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TBSS++: A novel computational method for Tract-Based Spatial Statistics

Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. One of the most common computations in dMRI involves cross-subject tract-specific analysis, whereby dMRI-derived biomarkers are compared between cohorts of subjects. The accuracy and reliability of...

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Autores principales: Karimi, Davood, Kebiri, Hamza, Gholipour, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10369867/
https://www.ncbi.nlm.nih.gov/pubmed/37503293
http://dx.doi.org/10.1101/2023.07.10.548454
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author Karimi, Davood
Kebiri, Hamza
Gholipour, Ali
author_facet Karimi, Davood
Kebiri, Hamza
Gholipour, Ali
author_sort Karimi, Davood
collection PubMed
description Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. One of the most common computations in dMRI involves cross-subject tract-specific analysis, whereby dMRI-derived biomarkers are compared between cohorts of subjects. The accuracy and reliability of these studies hinges on the ability to compare precisely the same white matter tracts across subjects. This is an intricate and error-prone computation. Existing computational methods such as Tract-Based Spatial Statistics (TBSS) suffer from a host of shortcomings and limitations that can seriously undermine the validity of the results. We present a new computational framework that overcomes the limitations of existing methods via (i) accurate segmentation of the tracts, and (ii) precise registration of data from different subjects/scans. The registration is based on fiber orientation distributions. To further improve the alignment of cross-subject data, we create detailed atlases of white matter tracts. These atlases serve as an unbiased reference space where the data from all subjects is registered for comparison. Extensive evaluations show that, compared with TBSS, our proposed framework offers significantly higher reproducibility and robustness to data perturbations. Our method promises a drastic improvement in accuracy and reproducibility of cross-subject dMRI studies that are routinely used in neuroscience and medical research.
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spelling pubmed-103698672023-07-27 TBSS++: A novel computational method for Tract-Based Spatial Statistics Karimi, Davood Kebiri, Hamza Gholipour, Ali bioRxiv Article Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. One of the most common computations in dMRI involves cross-subject tract-specific analysis, whereby dMRI-derived biomarkers are compared between cohorts of subjects. The accuracy and reliability of these studies hinges on the ability to compare precisely the same white matter tracts across subjects. This is an intricate and error-prone computation. Existing computational methods such as Tract-Based Spatial Statistics (TBSS) suffer from a host of shortcomings and limitations that can seriously undermine the validity of the results. We present a new computational framework that overcomes the limitations of existing methods via (i) accurate segmentation of the tracts, and (ii) precise registration of data from different subjects/scans. The registration is based on fiber orientation distributions. To further improve the alignment of cross-subject data, we create detailed atlases of white matter tracts. These atlases serve as an unbiased reference space where the data from all subjects is registered for comparison. Extensive evaluations show that, compared with TBSS, our proposed framework offers significantly higher reproducibility and robustness to data perturbations. Our method promises a drastic improvement in accuracy and reproducibility of cross-subject dMRI studies that are routinely used in neuroscience and medical research. Cold Spring Harbor Laboratory 2023-07-11 /pmc/articles/PMC10369867/ /pubmed/37503293 http://dx.doi.org/10.1101/2023.07.10.548454 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Karimi, Davood
Kebiri, Hamza
Gholipour, Ali
TBSS++: A novel computational method for Tract-Based Spatial Statistics
title TBSS++: A novel computational method for Tract-Based Spatial Statistics
title_full TBSS++: A novel computational method for Tract-Based Spatial Statistics
title_fullStr TBSS++: A novel computational method for Tract-Based Spatial Statistics
title_full_unstemmed TBSS++: A novel computational method for Tract-Based Spatial Statistics
title_short TBSS++: A novel computational method for Tract-Based Spatial Statistics
title_sort tbss++: a novel computational method for tract-based spatial statistics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10369867/
https://www.ncbi.nlm.nih.gov/pubmed/37503293
http://dx.doi.org/10.1101/2023.07.10.548454
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