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Neuroimage. 2014 Jul 1;94:396-407. doi: 10.1016/j.neuroimage.2013.12.009. Epub 2013 Dec 15.

A DCM for resting state fMRI.

Author information

1
The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK. Electronic address: k.firston@ucl.ac.uk.
2
Sobell Department of Motor Neuroscience & Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK.
3
The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK.
4
The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK; Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan.

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.

KEYWORDS:

Bayesian; Dynamic causal modelling; Effective connectivity; Functional connectivity; Graph; Resting state; fMRI

PMID:
24345387
PMCID:
PMC4073651
DOI:
10.1016/j.neuroimage.2013.12.009
[Indexed for MEDLINE]
Free PMC Article

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