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Dynamic Causal Modelling (DCM) is the predominant method for inferringeffective connectivity from neuroimaging data. In the 15 years since itsintroduction, the neural models and statistical routines in DCM have developedin parallel, driven by the needs of researchers in cognitive and clinicalneuroscience. In this tutorial, we step through an exemplar fMRI analysis indetail, reviewing the current implementation of DCM and demonstrating recentdevelopments in group-level connectivity analysis. In the first part of thetutorial (current paper), we focus on issues specific to DCM for fMRI,unpacking the relevant theory and highlighting practical considerations. Inparticular, we clarify the assumptions (i.e., priors) used in DCM for fMRI andhow to interpret the model parameters. This tutorial is accompanied by all thenecessary data and instructions to reproduce the analyses using the SPMsoftware. In the second part (in a companion paper), we move from subject-levelto group-level modelling using the Parametric Empirical Bayes framework, andillustrate how to test for commonalities and differences in effectiveconnectivity across subjects, based on imaging data from any modality.

A tutorial on group effective connectivity analysis, part 1: first level analysis with DCM for fMRI