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Detection of functional networks within white matter using independent component analysis

Spontaneous fluctuations in MRI signals from gray matter (GM) in the brain are interpreted as originating from variations in neural activity, and their inter-regional correlations may be analyzed to reveal functional connectivity. However, most studies of intrinsic neuronal activity have ignored the...

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Autores principales: Huang, Yali, Yang, Yang, Hao, Lei, Hu, Xuefang, Wang, Peiguang, Ding, Zhaohua, Gao, Jia-Hong, Gore, John C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7736513/
https://www.ncbi.nlm.nih.gov/pubmed/32835817
http://dx.doi.org/10.1016/j.neuroimage.2020.117278
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author Huang, Yali
Yang, Yang
Hao, Lei
Hu, Xuefang
Wang, Peiguang
Ding, Zhaohua
Gao, Jia-Hong
Gore, John C.
author_facet Huang, Yali
Yang, Yang
Hao, Lei
Hu, Xuefang
Wang, Peiguang
Ding, Zhaohua
Gao, Jia-Hong
Gore, John C.
author_sort Huang, Yali
collection PubMed
description Spontaneous fluctuations in MRI signals from gray matter (GM) in the brain are interpreted as originating from variations in neural activity, and their inter-regional correlations may be analyzed to reveal functional connectivity. However, most studies of intrinsic neuronal activity have ignored the spontaneous fluctuations that also arise in white matter (WM). In this work, we explore spontaneous fluctuations in resting state MRI signals in WM based on spatial independent component analyses (ICA), a data-driven approach that separates signals into independent sources without making specific modeling assumptions. ICA has become widely accepted as a valuable approach for identifying functional connectivity within cortex but has been rarely applied to derive equivalent structures within WM. Here, BOLD signal changes in WM of a group of subjects performing motor tasks were first detected using ICA, and a spatial component whose time course was consistent with the task was found, demonstrating the analysis is sensitive to evoked BOLD signals in WM. Secondly, multiple spatial components were derived by applying ICA to identify those voxels in WM whose MRI signals showed similar temporal behaviors in a resting state. These functionally-related structures are grossly symmetric and coincide with corresponding tracts identified from diffusion MRI. Finally, functional connectivity was quantified by calculating correlations between pairs of structures to explore the synchronicity of resting state BOLD signals across WM regions, and the experimental results revealed that there exist two distinct groupings of functional correlations in WM tracts at rest. Our study provides further insights into the nature of activation patterns, functional responses and connectivity in WM, and support previous suggestions that BOLD signals in WM show similarities with cortical activations and are characterized by distinct underlying structures in tasks and at rest.
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spelling pubmed-77365132020-12-15 Detection of functional networks within white matter using independent component analysis Huang, Yali Yang, Yang Hao, Lei Hu, Xuefang Wang, Peiguang Ding, Zhaohua Gao, Jia-Hong Gore, John C. Neuroimage Article Spontaneous fluctuations in MRI signals from gray matter (GM) in the brain are interpreted as originating from variations in neural activity, and their inter-regional correlations may be analyzed to reveal functional connectivity. However, most studies of intrinsic neuronal activity have ignored the spontaneous fluctuations that also arise in white matter (WM). In this work, we explore spontaneous fluctuations in resting state MRI signals in WM based on spatial independent component analyses (ICA), a data-driven approach that separates signals into independent sources without making specific modeling assumptions. ICA has become widely accepted as a valuable approach for identifying functional connectivity within cortex but has been rarely applied to derive equivalent structures within WM. Here, BOLD signal changes in WM of a group of subjects performing motor tasks were first detected using ICA, and a spatial component whose time course was consistent with the task was found, demonstrating the analysis is sensitive to evoked BOLD signals in WM. Secondly, multiple spatial components were derived by applying ICA to identify those voxels in WM whose MRI signals showed similar temporal behaviors in a resting state. These functionally-related structures are grossly symmetric and coincide with corresponding tracts identified from diffusion MRI. Finally, functional connectivity was quantified by calculating correlations between pairs of structures to explore the synchronicity of resting state BOLD signals across WM regions, and the experimental results revealed that there exist two distinct groupings of functional correlations in WM tracts at rest. Our study provides further insights into the nature of activation patterns, functional responses and connectivity in WM, and support previous suggestions that BOLD signals in WM show similarities with cortical activations and are characterized by distinct underlying structures in tasks and at rest. 2020-08-22 2020-11-15 /pmc/articles/PMC7736513/ /pubmed/32835817 http://dx.doi.org/10.1016/j.neuroimage.2020.117278 Text en This is an open access article under the CC BY-NC-ND license. (http://creativecommons.org/licenses/by-nc-nd/4.0/)
spellingShingle Article
Huang, Yali
Yang, Yang
Hao, Lei
Hu, Xuefang
Wang, Peiguang
Ding, Zhaohua
Gao, Jia-Hong
Gore, John C.
Detection of functional networks within white matter using independent component analysis
title Detection of functional networks within white matter using independent component analysis
title_full Detection of functional networks within white matter using independent component analysis
title_fullStr Detection of functional networks within white matter using independent component analysis
title_full_unstemmed Detection of functional networks within white matter using independent component analysis
title_short Detection of functional networks within white matter using independent component analysis
title_sort detection of functional networks within white matter using independent component analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7736513/
https://www.ncbi.nlm.nih.gov/pubmed/32835817
http://dx.doi.org/10.1016/j.neuroimage.2020.117278
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