A guide to group effective connectivity analysis, part 1: First level analysis with DCM for fMRI

Abstract

Dynamic Causal Modelling (DCM) is the predominant method for inferring effective connectivity from neuroimaging data. In the 15 years since its introduction, the neural models and statistical routines in DCM have developed in parallel, driven by the needs of researchers in cognitive and clinical neuroscience. In this guide, we step through an exemplar fMRI analysis in detail, reviewing the current implementation of DCM and demonstrating recent developments in group-level connectivity analysis. In the appendices, we detail the theory underlying DCM and the assumptions (i.e., priors) in the models. In the first part of the guide (current paper), we focus on issues specific to DCM for fMRI. This is accompanied by all the necessary data and instructions to reproduce the analyses using the SPM software. In the second part (in a companion paper), we move from subject-level to group-level modelling using the Parametric Empirical Bayes framework, and illustrate how to test for commonalities and differences in effective connectivity across subjects, based on imaging data from any modality.

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Cited by papers we hold 3

• Similar network compositions, but distinct neural dynamics underlying belief updating in environments with and without explicit outcomes · 2022
  Not checked
• Altered effective connectivity within an oculomotor control network in individuals with schizophrenia · 2021
  Not checked
• Functional specialization within the inferior parietal lobes across cognitive domains · 2021
  No notice

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