  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 tutorial, 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 first part of the
tutorial (current paper), we focus on issues specific to DCM for fMRI,
unpacking the relevant theory and highlighting practical considerations. In
particular, we clarify the assumptions (i.e., priors) used in DCM for fMRI and
how to interpret the model parameters. This tutorial 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.
