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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: Richie-Halford, Adam, Yeatman, Jason D., Simon, Noah, Rokem, Ariel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8270416/
https://www.ncbi.nlm.nih.gov/pubmed/34181648
http://dx.doi.org/10.1371/journal.pcbi.1009136
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author Richie-Halford, Adam
Yeatman, Jason D.
Simon, Noah
Rokem, Ariel
author_facet Richie-Halford, Adam
Yeatman, Jason D.
Simon, Noah
Rokem, Ariel
author_sort Richie-Halford, Adam
collection PubMed
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.
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spelling pubmed-82704162021-07-20 Multidimensional analysis and detection of informative features in human brain white matter Richie-Halford, Adam Yeatman, Jason D. Simon, Noah Rokem, Ariel PLoS Comput Biol Research Article 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. Public Library of Science 2021-06-28 /pmc/articles/PMC8270416/ /pubmed/34181648 http://dx.doi.org/10.1371/journal.pcbi.1009136 Text en © 2021 Richie-Halford et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Richie-Halford, Adam
Yeatman, Jason D.
Simon, Noah
Rokem, Ariel
Multidimensional analysis and detection of informative features in human brain white matter
title 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_short 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8270416/
https://www.ncbi.nlm.nih.gov/pubmed/34181648
http://dx.doi.org/10.1371/journal.pcbi.1009136
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