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Dynamic Causal Modelling for ERP/ERFs Valentina Doria Georg Kaegi Methods for Dummies 19/03/2008

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Classical ERP analysis Analyse averages over channels and select interesting peri- stimulus times condition 1 Difference between selected data Analysis of variance (Anova), over subjects Analysis of variance (Anova), over subjects Analysis at channel level. but not in brain space Analysis at channel level. but not in brain space time channels condition 2

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Source reconstruction 0 1 R L Reconstruct brain sources which generated the observed channel data Analysis at source level, but typically no model about dynamics Selected data

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New approach Develop mechanistic model for the full data, not only for selected or averaged part Use network model Explain differences in responses by change of a few interpretable parameters in generating network condition 1 condition 2

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D ynamic C ausal M odelling for ERPs/ERFs differences in the evoked responses changes in effective connectivity functional connectivity vs. effective connectivity causal architecture of interactions The aim of DCM is to estimate and make inferences about the coupling among brain areas, and how that coupling is influences by changes in the experimental contex. estimated by perturbing the system and measuring the response

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neural mass model Layer 4 Supra-granular Infra-granular Intrinsic Forward Backward Lateral Input u area model state eq. output eq. Extrinsic M/EEG neuronal states parameters input David et al., 2006 D ynamic C ausal M odelling for ERPs/ERFs (II)

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Dynamics f Input u Spatial forward model g Generative model data y parameters θ states x ERP/ERF

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Generative forward model: an example A1 A2 A4 input Forward Backward Lateral A3 4 areas, somewhere in the brain, happily working together..

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Modulation of extrinsic connectivity A1 A2 A4 Forward Backward Lateral A3 Increase in backward connection A2->A1 Increase in backward connection A2->A1 modulation input

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Four steps through the model Single source Network of sources Spatial expression in sensors Single neuronal population

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Neural mass model h t 0 x uouo 0 Input synapses Dendrites and somas Axons State-space model Neuronal convolution

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Single source Input spiny stellate cells inhibitory interneurons pyramidal cells Intrinsic connections neuronal (source) model State equations

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Extrinsic connectivity Extrinsic forward connections spiny stellate cells inhibitory interneurons pyramidal cells Extrinsic backward connections Intrinsic connections neuronal (source) model Extrinsic lateral connections State equations Output equation

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Spatial forward model Depolarisation of pyramidal cells Spatial model Sensor data

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Dynamics f Input u Spatial forward model g Generative model data y parameters θ states x ERP/ERF

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A1 A2 A4 Forward Backward Lateral A3 Data Model inversion: possible? Model Can we estimate extrinsic connectivity parameters and its modulation from data? input modulation

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Data Specify generative forward model (with prior distributions on unknown parameters) Expectation-Maximization algorithm Iterative procedure: Compute model response using current set of parameters Compare model response with data Improve parameters, if possible DCM: The basic approach Output: Posterior distributions of parameters Make inferences on parameters

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DCM specification DCM is specified by a graph of nodes (cortical areas) and edges (connections). Differences in 2 ERPs/ERFs are explained by coupling modulations, i.e., changes in connection strength. DCM doesn’t test all possible models. Is crucial to build a model biologically plausible! Different hypotheses Different models Bayesian model comparison identifies the best model/hypothesis within the universe of models/hypothesis considered.

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pseudo-random auditory sequence 80% standard tones – 1000 Hz 20% deviant tones – 2000 Hz time standardsdeviants Oddball paradigm DCM specification – put into context mode 1 mode 2 mode 3 svd raw data preprocessing data reduction to principal spatial modes (explaining most of the variance) convert to matlab file epoch down sample filter artifact correction average ERPs / ERFs

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A1 STG input STG IFG A1 STG IFG a plausible model… DCM specification – areas and connections Choice of nodes/areas? - source localization, prior knowledge from literature Choice of edges/connections? - anatomical or functional evidence

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A1 STG Forward Backward Lateral STG input A1 STG Forward Backward Lateral input A1 STG Forward Backward Lateral input Forward-FBackward-B Forward and Backward-FB STG IFG modulation of effective connectivity DCM specification – testing different models

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A1 STG Forward Backward Lateral input Forward and Backward - FB STG IFG 2.41 (100%) 4.50 (100%) 5.40 (100%) 1.74 (96%) 1.41 (99%) standard deviant 0.93 (55%) DCM output single subject reconstructed responses at source level coupling changes probability that a change occured

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A1 STG Forward Backward Lateral input Forward and Backward - FB STG IFG 2.17 (100%) (100%) 2.65 (100%) 1.58 (100%) 0.60 (100%) 1.40 (100%) group Neumann and Lohmann, 2003 DCM output Parameters at group level?

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log-evidence (log-evidence normalized to the null model) Bayesian Model Comparison subjects Forward (F) Backward (B) Forward and Backward (FB) Penny et al., 2004 DCM output DCM.F add up log-evidences for group analysis

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Summary DCM models ERPs on the basis of a network of interacting cortical areas. Differences in waveforms are explained by coupling changes among these areas. The specification of the DCM (areas and connections in the network) is a critical point. It should be biologically plausible and motivated by specific hypotheses. DCM can be used to test different hypotheses or models of connectivity. STG A1 IFG

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