Multidimensional analysis and detection of informative features in human brain white matter.

The white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to quantify tissue properties along...

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Autores principales:	Adam Richie-Halford, Jason D Yeatman, Noah Simon, Ariel Rokem
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/47ba63b6a0c844f9aeeb789b64666732
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spelling	oai:doaj.org-article:47ba63b6a0c844f9aeeb789b646667322021-11-25T05:40:34ZMultidimensional analysis and detection of informative features in human brain white matter.1553-734X1553-735810.1371/journal.pcbi.1009136https://doaj.org/article/47ba63b6a0c844f9aeeb789b646667322021-06-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009136https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to quantify tissue properties along the trajectories of these connections. Statistical inference from tractometry usually either averages these quantities along the length of each fiber bundle or computes regression models separately for each point along every one of the bundles. These approaches are limited in their sensitivity, in the former case, or in their statistical power, in the latter. We developed a method based on the sparse group lasso (SGL) that takes into account tissue properties along all of the bundles and selects informative features by enforcing both global and bundle-level sparsity. We demonstrate the performance of the method in two settings: i) in a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls. Furthermore, SGL identifies the corticospinal tract as important for this classification, correctly finding the parts of the white matter known to be affected by the disease. ii) In a regression setting, SGL accurately predicts "brain age." In this case, the weights are distributed throughout the white matter indicating that many different regions of the white matter change over the lifespan. Thus, SGL leverages the multivariate relationships between diffusion properties in multiple bundles to make accurate phenotypic predictions while simultaneously discovering the most relevant features of the white matter.Adam Richie-HalfordJason D YeatmanNoah SimonAriel RokemPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 6, p e1009136 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Adam Richie-Halford Jason D Yeatman Noah Simon Ariel Rokem Multidimensional analysis and detection of informative features in human brain white matter.
description	The white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to quantify tissue properties along the trajectories of these connections. Statistical inference from tractometry usually either averages these quantities along the length of each fiber bundle or computes regression models separately for each point along every one of the bundles. These approaches are limited in their sensitivity, in the former case, or in their statistical power, in the latter. We developed a method based on the sparse group lasso (SGL) that takes into account tissue properties along all of the bundles and selects informative features by enforcing both global and bundle-level sparsity. We demonstrate the performance of the method in two settings: i) in a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls. Furthermore, SGL identifies the corticospinal tract as important for this classification, correctly finding the parts of the white matter known to be affected by the disease. ii) In a regression setting, SGL accurately predicts "brain age." In this case, the weights are distributed throughout the white matter indicating that many different regions of the white matter change over the lifespan. Thus, SGL leverages the multivariate relationships between diffusion properties in multiple bundles to make accurate phenotypic predictions while simultaneously discovering the most relevant features of the white matter.
format	article
author	Adam Richie-Halford Jason D Yeatman Noah Simon Ariel Rokem
author_facet	Adam Richie-Halford Jason D Yeatman Noah Simon Ariel Rokem
author_sort	Adam Richie-Halford
title	Multidimensional analysis and detection of informative features in human brain white matter.
title_short	Multidimensional analysis and detection of informative features in human brain white matter.
title_full	Multidimensional analysis and detection of informative features in human brain white matter.
title_fullStr	Multidimensional analysis and detection of informative features in human brain white matter.
title_full_unstemmed	Multidimensional analysis and detection of informative features in human brain white matter.
title_sort	multidimensional analysis and detection of informative features in human brain white matter.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/47ba63b6a0c844f9aeeb789b64666732
work_keys_str_mv	AT adamrichiehalford multidimensionalanalysisanddetectionofinformativefeaturesinhumanbrainwhitematter AT jasondyeatman multidimensionalanalysisanddetectionofinformativefeaturesinhumanbrainwhitematter AT noahsimon multidimensionalanalysisanddetectionofinformativefeaturesinhumanbrainwhitematter AT arielrokem multidimensionalanalysisanddetectionofinformativefeaturesinhumanbrainwhitematter
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Multidimensional analysis and detection of informative features in human brain white matter.

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