Dynamic Causal Modelling

Dynamic 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

Attention to Visual Motion Stimuli 250 radially moving dots at 4.7 degrees/s Pre-Scanning 5 x 30s trials with 5 speed changes (reducing to 1%) Task - detect change in radial velocity Scanning (no speed changes) 6 normal subjects, 4 100 scan sessions; each session comprising 10 scans of 4 different condition e.g. F A F N F A F N S ................. F – fixation S – stationary dots N – moving dots A – attended moving dots Buchel et al. 1997 Experimental Factors • Photic Stimulation, S-F • Motion, N-S • Attention, A-N

Functional Specialisation Q. In what areas does the ‘motion’ factor change activity ? Univariate Analysis

Attention V2 Functional Integration Multivariate Analysis SPM{Z} Q. In what areas is activity correlated with activity in V2 ? Q. In what areas does the ‘attention’ factor change this correlation ? V5 activity time attention V5 activity no attention V2 activity

Attention V2 Functional Integration PPI Question: Psycho-Physiological Interaction Q. In what areas is activity correlated with activity in V2 ? Q. In what areas does the ‘attention’ factor change this correlation ? Gitelman et al. 2003 • Larger Networks: • Structural Equation Modelling (SEM) • Dynamic Causal Modelling (DCM) Activity = ‘Hemodynamic’ (SEM) = ‘Neuronal’ (PPI/DCM)

Z4 Z5 Z2 Z3 Aim of DCM 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

DCM Theory • A Model of Neuronal Activity • A Model of Hemodynamic Activity • Fitting the Model • Making inferences

Z2 Set u2 Stimuli u1 Z4 Z5 Z1 Z2 Z3 Model of Neuronal Activity Nonlinear, systems-level model

Bilinear Dynamics a53 Set u2 Stimuli u1

u 1 u 2 Z 1 Z 2 Bilinear Dynamics: Positive transients Stimuli u1 Set u2 - + Z1 - + + Z2 - -

DCM: A model for fMRI Set u2 Stimuli u1 Causality: set of differential equations relating change in one area to change in another

The hemodynamic model Buxton, Mandeville, Hoge, Mayhew.

Impulse response Hemodynamics BOLD is sluggish

Model estimation and inference Unknown neural parameters, N={A,B,C} Unknown hemodynamic parameters, H Vague priors and stability priors, p(N) Informative priors, p(H) Observed BOLD time series, B. Data likelihood, p(B|H,N) = Gauss (B-Y) Bayesian inference p(N|B) a p(B|N) p(N) Laplace Approximation

P(A(ij)) = N (mA(i,j),SA(ij)) P(B(ij)) = N (mB(i,j),SB(ij)) A1 A2 P(C(ij)) = N (mC(i,j),SC(ij)) WA Posterior Distributions mA mB mC Show connections for which A(i,j) > Thresh with probability > 90%

Z4 Z5 Z2 Z3 Practical Steps of DCM Design matrix 1) Standard Analysis of fMRI Data 2) Statistical Parametric Maps 3) Construction of a Connectivity Model 4) Evaluation of the Connectivity Model SPMs

Single word processing at different rates “Dog” “Mountain” SPM{F} “Gate” Friston et al. 2003 Functional localisation of primary and secondary auditory cortex and Wernicke’s area

A2 A1 WA Time Series Auditory stimulus, u1 Adaptation variable, u2

Auditory stimulus, u1 Adaptation variable, u2 Dynamic Causal Model u1 enters A1 and is also allowed to affect all intrinsic self-connections A2 Model allows for full intrinsic connectivity . A1 u1 u2 is allowed to affect all intrinsic connections between regions . WA

A2 -.62 (99%) .92 (100%) .37 (100%) A1 .47 (98%) .38 (94%) .37 (91%) WA -.51 (99%) Inferred Neural Network Intrinsic connections are feed-forward Neuronal saturation with increasing stimulus frequency in A1 & WA Time-dependent change in A1-WA connectivity

DCM: Category Effects Mechelli et al. 2003 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

Results Ishai 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 Medial Fusiform Lateral Fusiform Inferor Temporal L R p<0.05 (corrected)

QUESTION: 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?

DCM Model (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 Superior Parietal Category Effects Face responsive area Chair responsive area House responsive area V3 Visual Objects DCM was used to estimate Extrinsic, Intrinsic and Modulatory connections at the neuronal level using Bayesian framework. Inferences were made at 95%

Hypothesis 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.

Lateral Fusiform Inferior Temporal Medial Fusiform DCM Results The extrinsic connection from the experimental input to V3 was significant in all subjects Sup Par House responsive area Face responsive area Chair responsive area V3 Visual Objects

Lateral Fusiform Inferior Temporal Medial Fusiform DCM Results The intrinsic connections between V3, superior parietal and the category-responsive regions were significant Sup Par House responsive area Face responsive area Chair responsive area V3 Visual Objects

DCM Results The modulatory connections showed that category effects in the occipital and temporal cortex were mediated by inputs from V3. Sup Par Medial Fusiform Lateral Fusiform Inferior Temporal House responsive area Face responsive area Chair responsive area Equivalent top-down effect was not significant V3 Visual Objects

Summary • Studies of functional integration look at experimentally induced changes in connectivity • In PPI’s and DCM this connectivity is at the neuronal level • DCM: Neurodynamics and hemodynamics • Inferences about large-scale neuronal networks

Dynamic Causal Modelling