TY - JOUR
T1 - A guide to group effective connectivity analysis, part 1
T2 - First level analysis with DCM for fMRI
AU - Zeidman, Peter
AU - Jafarian, Amirhossein
AU - Corbin, Nadège
AU - Seghier, Mohamed L.
AU - Razi, Adeel
AU - Price, Cathy J.
AU - Friston, Karl J.
N1 - Funding Information:
The Wellcome Centre for Human Neuroimaging is supported by core funding from Wellcome [ 203147/Z/16/Z ]. We thank Wiktor Olszowy for helpful feedback on the manuscript.
Publisher Copyright:
© 2019 The Authors
PY - 2019/10/15
Y1 - 2019/10/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85067896826&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2019.06.031
DO - 10.1016/j.neuroimage.2019.06.031
M3 - Article
C2 - 31226497
AN - SCOPUS:85067896826
SN - 1053-8119
VL - 200
SP - 174
EP - 190
JO - NeuroImage
JF - NeuroImage
ER -