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Dynamic Causal Modeling in Cognitive Neuroscience: Practical Steps for Analysis

Learn how to conduct Dynamic Causal Modeling (DCM) analysis in cognitive neuroscience, from setting up a multifactorial design to defining models and interpreting outputs. This guide covers the essential steps and considerations for effective DCM analysis.

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Dynamic Causal Modeling in Cognitive Neuroscience: Practical Steps for Analysis

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  1. D Y N A M I C C A U S A L M O D E L L I N G z = (A +  uj Bj)z + Cu ... the (un)practical side . j

  2. at least 1 factor for stimulus input e.g. low vs. high pain at least 1 factor for contextual input e.g. attentional set What to keep in mind if you want to do a DCM analysis • Multifactorial design ( ... is optimal)

  3. 2. Defined model to test DCM is not an exploratory technique! 1st inaccuracy 2nd inaccuracy 3. TR < 2 sec

  4. easy hard T A S K (attention) u2 How to define a model for DCM: from hypothesis to model ... Example: P A I N u1 low high

  5. . z = (A +  uj Bj)z + Cu DLPFC j INSULA low pain high pain -

  6. easy task hard task DLPFC hard task INSULA low pain high pain Direct ...

  7. easy task hard task DLPFC hard task INSULA SI low pain high pain ... or indirect influence? currently not possible in DCM!

  8. easy task DORSOLATERAL PREFRONTAL hard task hard task easy task INSULA ORBITOFRONTAL hard task low pain high pain one possible model

  9. The steps of a DCM analysis 1. set up (new) design matrix 2. define VOIs 3. enter DCM model 4. read output

  10. Setting up the (new) design matrix Regressors in ‚regular‘ SPM analysis: easy task, low pain easy task, high pain hard task, low pain hard task, high pain Regressors for DCM analysis: low pain high pain hard task easy task

  11. Define volumes of interest • 1. DCM for a single subject analysis (i.e. no 2nd-level analysis intended): • determine representative coordinate for each brain region from the appropriate contrast (e.g. choose coordinate for SI from main effect pain) • Subject specific DCM, but results will eventually be entered into a 2nd-level analysis: • determine group maximum for the area of interest (e.g. from RFX analysis) in the appropriate contrast • in each subject, jump to local maximum nearest to the group maximum, using the same contrast and a liberal threshold (e.g. p<0.05, uncorrected)

  12. Setting up the DCM model (1)

  13. Setting up the DCM model (2)

  14. insula OFC DLPFC insula OFC DLPFC Read output: latent (intrinsic) connectivity (A)

  15. high pain low pain hard task easy task Read output: Modulation of connections (B)

  16. easy high low hard insula OFC DLPFC Read output: Input (C)

  17. ... not the model ... not a short TR S O C I A L S U P P O R T What you really need for a DCM analysis

  18. ‚hm .... weird ............ but also encouraging, don‘t you think???‘ (Ben) ‚ok, Katja, don‘t worry! ............ That only means that there must be a better model!!‘ (Klaas)

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