Spectral dynamic causal modeling: A didactic introduction and its relationship with functional connectivity

Author:

Novelli Leonardo1ORCID,Friston Karl2ORCID,Razi Adeel123ORCID

Affiliation:

1. Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Australia

2. Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom

3. CIFAR Azrieli Global Scholars Program, Toronto, Canada

Abstract

Abstract We present a didactic introduction to spectral dynamic causal modeling (DCM), a Bayesian state-space modeling approach used to infer effective connectivity from noninvasive neuroimaging data. Spectral DCM is currently the most widely applied DCM variant for resting-state functional MRI analysis. Our aim is to explain its technical foundations to an audience with limited expertise in state-space modeling and spectral data analysis. Particular attention will be paid to cross-spectral density, which is the most distinctive feature of spectral DCM and is closely related to functional connectivity, as measured by (zero-lag) Pearson correlations. In fact, the model parameters estimated by spectral DCM are those that best reproduce the cross-correlations between all measurements—at all time lags—including the zero-lag correlations that are usually interpreted as functional connectivity. We derive the functional connectivity matrix from the model equations and show how changing a single effective connectivity parameter can affect all pairwise correlations. To complicate matters, the pairs of brain regions showing the largest changes in functional connectivity do not necessarily coincide with those presenting the largest changes in effective connectivity. We discuss the implications and conclude with a comprehensive summary of the assumptions and limitations of spectral DCM.

Funder

Australian Research Council

Australian National Health and Medical Research

Wellcome Centre for Human Neuroimaging

Canada-UK Artificial Intelligence Initiative

Publisher

MIT Press

Subject

Applied Mathematics,Artificial Intelligence,Computer Science Applications,General Neuroscience

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