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...
Main Authors: | , , , , , , , , |
---|---|
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 |
id |
doaj-7317c6900a7b4d7a9b0899543f77c9e4 |
---|---|
record_format |
Article |
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 |
work_keys_str_mv |
AT adeelrazi largescaledcmsforrestingstatefmri AT mohamedlseghier largescaledcmsforrestingstatefmri AT yuanzhou largescaledcmsforrestingstatefmri AT petermccolgan largescaledcmsforrestingstatefmri AT peterzeidman largescaledcmsforrestingstatefmri AT haejeongpark largescaledcmsforrestingstatefmri AT olafsporns largescaledcmsforrestingstatefmri AT geraintrees largescaledcmsforrestingstatefmri AT karljfriston largescaledcmsforrestingstatefmri |
_version_ |
1725311429210275840 |