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Tract-specific statistics based on diffusion-weighted probabilistic tractography
Diffusion-weighted neuroimaging approaches provide rich evidence for estimating the structural integrity of white matter in vivo, but typically do not assess white matter integrity for connections between two specific regions of the brain. Here, we present a method for deriving tract-specific diffus...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8854429/ https://www.ncbi.nlm.nih.gov/pubmed/35177755 http://dx.doi.org/10.1038/s42003-022-03073-w |
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author | Reid, Andrew T. Camilleri, Julia A. Hoffstaedter, Felix Eickhoff, Simon B. |
author_facet | Reid, Andrew T. Camilleri, Julia A. Hoffstaedter, Felix Eickhoff, Simon B. |
author_sort | Reid, Andrew T. |
collection | PubMed |
description | Diffusion-weighted neuroimaging approaches provide rich evidence for estimating the structural integrity of white matter in vivo, but typically do not assess white matter integrity for connections between two specific regions of the brain. Here, we present a method for deriving tract-specific diffusion statistics, based upon predefined regions of interest. Our approach derives a population distribution using probabilistic tractography, based on the Nathan Kline Institute (NKI) Enhanced Rockland sample. We determine the most likely geometry of a path between two regions and express this as a spatial distribution. We then estimate the average orientation of streamlines traversing this path, at discrete distances along its trajectory, and the fraction of diffusion directed along this orientation for each participant. The resulting participant-wise metrics (tract-specific anisotropy; TSA) can then be used for statistical analysis on any comparable population. Based on this method, we report both negative and positive associations between age and TSA for two networks derived from published meta-analytic studies (the “default mode” and “what-where” networks), along with more moderate sex differences and age-by-sex interactions. The proposed method can be applied to any arbitrary set of brain regions, to estimate both the spatial trajectory and DWI-based anisotropy specific to those regions. |
format | Online Article Text |
id | pubmed-8854429 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-88544292022-03-03 Tract-specific statistics based on diffusion-weighted probabilistic tractography Reid, Andrew T. Camilleri, Julia A. Hoffstaedter, Felix Eickhoff, Simon B. Commun Biol Article Diffusion-weighted neuroimaging approaches provide rich evidence for estimating the structural integrity of white matter in vivo, but typically do not assess white matter integrity for connections between two specific regions of the brain. Here, we present a method for deriving tract-specific diffusion statistics, based upon predefined regions of interest. Our approach derives a population distribution using probabilistic tractography, based on the Nathan Kline Institute (NKI) Enhanced Rockland sample. We determine the most likely geometry of a path between two regions and express this as a spatial distribution. We then estimate the average orientation of streamlines traversing this path, at discrete distances along its trajectory, and the fraction of diffusion directed along this orientation for each participant. The resulting participant-wise metrics (tract-specific anisotropy; TSA) can then be used for statistical analysis on any comparable population. Based on this method, we report both negative and positive associations between age and TSA for two networks derived from published meta-analytic studies (the “default mode” and “what-where” networks), along with more moderate sex differences and age-by-sex interactions. The proposed method can be applied to any arbitrary set of brain regions, to estimate both the spatial trajectory and DWI-based anisotropy specific to those regions. Nature Publishing Group UK 2022-02-17 /pmc/articles/PMC8854429/ /pubmed/35177755 http://dx.doi.org/10.1038/s42003-022-03073-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Reid, Andrew T. Camilleri, Julia A. Hoffstaedter, Felix Eickhoff, Simon B. Tract-specific statistics based on diffusion-weighted probabilistic tractography |
title | Tract-specific statistics based on diffusion-weighted probabilistic tractography |
title_full | Tract-specific statistics based on diffusion-weighted probabilistic tractography |
title_fullStr | Tract-specific statistics based on diffusion-weighted probabilistic tractography |
title_full_unstemmed | Tract-specific statistics based on diffusion-weighted probabilistic tractography |
title_short | Tract-specific statistics based on diffusion-weighted probabilistic tractography |
title_sort | tract-specific statistics based on diffusion-weighted probabilistic tractography |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8854429/ https://www.ncbi.nlm.nih.gov/pubmed/35177755 http://dx.doi.org/10.1038/s42003-022-03073-w |
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