Hierarchical modelling of functional brain networks in population and individuals from big fMRI data
A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose...
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doaj-e6e3c135d20e4d2db57fa287a6c0e5de2021-10-05T04:18:47ZengElsevierNeuroImage1095-95722021-11-01243118513Hierarchical modelling of functional brain networks in population and individuals from big fMRI dataSeyedeh-Rezvan Farahibozorg0Janine D. Bijsterbosch1Weikang Gong2Saad Jbabdi3Stephen M. Smith4Samuel J. Harrison5Mark W. Woolrich6FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom; Corresponding author.Department of Radiology, Washington University School of Medicine, St. Louis, United StatesFMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United KingdomFMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United KingdomFMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United KingdomFMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom; Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland; New Zealand Brain Research Institute, University of Otago, Christchurch, New ZealandFMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom; OHBA, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, Oxford University, Oxford, United KingdomA major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the model's utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data.http://www.sciencedirect.com/science/article/pii/S1053811921007862sPROFUMOHierarchical network modellingBig data fMRISingle subject connectivityStochastic inferenceCognition prediction |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Seyedeh-Rezvan Farahibozorg Janine D. Bijsterbosch Weikang Gong Saad Jbabdi Stephen M. Smith Samuel J. Harrison Mark W. Woolrich |
spellingShingle |
Seyedeh-Rezvan Farahibozorg Janine D. Bijsterbosch Weikang Gong Saad Jbabdi Stephen M. Smith Samuel J. Harrison Mark W. Woolrich Hierarchical modelling of functional brain networks in population and individuals from big fMRI data NeuroImage sPROFUMO Hierarchical network modelling Big data fMRI Single subject connectivity Stochastic inference Cognition prediction |
author_facet |
Seyedeh-Rezvan Farahibozorg Janine D. Bijsterbosch Weikang Gong Saad Jbabdi Stephen M. Smith Samuel J. Harrison Mark W. Woolrich |
author_sort |
Seyedeh-Rezvan Farahibozorg |
title |
Hierarchical modelling of functional brain networks in population and individuals from big fMRI data |
title_short |
Hierarchical modelling of functional brain networks in population and individuals from big fMRI data |
title_full |
Hierarchical modelling of functional brain networks in population and individuals from big fMRI data |
title_fullStr |
Hierarchical modelling of functional brain networks in population and individuals from big fMRI data |
title_full_unstemmed |
Hierarchical modelling of functional brain networks in population and individuals from big fMRI data |
title_sort |
hierarchical modelling of functional brain networks in population and individuals from big fmri data |
publisher |
Elsevier |
series |
NeuroImage |
issn |
1095-9572 |
publishDate |
2021-11-01 |
description |
A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the model's utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data. |
topic |
sPROFUMO Hierarchical network modelling Big data fMRI Single subject connectivity Stochastic inference Cognition prediction |
url |
http://www.sciencedirect.com/science/article/pii/S1053811921007862 |
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