Probabilistic flow in brain-wide activity

Patterns of low frequency brain-wide activity have drawn attention across multiple disciplines in neuroscience. Brain-wide activity patterns are often described through correlations, which capture concurrent increases and decreases in neural activity. More recently, several groups have described rep...

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Main Authors: Anish Mitra, Abraham Z. Snyder, Marcus E. Raichle
Format: Article
Language:English
Published: Elsevier 2020-12-01
Series:NeuroImage
Online Access:http://www.sciencedirect.com/science/article/pii/S1053811920308077
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author Anish Mitra
Abraham Z. Snyder
Marcus E. Raichle
author_facet Anish Mitra
Abraham Z. Snyder
Marcus E. Raichle
author_sort Anish Mitra
collection DOAJ
description Patterns of low frequency brain-wide activity have drawn attention across multiple disciplines in neuroscience. Brain-wide activity patterns are often described through correlations, which capture concurrent increases and decreases in neural activity. More recently, several groups have described reproducible temporal sequences across the brain, illustrating precise long-distance control over the timing of low frequency activity. Features of correlation and temporal organization both point to a systems-level structure of brain activity consisting of large-scale networks and their mutual interactions. Yet a unified view for understanding large networks and their interactions remains elusive. Here, we propose a framework for computing probabilistic flow in brain-wide activity. We demonstrate how flow probabilities are modulated across rest and task states and show that the probabilistic perspective captures both intra- and inter-network dynamics. Finally, we suggest that a probabilistic framework may prove fruitful in characterizing low frequency brain-wide activity in health and disease.
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spelling doaj.art-4bdd5195c5014531a17822a68d103e442022-12-21T20:20:48ZengElsevierNeuroImage1095-95722020-12-01223117321Probabilistic flow in brain-wide activityAnish Mitra0Abraham Z. Snyder1Marcus E. Raichle2Department of Psychiatry, Stanford University, 401 Quarry Rd, Stanford, CA 94304, United States; Corresponding author.Department of Neurology, Washington University, St. Louis, United StatesDepartment of Neurology, Washington University, St. Louis, United StatesPatterns of low frequency brain-wide activity have drawn attention across multiple disciplines in neuroscience. Brain-wide activity patterns are often described through correlations, which capture concurrent increases and decreases in neural activity. More recently, several groups have described reproducible temporal sequences across the brain, illustrating precise long-distance control over the timing of low frequency activity. Features of correlation and temporal organization both point to a systems-level structure of brain activity consisting of large-scale networks and their mutual interactions. Yet a unified view for understanding large networks and their interactions remains elusive. Here, we propose a framework for computing probabilistic flow in brain-wide activity. We demonstrate how flow probabilities are modulated across rest and task states and show that the probabilistic perspective captures both intra- and inter-network dynamics. Finally, we suggest that a probabilistic framework may prove fruitful in characterizing low frequency brain-wide activity in health and disease.http://www.sciencedirect.com/science/article/pii/S1053811920308077
spellingShingle Anish Mitra
Abraham Z. Snyder
Marcus E. Raichle
Probabilistic flow in brain-wide activity
NeuroImage
title Probabilistic flow in brain-wide activity
title_full Probabilistic flow in brain-wide activity
title_fullStr Probabilistic flow in brain-wide activity
title_full_unstemmed Probabilistic flow in brain-wide activity
title_short Probabilistic flow in brain-wide activity
title_sort probabilistic flow in brain wide activity
url http://www.sciencedirect.com/science/article/pii/S1053811920308077
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