Dynamic Causal ModellingDynamic Causal Modelling

Will Penny

Wellcome Department of Imaging Neuroscience, University College London, UK

Cyclotron Research Centre, University of Liege, April 2003

Outline

Functional specialisation and integration

DCM theory

DCM for auditory word processing

DCM for category effects

Outline

Functional specialisation and integration

DCM theory

DCM for auditory word processing

DCM for category effects

Attention to Visual MotionAttention to Visual Motion

StimuliStimuli

250 radially moving dots at 4.7 degrees/s250 radially moving dots at 4.7 degrees/s

Pre-ScanningPre-Scanning

5 x 30s trials with 5 speed changes (reducing to 1%)5 x 30s trials with 5 speed changes (reducing to 1%)

Task - detect change in radial velocityTask - detect change in radial velocity

ScanningScanning (no speed changes) (no speed changes)

6 normal subjects, 4 100 scan sessions;6 normal subjects, 4 100 scan sessions;

each session comprising 10 scans of 4 different conditioneach session comprising 10 scans of 4 different condition

e.g. F A F N F A F N S .................e.g. F A F N F A F N S .................

F – fixationF – fixation

S – stationary dots S – stationary dots

N – moving dotsN – moving dots

A – attended moving dotsA – attended moving dots

1. Photic Stimulation, S-F2. Motion, N-S3. Attention, A-N

Experimental Factors

Buchel et al. 1997

Functional Specialisation

Q. In what areas does the ‘motion’ factor change activity ?

Univariate Analysis

AttentionAttention

V2V2

attention

no attention

V2 activity

V5

acti

vity

SPM{Z}

time

V5

acti

vity

Functional Integration

Q. In what areas is activity correlated with activity in V2 ?

Q. In what areas does the ‘attention’ factor change this correlation ?

Multivariate Analysis

AttentionAttention

V2V2

Functional Integration

Q. In what areas is activity correlated with activity in V2 ?

Q. In what areas does the ‘attention’ factor change this correlation ?

PPI Question:

Psycho-PhysiologicalInteraction

Larger Networks:

1. Structural Equation Modelling (SEM)2. Dynamic Causal Modelling (DCM)

Activity = ‘Hemodynamic’ (SEM) = ‘Neuronal’ (PPI/DCM)

Gitelman et al. 2003

Outline

Functional specialisation and integration

DCM theory

DCM for auditory word processing

DCM for category effects

To estimate and make inferences about

(1) the influence that one neural system exerts over another (i.e. effective connectivity)

(2) how this is affected by the experimental context

Aim of DCM

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Z3

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DCM Theory

A Model of Neuronal ActivityA Model of Hemodynamic ActivityFitting the ModelMaking inferences

Model of Neuronal Activity

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DCM: A model for fMRI

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State Equations

Buxton,Mandeville,Hoge,Mayhew.

Hemodynamics

Impulseresponse

BOLD is sluggish

Model estimation and inference

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Bayesian inference p(N|B) p(B|N) p(N)

LaplaceApproximation

Posterior Distributions

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P(A(ij)) = N (A(i,j),ij))

P(B(ij)) = N (B(i,j),ij))

P(C(ij)) = N (C(i,j),Cij))

Show connections for which A(i,j) > Threshwith probability > 90%

1) Standard Analysis of fMRI Data

2) Statistical Parametric Maps

3) Construction of a Connectivity Model

4) Evaluation of the Connectivity Model

Design matrix

SPMs

Practical Steps of DCM

Z2

Z4

Z3

Z5

Outline

Functional specialisation and integration

DCM theory

DCM for auditory word processing

DCM for category effects

Single word processing at different rates

SPM{F}

“Dog”

“Mountain”

“Gate”

Functional localisation of primary and secondary auditory cortex and Wernicke’s area

Friston et al.2003

Time Series

A1

WA

A2Auditory stimulus, u1

Adaptation variable, u2

Dynamic Causal Model

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Auditory stimulus, u1

Model allows forfull intrinsicconnectivity

u1 Adaptation variable, u2

u1 enters A1 and is also allowed to affect all intrinsic self-connections

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u2 is allowed to affect all intrinsic connections betweenregions

Inferred Neural Network

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-.62 (99%)

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.37 (100%)

Intrinsic connectionsare feed-forward

Neuronal saturationwith increasing stimulus frequencyin A1 & WA

Time-dependentchange in A1-WAconnectivity

Outline

Functional specialisation and integration

DCM theory

DCM for auditory word processing

DCM for category effects

The fMRI data were originally acquired by Ishai et al. (1999; 2000) and provided by the National fMRI Data Center (www.fmridc.org)

2x3 Factorial Design:

Tasks were (1) passive viewing (2) delayed matching Stimuli were pictures of(1) Houses(2) Faces (3) Chairs

Baselines involved scrambled pictures of Houses, Faces and Chairs

DCM: Category Effects Mechelli et al. 2003

ResultsIshai et al. found that...

(1) all categories activated a distributed system including bilateral fusiform, inferior occipital, mid-occipital and inferior temporal regions

(2) within this network, distinct regions in the occipital and temporal cortex responded preferentially to Faces, House and Chairs

L R

Medial Fusiform

Lateral Fusiform

Inferor Temporal

p<0.05 (corrected)

Are the category effects reported by Ishai et al. (1999; 2000) in the occipital and temporal cortex

mediated by Bottom-up or Top-down mechanisms?

QUESTION:

(1) V3 and the Superior Parietal area (that did not show category effects) (2) Temporal and Occipital areas (that did show category effects)

(3) Extrinsic connections

(4) Intrinsic Connections

(5) Modulatory Connections

Chair responsive

area

V3

SuperiorParietal

Houseresponsive

area

Visual Objects

Category Effects

DCM was used to estimate Extrinsic, Intrinsic and Modulatory connectionsat the neuronal level using Bayesian framework. Inferences were made at 95%

DCM Model

Faceresponsivearea

We hypothesised a significant influence of category on the intrinsic connections which would account for the category effects observed in the occipital and temporal cortex.

(i) One possibility was that this influence would be expressed through the connections from V3 to the category-responsive areas – which would suggest bottom-up modulation.

(ii) Another possibility was that the influence of object category on the connectivity parameters was expressed in the connections from parietal cortex to the category-responsive areas – thereby indicating top-down modulation.

(iii) Finally, it was possible that object-specificity was conferred by connections from both V3 and parietal cortex.

Hypothesis

DCM ResultsThe extrinsic connection from the experimental input to V3 was significant in all subjects

V3

Sup Par

Visual Objects

LateralFusiform

InferiorTemporal

Medial

Fusiform

Face responsive

area

Chair responsive

area

House responsive

area

DCM ResultsThe intrinsic connections between V3, superior parietal and the category-responsive regions were significant

V3

Sup Par

Visual Objects

LateralFusiform

InferiorTemporal

Medial

Fusiform

Face responsive

area

Chair responsive

area

House responsive

area

DCM ResultsThe modulatory connections showed that category effects in the occipital and temporal cortex were mediated by inputs from V3.

V3

Sup Par

Visual Objects

LateralFusiform

Face responsive

area

InferiorTemporal

Chair responsive

area

MedialFusiform

House responsive

area

Equivalent top-down effectwas not significant

DCM ResultsThe modulatory connections showed that category effects in the occipital and temporal cortex were mediated by inputs from V3.

V3

Sup Par

Visual Objects

LateralFusiform

Face responsive

area

InferiorTemporal

Chair responsive

area

MedialFusiform

House responsive

area

Equivalent top-down effectwas not significant

DCM ResultsThe modulatory connections showed that category effects in the occipital and temporal cortex were mediated by inputs from V3.

V3

Sup Par

Visual Objects

LateralFusiform

Face responsive

area

InferiorTemporal

Chair responsive

area

MedialFusiform

House responsive

area

Equivalent top-down effectwas not significant

Summary

Studies of functional integration look at

experimentally induced changes in connectivityIn PPI’s and DCM this connectivity is at the

neuronal levelDCM: Neurodynamics and hemodynamicsInferences about large-scale neuronal networks