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

Learn the fundamentals of DCM for ERPs/ERFs, including functional vs effective connectivity, causal architecture, and estimating brain area coupling. Explore DCM specifications, neural mass models, and Bayesian model comparison.

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

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

  2. Dynamic Causal ModellingforERPs/ERFs (I) functional connectivity vs. effective connectivity causal architecture of interactions estimated by perturbing the system and measuring the response 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. differences in the evoked responses changes in effective connectivity

  3. output eq. state eq. Dynamic Causal ModellingforERPs/ERFs (II) neural mass model 3 area model Supra-granular M/EEG parameters 2 3 neuronal states Layer 4 1 Infra-granular input Intrinsic Forward Backward Lateral Extrinsic Input u David et al., 2006

  4. 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.

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

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

  7. IFG IFG IFG Forward and Forward - F Backward - B Backward - FB STG STG STG STG STG STG STG A1 A1 A1 A1 A1 A1 input input input Forward Forward Forward Backward Backward Backward Lateral Lateral Lateral DCM specification (IV) – testing different models modulation of effective connectivity

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

  9. Forward and Backward - FB q µ q q p ( | y ) p ( y | ) p ( ) 1 1 q µ q q q p ( | y , y ) p ( y | ) p ( y | ) p ( ) 1 2 2 1 q q µ p ( y | ) p ( | y ) 2 1 ... q µ q q q p ( | y ,..., y ) p ( y | ) p ( | y )... p ( | y ) - 1 N N N 1 1 DCM output (II) group Parameters at group level? IFG 0.60 (100%) 1.40 (100%) STG STG 1.58 (100%) 2.65 (100%) 2.17 (100%) 17.95 (100%) A1 A1 input Forward Neumann and Lohmann, 2003 Backward Lateral

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

  11. STG A1 IFG 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.

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