What do you need to know about DCM for ERPs/ERFs to be able to use it?

D ynamic C ausal M odelling for ERPs/ERFs (I) 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

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)

DCM specification (I) 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.

pseudo-random auditory sequence 80% standard tones – 1000 Hz 20% deviant tones – 2000 Hz time standardsdeviants Oddball paradigm DCM specification (II) – 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

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

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 (IV) – testing different models

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 (I) single subject reconstructed responses at source level coupling changes probability that a change occured

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 (II) Parameters at group level?

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 (III) DCM.F add up log-evidences for group analysis

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