TY - JOUR
T1 - Modelling subject variability in the spatial and temporal characteristics of functional modes
AU - Harrison, Samuel J.
AU - Bijsterbosch, Janine D.
AU - Segerdahl, Andrew R.
AU - Fitzgibbon, Sean P.
AU - Farahibozorg, Seyedeh Rezvan
AU - Duff, Eugene P.
AU - Smith, Stephen M.
AU - Woolrich, Mark W.
N1 - Funding Information:
We would like to offer our profound thanks to Tamar Makin for making the active-state data set available, and to Roser Sala-Llonch and Sasidar Madugular for the preprocessing. We would also like to thank the anonymous reviewers, whose feedback and suggestions led to many improvements to the manuscript. Data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research ; and by the McDonnell Center for Systems Neuroscience at Washington University. S.J.H., J.D.B. & S.M.S. were funded by the Wellcome Trust (grants 098369/Z/12/Z and 091509/Z/10/Z). S.J.H. was also supported by the grant #2017-403 of the Strategic Focal Area “Personalized Health and Related Technologies (PHRT)” of the ETH Domain. S.M.S. also received funding from an MRC Mental Health Pathfinder grant (MC_PC_17215). A.R.S. received funding for this work from the following sources: National Institute for Health Research Oxford Biomedical Research Centre, Medical Research Council of Great Britain and Northern Ireland, the Wellcome Trust (London, UK) and the Innovative Medicines Initiative Joint Undertaking (Brussels, Belgium), under grant agreement no 115007 resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies’ in kind contribution. A.R.S. is also a member of the Wellcome Pain Consortium (Ref. 102645), and the project was supported by a strategic award from the Wellcome (Ref. 102645). S.P.F. is supported by the European Research Council under the European Union’s Seventh Framework Programme (Developing Human Connectome Project: FP/2007–2013/ERC Grant Agreement no. 319456). E.P.D. has been supported by the Developing Human Connectome Project (European Research Council Synergy grant FP/2007-2013), and the SSNAP charity, Oxford (Support for the Sick Newborn and their Parents). M.W.W. is funded by the Wellcome Trust (092753), and is supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre based at Oxford University Hospitals Trust, Oxford University (the views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health). Finally, the Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z).
Funding Information:
We would like to offer our profound thanks to Tamar Makin for making the active-state data set available, and to Roser Sala-Llonch and Sasidar Madugular for the preprocessing. We would also like to thank the anonymous reviewers, whose feedback and suggestions led to many improvements to the manuscript. Data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. S.J.H. J.D.B. & S.M.S. were funded by the Wellcome Trust (grants 098369/Z/12/Z and 091509/Z/10/Z). S.J.H. was also supported by the grant #2017-403 of the Strategic Focal Area “Personalized Health and Related Technologies (PHRT)” of the ETH Domain. S.M.S. also received funding from an MRC Mental Health Pathfinder grant (MC_PC_17215). A.R.S. received funding for this work from the following sources: National Institute for Health Research Oxford Biomedical Research Centre, Medical Research Council of Great Britain and Northern Ireland, the Wellcome Trust (London, UK) and the Innovative Medicines Initiative Joint Undertaking (Brussels, Belgium), under grant agreement no 115007 resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007–2013) and EFPIA companies’ in kind contribution. A.R.S. is also a member of the Wellcome Pain Consortium (Ref. 102645), and the project was supported by a strategic award from the Wellcome (Ref. 102645). S.P.F. is supported by the European Research Council under the European Union's Seventh Framework Programme (Developing Human Connectome Project: FP/2007–2013/ERC Grant Agreement no. 319456). E.P.D. has been supported by the Developing Human Connectome Project (European Research Council Synergy grant FP/2007-2013), and the SSNAP charity, Oxford (Support for the Sick Newborn and their Parents). M.W.W. is funded by the Wellcome Trust (092753), and is supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre based at Oxford University Hospitals Trust, Oxford University (the views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health). Finally, the Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z).
Publisher Copyright:
© 2020
PY - 2020/11/15
Y1 - 2020/11/15
N2 - Recent work has highlighted the scale and ubiquity of subject variability in observations from functional MRI data (fMRI). Furthermore, it is highly likely that errors in the estimation of either the spatial presentation of, or the coupling between, functional regions can confound cross-subject analyses, making accurate and unbiased representations of functional data essential for interpreting any downstream analyses. Here, we extend the framework of probabilistic functional modes (PFMs) (Harrison et al., 2015) to capture cross-subject variability not only in the mode spatial maps, but also in the functional coupling between modes and in mode amplitudes. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets, and the combined inference and analysis package, PROFUMO, is available from git.fmrib.ox.ac.uk/samh/profumo. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets. Using simulated data, resting-state data from 1000 subjects collected as part of the Human Connectome Project (Van Essen et al., 2013), and an analysis of 14 subjects in a variety of continuous task-states (Kieliba et al., 2019), we demonstrate how PFMs are able to capture, within a single model, a rich description of how the spatio-temporal structure of resting-state fMRI activity varies across subjects. We also compare the new PFM model to the well established independent component analysis with dual regression (ICA-DR) pipeline. This reveals that, under PFM assumptions, much more of the (behaviorally relevant) cross-subject variability in fMRI activity should be attributed to the variability in spatial maps, and that, after accounting for this, functional coupling between modes primarily reflects current cognitive state. This has fundamental implications for the interpretation of cross-sectional studies of functional connectivity that do not capture cross-subject variability to the same extent as PFMs.
AB - Recent work has highlighted the scale and ubiquity of subject variability in observations from functional MRI data (fMRI). Furthermore, it is highly likely that errors in the estimation of either the spatial presentation of, or the coupling between, functional regions can confound cross-subject analyses, making accurate and unbiased representations of functional data essential for interpreting any downstream analyses. Here, we extend the framework of probabilistic functional modes (PFMs) (Harrison et al., 2015) to capture cross-subject variability not only in the mode spatial maps, but also in the functional coupling between modes and in mode amplitudes. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets, and the combined inference and analysis package, PROFUMO, is available from git.fmrib.ox.ac.uk/samh/profumo. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets. Using simulated data, resting-state data from 1000 subjects collected as part of the Human Connectome Project (Van Essen et al., 2013), and an analysis of 14 subjects in a variety of continuous task-states (Kieliba et al., 2019), we demonstrate how PFMs are able to capture, within a single model, a rich description of how the spatio-temporal structure of resting-state fMRI activity varies across subjects. We also compare the new PFM model to the well established independent component analysis with dual regression (ICA-DR) pipeline. This reveals that, under PFM assumptions, much more of the (behaviorally relevant) cross-subject variability in fMRI activity should be attributed to the variability in spatial maps, and that, after accounting for this, functional coupling between modes primarily reflects current cognitive state. This has fundamental implications for the interpretation of cross-sectional studies of functional connectivity that do not capture cross-subject variability to the same extent as PFMs.
UR - http://www.scopus.com/inward/record.url?scp=85089899116&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2020.117226
DO - 10.1016/j.neuroimage.2020.117226
M3 - Article
C2 - 32771617
AN - SCOPUS:85089899116
SN - 1053-8119
VL - 222
JO - NeuroImage
JF - NeuroImage
M1 - 117226
ER -