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Dynamic causal Modelling for evoked responses Stefan Kiebel Wellcome Trust Centre for Neuroimaging UCL

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Overview of the talk 1 M/EEG analysis 2 Dynamic Causal Modelling 3 Bayesian model inversion 4 Examples

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Overview of the talk 1 M/EEG analysis 2 Dynamic Causal Modelling 3 Bayesian model inversion 4 Examples

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Electroencephalography (EEG) time channels trial type 1 trial type 2 time (ms) amplitude (μV)

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M/EEG analysis at sensor level channels trial type 1 trial type 2 time Approach: Reduce evoked response to a few variables, e.g.: The average over a few channels in peri-stimulus time. What else can we try to reduce the evoked response to a few variables?

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Overview of the talk 1 M/EEG analysis 2 Dynamic Causal Modelling 3 Bayesian model inversion 4 Examples

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Dynamic Causal Modelling A1 A2 ??? Build a model for spatiotemporal data: Assume that both ERPs are generated by temporal dynamics of a few sources Describe temporal dynamics by differential equations Each source projects to the sensors, following physical laws Solve for the model s parameters using Bayesian model inversion Dynamic Causal Modelling

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pseudo-random auditory sequence 80% standard tones – 500 Hz 20% deviant tones – 550 Hz time standardsdeviants Oddball paradigm raw data preprocessing data reduction to principal spatial modes (explaining most of the variance) convert to matlab file filter epoch down sample artifact correction average ERPs / ERFs 128 EEG scalp electrodes mode 2 mode 1 mode 3 time (ms) Mismatch negativity (MMN)

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Model for mismatch negativity Garrido et al., PNAS, 2008

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Macro- and meso-scale internal granular layer internal pyramidal layer external pyramidal layer external granular layer AP generation zonesynapses macro-scalemeso-scalemicro-scale

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The generative model Source dynamics f states x parameters θ Input u Evoked response data y Spatial forward model g

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

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Spatial model Depolarisation of pyramidal cells Spatial model Sensor data

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Overview of the talk 1 M/EEG analysis 2 Dynamic Causal Modelling 3 Bayesian model inversion 4 Examples

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Bayesian model inversion Measured data Specify generative forward model (with prior distributions of parameters) Expectation-Maximization algorithm Iterative procedure: 1.Compute model response using current set of parameters 2.Compare model response with data 3.Improve parameters, if possible 1.Posterior distributions of parameters 2.Model evidence

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Model comparison: Which model is the best? Model 1 data y Model 2... Model n best? Model comparison: Select model with highest model evidence

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Overview of the talk 1 M/EEG analysis 2 Dynamic Causal Modelling 3 Bayesian model inversion 4 Examples

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Mismatch negativity (MMN) Garrido et al., PNAS, 2008

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Mismatch negativity (MMN) Garrido et al., PNAS, 2008 time (ms)

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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 Another (MMN) example

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Bayesian Model Comparison Forward (F) Backward (B) Forward and Backward (FB) subjects log-evidence Group level Group model comparison Garrido et al., (2007), NeuroImage

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Ongoing work CC Chen et al.: Dynamic Causal Modelling of induced responses, Neuroimage (in press). CC Chen et al.: Forward and backward connections in the brain: A DCM study of functional asymmetries in face processing, in preparation. R Moran et al.: A neural mass model of spectral responses in electrophysiology, Neuroimage (2007) R Moran et al.: Bayesian estimation of synaptic physiology from the spectral responses of neural masses, Neuroimage (in press) Fastenrath et al., Dynamic Causal Modelling for M/EEG: Spatial and temporal symmetry constraints, submitted Daunizeau et al.: Dynamic Causal Modelling of distributed electromagnetic responses, in preparation Marreiros et al.: Population dynamics under the Laplace assumption, in preparation

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Summary DCM combines state-equations for neural mass dynamics with spatial forward model. Differences between evoked responses are modelled as modulation of connectivity between/within sources. Bayesian model comparison allows one to compare many different models and identify the best one. Make inference about posterior distribution of parameters (e.g., effective connectivity, location of dipoles, etc.). Many extensions to DCM for M/EEG will be available in SPM8.

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Thanks to Karl Friston Marta Garrido Jean Daunizeau

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