TY - JOUR
T1 - Hierarchical modelling of functional brain networks in population and individuals from big fMRI data
AU - Farahibozorg, Seyedeh Rezvan
AU - Bijsterbosch, Janine D.
AU - Gong, Weikang
AU - Jbabdi, Saad
AU - Smith, Stephen M.
AU - Harrison, Samuel J.
AU - Woolrich, Mark W.
N1 - Funding Information:
We would like to thank Dr. Diego Vidaurre for helpful input during early stages of this work. We are thankful to the three anonymous reviewers, who provided valuable feedback, leading to several improvements in this study. We are grateful to UK Biobank for making this invaluable resource available, and to the UK Biobank participants for dedicating their time to make this data possible. SMS is supported by the Wellcome Trust Strategic Award (098369/Z/12/Z) and Collaborative Award (215573/Z/19/Z), and MRC Mental Health Pathfinder grant (MC_PC_17215). MWW's research is supported by the NIHR Oxford Health Biomedical Research Centre, the Wellcome Trust (098369/Z/12/Z, 106183/Z/14/Z, 215573/Z/19/Z), and the New Therapeutics in Alzheimer's Diseases (NTAD) study supported by UK MRC and the Dementia Platform UK. SJH was supported by the grant #2017-403 of the Strategic Focal Area “Personalized Health and Related Technologies (PHRT)” of the ETH Domain. JDB is funded by the NIH (1 R34 NS118618-01) and the McDonnell Center for Systems Neuroscience. SJ is supported by the Wellcome Trust (221933/Z/20/Z, 215573/Z/19/Z). This research is further supported by the NIH Human Connectome Project (1U01MH109589–01 and 1U01AG052564–01). The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z). For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. The computational aspects of this research were partly carried out at Oxford Biomedical Research Computing (BMRC), that is funded by the NIHR Oxford BRC with additional support from the Wellcome Trust Core Award Grant Number 203141/Z/16/Z. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.
Funding Information:
We would like to thank Dr. Diego Vidaurre for helpful input during early stages of this work. We are thankful to the three anonymous reviewers, who provided valuable feedback, leading to several improvements in this study. We are grateful to UK Biobank for making this invaluable resource available, and to the UK Biobank participants for dedicating their time to make this data possible. SMS is supported by the Wellcome Trust Strategic Award (098369/Z/12/Z) and Collaborative Award (215573/Z/19/Z), and MRC Mental Health Pathfinder grant (MC_PC_17215). MWW's research is supported by the NIHR Oxford Health Biomedical Research Centre, the Wellcome Trust (098369/Z/12/Z, 106183/Z/14/Z, 215573/Z/19/Z), and the New Therapeutics in Alzheimer's Diseases (NTAD) study supported by UK MRC and the Dementia Platform UK. SJH was supported by the grant #2017-403 of the Strategic Focal Area ?Personalized Health and Related Technologies (PHRT)? of the ETH Domain. JDB is funded by the NIH (1 R34 NS118618-01) and the McDonnell Center for Systems Neuroscience. SJ is supported by the Wellcome Trust (221933/Z/20/Z, 215573/Z/19/Z). This research is further supported by the NIH Human Connectome Project (1U01MH109589?01 and 1U01AG052564?01). The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z). For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. The computational aspects of this research were partly carried out at Oxford Biomedical Research Computing (BMRC), that is funded by the NIHR Oxford BRC with additional support from the Wellcome Trust Core Award Grant Number 203141/Z/16/Z. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. Data used in this study is generously provided by UK Biobank and is available upon registration and applying for data access from UK Biobank website: http://www.ukbiobank.ac.uk/register-apply. Codes used for simulations is available from the following public repository: https://git.fmrib.ox.ac.uk/samh/PFM_Simulations. Code for sPROFUMO implementation is currently available from the following repository, it will be integrated within PROFUMO and will be made available in an upcoming FSL release: https://git.fmrib.ox.ac.uk/rezvanh/sprofumo_develop.
Publisher Copyright:
© 2021
PY - 2021/11
Y1 - 2021/11
N2 - 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.
AB - 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.
KW - Big data fMRI
KW - Cognition prediction
KW - Hierarchical network modelling
KW - Resting state networks
KW - Single subject connectivity
KW - Stochastic inference
KW - sPROFUMO
UR - http://www.scopus.com/inward/record.url?scp=85114249923&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2021.118513
DO - 10.1016/j.neuroimage.2021.118513
M3 - Article
C2 - 34450262
AN - SCOPUS:85114249923
VL - 243
JO - NeuroImage
JF - NeuroImage
SN - 1053-8119
M1 - 118513
ER -