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

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Systems Neuroscience

Background:

• Dynamic Causal Modelling (DCM) is a leading technique for inferring effective connectivity from neuroimaging data.
• DCM has evolved over 15 years, with parallel advancements in neural models and statistical methods.
• Research in cognitive and clinical neuroscience drives DCM development.

Purpose of the Study:

• To provide a detailed guide to Dynamic Causal Modelling (DCM) for functional Magnetic Resonance Imaging (fMRI) analysis.
• To review the current implementation of DCM and showcase recent group-level connectivity analysis developments.
• To offer reproducible analyses with accompanying data and instructions for SPM software.

Main Methods:

• Detailed walkthrough of an exemplar fMRI analysis using DCM.
• Review of current DCM implementation and statistical routines.
• Demonstration of group-level connectivity analysis techniques.

Main Results:

• The guide focuses on DCM for fMRI, detailing its application and recent advancements.
• Accompanying data and instructions enable reproduction of analyses using SPM software.
• The companion paper extends to group-level modelling and cross-subject connectivity testing.

Conclusions:

• This guide offers a comprehensive resource for applying DCM to fMRI data.
• It facilitates understanding and implementation of advanced group-level connectivity analyses.
• The presented methods support robust inference of effective connectivity across subjects and modalities.