A DCM for resting state fMRI

Karl J. Friston, Joshua Kahan, Bharat Biswal, Adeel Razi

Research output: Contribution to journalArticleResearchpeer-review

164 Citations (Scopus)

Abstract

This technical note introduces a dynamic causal model (DCM) for resting state fMRI time series based upon observed functional connectivity-as measured by the cross spectra among different brain regions. This DCM is based upon a deterministic model that generates predicted crossed spectra from a biophysically plausible model of coupled neuronal fluctuations in a distributed neuronal network or graph. Effectively, the resulting scheme finds the best effective connectivity among hidden neuronal states that explains the observed functional connectivity among haemodynamic responses. This is because the cross spectra contain all the information about (second order) statistical dependencies among regional dynamics. In this note, we focus on describing the model, its relationship to existing measures of directed and undirected functional connectivity and establishing its face validity using simulations. In subsequent papers, we will evaluate its construct validity in relation to stochastic DCM and its predictive validity in Parkinson's and Huntington's disease.

Original languageEnglish
Pages (from-to)396-407
Number of pages12
JournalNeuroImage
Volume94
DOIs
Publication statusPublished - 1 Jul 2014
Externally publishedYes

Keywords

  • Bayesian
  • Dynamic causal modelling
  • Effective connectivity
  • FMRI
  • Functional connectivity
  • Graph
  • Resting state

Cite this