG .04 .07 .5 .6

neu IFG to MFG .03 .09 1.4 .2

neu IFG to Amy -.003 .1 -.1 .9

Table 4 b. RightMean SD t Sig.

MFG to Amy .12 .14 3.8 .001

MFG to IFG .2 .3 3.0 .008

IFG to MFG .2 .3 2.5 .02

IFG to Amy .14 ,15 4.5 <.00

Pos MFG to Amy .01 .05 -.8 .4

Pos MFG to IFG .02 .06 1.6 .1

Pos IFG to MFG .03 .07 2.2 .04

Pos IFG to Amy -.03 .05 -2.2 0.04

Neg MFG to Amy -.01 .07 -.5 .6

Neg MFG to IFG .05 .08 2.97 .008

Neg IFG to MFG .02 .03 2.99 .008

Neg IFG to Amy .01 .09 -0.7 .5

neu MFG to Amy -.03 .07 -1.9 .07

neu MFG to IFG .01 .07 .8 .4

neu IFG to MFG .04 .08 2.3 .03

neu IFG to Amy -0.04 .07 -.45 .02

Table 4 a. Left

CNPCNP The resulting connectivities and modulatory effects

Fig 6. Modulatory effects of the best DCM model. Increasing effect (red bold arrows) and decreasing effect (blue dashed arrow) are presented with the % of influence on the effective connectivity and the significance level (in brackets).

Amy

IF

MF

Amy

IF

MF

CNPCNPLateralization yes or no?

• t – test between modulatory effects for left and right hemisphere showed NO significant difference between mean values of modulatory effects for each pair.

• Thus, we can not claim that there is lateralization.

CNPCNPConclusions

• The area involved in basic emotional learning (the amygdala) does not affect the change in activity of the cognitive areas (the IFG and MFG).

• The subjects appear to pay more attention to the context and evaluation of the given stimuli, and these processes were not affected by emotions.

• In our case it seems that the subjects concentrated on the task and suppressed their emotions to some extent

CNPCNPMain conclusion

• In conclusion, it is evident that complex emotional learning is led by a “top-down” process from the frontal areas- the MFG and IFG- to the amygdala.

CNPCNPPitfall

• There are gender differences (Cahil et al 2001;2002):– Correlations between Left Amygdala – emotional

memory enhancements for Females– Correlations between Right Amygdala – emotional

memory enhancements for Males

• We found no significant gender differences due to low statistical power for such a comparison

CNPCNPStill

• We have demonstrated that complex emotional learning is led by top-down processes from the frontal lobe toward the amygdala.

• This type of learning is more complicated than conditioned fear therefore the learning circuit is more complex.

• The top-down processes demonstrate that the cognition here is “emotion free”.

• The amygdala might still play a role in the modulation of learning material delivered to the memory areas. (it does! data not shown here ;-D)