Large-scale DCMs for resting-state fMRI

This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity. This identification can be contrasted with functional connectivity methods based on symmetric correlations...

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Main Authors: Adeel Razi, Mohamed L. Seghier, Yuan Zhou, Peter McColgan, Peter Zeidman, Hae-Jeong Park, Olaf Sporns, Geraint Rees, Karl J. Friston
Format: Article
Language:English
Published: The MIT Press 2017-01-01
Series:Network Neuroscience
Subjects:
Online Access:https://www.mitpressjournals.org/doi/pdf/10.1162/NETN_a_00015
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spelling doaj-7317c6900a7b4d7a9b0899543f77c9e42020-11-25T00:34:55ZengThe MIT PressNetwork Neuroscience2472-17512017-01-011322224110.1162/NETN_a_00015NETN_a_00015Large-scale DCMs for resting-state fMRIAdeel Razi0Mohamed L. Seghier1Yuan Zhou2Peter McColgan3Peter Zeidman4Hae-Jeong Park5Olaf Sporns6Geraint Rees7Karl J. Friston8The Wellcome Trust Centre for Neuroimaging, University College London, London, United KingdomThe Wellcome Trust Centre for Neuroimaging, University College London, London, United KingdomThe Wellcome Trust Centre for Neuroimaging, University College London, London, United KingdomHuntington’s Disease Centre, Institute of Neurology, University College London, London, United KingdomThe Wellcome Trust Centre for Neuroimaging, University College London, London, United KingdomDepartment of Nuclear Medicine and BK21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Koreaof Psychological and Brain Sciences, Indiana University, Bloomington, IndianaThe Wellcome Trust Centre for Neuroimaging, University College London, London, United KingdomThe Wellcome Trust Centre for Neuroimaging, University College London, London, United KingdomThis paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity. This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions. The ensuing graphs are directed and weighted, hence providing a neurobiologically plausible characterization of connectivity in terms of excitatory and inhibitory coupling. Furthermore, we show that the use of Bayesian model reduction to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied to large symmetric (functional connectivity) graphs. Using empirical fMRI data, we show that spectral DCM furnishes connectivity estimates on large graphs that correlate strongly with the estimates provided by stochastic DCM. Furthermore, we increase the efficiency of model inversion using functional connectivity modes to place prior constraints on effective connectivity. In other words, we use a small number of modes to finesse the potentially redundant parameterization of large DCMs. We show that spectral DCM—with functional connectivity priors—is ideally suited for directed graph theoretic analyses of resting-state fMRI. We envision that directed graphs will prove useful in understanding the psychopathology and pathophysiology of neurodegenerative and neurodevelopmental disorders. We will demonstrate the utility of large directed graphs in clinical populations in subsequent reports, using the procedures described in this paper.https://www.mitpressjournals.org/doi/pdf/10.1162/NETN_a_00015Dynamic causal modelingEffective connectivityFunctional connectivityResting statefMRIGraph theoryBayesian inferenceLarge-scale networks
collection DOAJ
language English
format Article
sources DOAJ
author Adeel Razi
Mohamed L. Seghier
Yuan Zhou
Peter McColgan
Peter Zeidman
Hae-Jeong Park
Olaf Sporns
Geraint Rees
Karl J. Friston
spellingShingle Adeel Razi
Mohamed L. Seghier
Yuan Zhou
Peter McColgan
Peter Zeidman
Hae-Jeong Park
Olaf Sporns
Geraint Rees
Karl J. Friston
Large-scale DCMs for resting-state fMRI
Network Neuroscience
Dynamic causal modeling
Effective connectivity
Functional connectivity
Resting state
fMRI
Graph theory
Bayesian inference
Large-scale networks
author_facet Adeel Razi
Mohamed L. Seghier
Yuan Zhou
Peter McColgan
Peter Zeidman
Hae-Jeong Park
Olaf Sporns
Geraint Rees
Karl J. Friston
author_sort Adeel Razi
title Large-scale DCMs for resting-state fMRI
title_short Large-scale DCMs for resting-state fMRI
title_full Large-scale DCMs for resting-state fMRI
title_fullStr Large-scale DCMs for resting-state fMRI
title_full_unstemmed Large-scale DCMs for resting-state fMRI
title_sort large-scale dcms for resting-state fmri
publisher The MIT Press
series Network Neuroscience
issn 2472-1751
publishDate 2017-01-01
description This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity. This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions. The ensuing graphs are directed and weighted, hence providing a neurobiologically plausible characterization of connectivity in terms of excitatory and inhibitory coupling. Furthermore, we show that the use of Bayesian model reduction to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied to large symmetric (functional connectivity) graphs. Using empirical fMRI data, we show that spectral DCM furnishes connectivity estimates on large graphs that correlate strongly with the estimates provided by stochastic DCM. Furthermore, we increase the efficiency of model inversion using functional connectivity modes to place prior constraints on effective connectivity. In other words, we use a small number of modes to finesse the potentially redundant parameterization of large DCMs. We show that spectral DCM—with functional connectivity priors—is ideally suited for directed graph theoretic analyses of resting-state fMRI. We envision that directed graphs will prove useful in understanding the psychopathology and pathophysiology of neurodegenerative and neurodevelopmental disorders. We will demonstrate the utility of large directed graphs in clinical populations in subsequent reports, using the procedures described in this paper.
topic Dynamic causal modeling
Effective connectivity
Functional connectivity
Resting state
fMRI
Graph theory
Bayesian inference
Large-scale networks
url https://www.mitpressjournals.org/doi/pdf/10.1162/NETN_a_00015
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