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Vascular physiology drives functional brain networks

We present the first evidence for vascular regulation driving fMRI signals in specific functional brain networks. Using concurrent neuronal and vascular stimuli, we collected 30 BOLD fMRI datasets in 10 healthy individuals: a working memory task, flashing checkerboard stimulus, and CO(2) inhalation...

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Detalles Bibliográficos
Autores principales: Bright, Molly G., Whittaker, Joseph R., Driver, Ian D., Murphy, Kevin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Academic Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7339138/
https://www.ncbi.nlm.nih.gov/pubmed/32387624
http://dx.doi.org/10.1016/j.neuroimage.2020.116907
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author Bright, Molly G.
Whittaker, Joseph R.
Driver, Ian D.
Murphy, Kevin
author_facet Bright, Molly G.
Whittaker, Joseph R.
Driver, Ian D.
Murphy, Kevin
author_sort Bright, Molly G.
collection PubMed
description We present the first evidence for vascular regulation driving fMRI signals in specific functional brain networks. Using concurrent neuronal and vascular stimuli, we collected 30 BOLD fMRI datasets in 10 healthy individuals: a working memory task, flashing checkerboard stimulus, and CO(2) inhalation challenge were delivered in concurrent but orthogonal paradigms. The resulting imaging data were averaged together and decomposed using independent component analysis, and three “neuronal networks” were identified as demonstrating maximum temporal correlation with the neuronal stimulus paradigms: Default Mode Network, Task Positive Network, and Visual Network. For each of these, we observed a second network component with high spatial overlap. Using dual regression in the original 30 datasets, we extracted the time-series associated with these network pairs and calculated the percent of variance explained by the neuronal or vascular stimuli using a normalized R(2) parameter. In each pairing, one network was dominated by the appropriate neuronal stimulus, and the other was dominated by the vascular stimulus as represented by the end-tidal CO(2) time-series recorded in each scan. We acquired a second dataset in 8 of the original participants, where no CO(2) challenge was delivered and CO(2) levels fluctuated naturally with breathing variations. Although splitting of functional networks was not robust in these data, performing dual regression with the network maps from the original analysis in this new dataset successfully replicated our observations. Thus, in addition to responding to localized metabolic changes, the brain’s vasculature may be regulated in a coordinated manner that mimics (and potentially supports) specific functional brain networks. Multi-modal imaging and advances in fMRI acquisition and analysis could facilitate further study of the dual nature of functional brain networks. It will be critical to understand network-specific vascular function, and the behavior of a coupled vascular-neural network, in future studies of brain pathology.
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spelling pubmed-73391382020-08-15 Vascular physiology drives functional brain networks Bright, Molly G. Whittaker, Joseph R. Driver, Ian D. Murphy, Kevin Neuroimage Article We present the first evidence for vascular regulation driving fMRI signals in specific functional brain networks. Using concurrent neuronal and vascular stimuli, we collected 30 BOLD fMRI datasets in 10 healthy individuals: a working memory task, flashing checkerboard stimulus, and CO(2) inhalation challenge were delivered in concurrent but orthogonal paradigms. The resulting imaging data were averaged together and decomposed using independent component analysis, and three “neuronal networks” were identified as demonstrating maximum temporal correlation with the neuronal stimulus paradigms: Default Mode Network, Task Positive Network, and Visual Network. For each of these, we observed a second network component with high spatial overlap. Using dual regression in the original 30 datasets, we extracted the time-series associated with these network pairs and calculated the percent of variance explained by the neuronal or vascular stimuli using a normalized R(2) parameter. In each pairing, one network was dominated by the appropriate neuronal stimulus, and the other was dominated by the vascular stimulus as represented by the end-tidal CO(2) time-series recorded in each scan. We acquired a second dataset in 8 of the original participants, where no CO(2) challenge was delivered and CO(2) levels fluctuated naturally with breathing variations. Although splitting of functional networks was not robust in these data, performing dual regression with the network maps from the original analysis in this new dataset successfully replicated our observations. Thus, in addition to responding to localized metabolic changes, the brain’s vasculature may be regulated in a coordinated manner that mimics (and potentially supports) specific functional brain networks. Multi-modal imaging and advances in fMRI acquisition and analysis could facilitate further study of the dual nature of functional brain networks. It will be critical to understand network-specific vascular function, and the behavior of a coupled vascular-neural network, in future studies of brain pathology. Academic Press 2020-08-15 /pmc/articles/PMC7339138/ /pubmed/32387624 http://dx.doi.org/10.1016/j.neuroimage.2020.116907 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bright, Molly G.
Whittaker, Joseph R.
Driver, Ian D.
Murphy, Kevin
Vascular physiology drives functional brain networks
title Vascular physiology drives functional brain networks
title_full Vascular physiology drives functional brain networks
title_fullStr Vascular physiology drives functional brain networks
title_full_unstemmed Vascular physiology drives functional brain networks
title_short Vascular physiology drives functional brain networks
title_sort vascular physiology drives functional brain networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7339138/
https://www.ncbi.nlm.nih.gov/pubmed/32387624
http://dx.doi.org/10.1016/j.neuroimage.2020.116907
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