The relationship between spatial configuration and functional connectivity of brain regions revisited
Previously we showed that network-based modelling of brain connectivity interacts strongly with the shape and exact location of brain regions, such that cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity (Bijs...
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doaj-de272dd0b819426492749715f44878572021-05-05T17:36:04ZengeLife Sciences Publications LtdeLife2050-084X2019-05-01810.7554/eLife.44890The relationship between spatial configuration and functional connectivity of brain regions revisitedJanine Diane Bijsterbosch0https://orcid.org/0000-0002-1385-9178Christian F Beckmann1Mark W Woolrich2Stephen M Smith3Samuel J Harrison4https://orcid.org/0000-0002-5886-2389Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United KingdomDonders Institute, Department of Cognitive Neurosciences, Radboud University Medical Centre, Nijmegen, NetherlandsCentre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, United KingdomCentre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United KingdomCentre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United KingdomPreviously we showed that network-based modelling of brain connectivity interacts strongly with the shape and exact location of brain regions, such that cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity (Bijsterbosch et al., 2018). Here we show that these spatial effects on connectivity estimates actually occur as a result of spatial overlap between brain networks. This is shown to systematically bias connectivity estimates obtained from group spatial ICA followed by dual regression. We introduce an extended method that addresses the bias and achieves more accurate connectivity estimates.https://elifesciences.org/articles/44890resting stateparcellationfunctional connectivityfunctional connectomesdual regression |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Janine Diane Bijsterbosch Christian F Beckmann Mark W Woolrich Stephen M Smith Samuel J Harrison |
spellingShingle |
Janine Diane Bijsterbosch Christian F Beckmann Mark W Woolrich Stephen M Smith Samuel J Harrison The relationship between spatial configuration and functional connectivity of brain regions revisited eLife resting state parcellation functional connectivity functional connectomes dual regression |
author_facet |
Janine Diane Bijsterbosch Christian F Beckmann Mark W Woolrich Stephen M Smith Samuel J Harrison |
author_sort |
Janine Diane Bijsterbosch |
title |
The relationship between spatial configuration and functional connectivity of brain regions revisited |
title_short |
The relationship between spatial configuration and functional connectivity of brain regions revisited |
title_full |
The relationship between spatial configuration and functional connectivity of brain regions revisited |
title_fullStr |
The relationship between spatial configuration and functional connectivity of brain regions revisited |
title_full_unstemmed |
The relationship between spatial configuration and functional connectivity of brain regions revisited |
title_sort |
relationship between spatial configuration and functional connectivity of brain regions revisited |
publisher |
eLife Sciences Publications Ltd |
series |
eLife |
issn |
2050-084X |
publishDate |
2019-05-01 |
description |
Previously we showed that network-based modelling of brain connectivity interacts strongly with the shape and exact location of brain regions, such that cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity (Bijsterbosch et al., 2018). Here we show that these spatial effects on connectivity estimates actually occur as a result of spatial overlap between brain networks. This is shown to systematically bias connectivity estimates obtained from group spatial ICA followed by dual regression. We introduce an extended method that addresses the bias and achieves more accurate connectivity estimates. |
topic |
resting state parcellation functional connectivity functional connectomes dual regression |
url |
https://elifesciences.org/articles/44890 |
work_keys_str_mv |
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1721459143562231808 |