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Neuroimage. 2015 Feb 1;106:1-14. doi: 10.1016/j.neuroimage.2014.11.027. Epub 2014 Nov 21.

Construct validation of a DCM for resting state fMRI.

Author information

1
The Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG, UK; Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan. Electronic address: a.razi@ucl.ac.uk.
2
Sobell Department of Motor Neuroscience & Movement Disorders, Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK.
3
The Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG, UK; Institute of Cognitive Neuroscience, University College London, 17 Queen Square, London WC1N 3AR, UK.
4
The Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG, UK.

Abstract

Recently, there has been a lot of interest in characterising the connectivity of resting state brain networks. Most of the literature uses functional connectivity to examine these intrinsic brain networks. Functional connectivity has well documented limitations because of its inherent inability to identify causal interactions. Dynamic causal modelling (DCM) is a framework that allows for the identification of the causal (directed) connections among neuronal systems--known as effective connectivity. This technical note addresses the validity of a recently proposed DCM for resting state fMRI--as measured in terms of their complex cross spectral density--referred to as spectral DCM. Spectral DCM differs from (the alternative) stochastic DCM by parameterising neuronal fluctuations using scale free (i.e., power law) forms, rendering the stochastic model of neuronal activity deterministic. Spectral DCM not only furnishes an efficient estimation of model parameters but also enables the detection of group differences in effective connectivity, the form and amplitude of the neuronal fluctuations or both. We compare and contrast spectral and stochastic DCM models with endogenous fluctuations or state noise on hidden states. We used simulated data to first establish the face validity of both schemes and show that they can recover the model (and its parameters) that generated the data. We then used Monte Carlo simulations to assess the accuracy of both schemes in terms of their root mean square error. We also simulated group differences and compared the ability of spectral and stochastic DCMs to identify these differences. We show that spectral DCM was not only more accurate but also more sensitive to group differences. Finally, we performed a comparative evaluation using real resting state fMRI data (from an open access resource) to study the functional integration within default mode network using spectral and stochastic DCMs.

KEYWORDS:

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

PMID:
25463471
PMCID:
PMC4295921
DOI:
10.1016/j.neuroimage.2014.11.027
[Indexed for MEDLINE]
Free PMC Article

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