Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference

Abstract Diffusion-weighted magnetic resonance imaging (dMRI) allows non-invasive investigation of whole-brain connectivity, which can reveal the brain’s global network architecture and also abnormalities involved in neurological and mental disorders. However, the reliability of connection inference...

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Autores principales:	Carlos Enrique Gutierrez, Henrik Skibbe, Ken Nakae, Hiromichi Tsukada, Jean Lienard, Akiya Watakabe, Junichi Hata, Marco Reisert, Alexander Woodward, Yoko Yamaguchi, Tetsuo Yamamori, Hideyuki Okano, Shin Ishii, Kenji Doya
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7a8bd2e00494415ab53835d71b0da8dc
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spelling	oai:doaj.org-article:7a8bd2e00494415ab53835d71b0da8dc2021-12-02T11:57:56ZOptimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference10.1038/s41598-020-78284-42045-2322https://doaj.org/article/7a8bd2e00494415ab53835d71b0da8dc2020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78284-4https://doaj.org/toc/2045-2322Abstract Diffusion-weighted magnetic resonance imaging (dMRI) allows non-invasive investigation of whole-brain connectivity, which can reveal the brain’s global network architecture and also abnormalities involved in neurological and mental disorders. However, the reliability of connection inferences from dMRI-based fiber tracking is still debated, due to low sensitivity, dominance of false positives, and inaccurate and incomplete reconstruction of long-range connections. Furthermore, parameters of tracking algorithms are typically tuned in a heuristic way, which leaves room for manipulation of an intended result. Here we propose a general data-driven framework to optimize and validate parameters of dMRI-based fiber tracking algorithms using neural tracer data as a reference. Japan’s Brain/MINDS Project provides invaluable datasets containing both dMRI and neural tracer data from the same primates. A fundamental difference when comparing dMRI-based tractography and neural tracer data is that the former cannot specify the direction of connectivity; therefore, evaluating the fitting of dMRI-based tractography becomes challenging. The framework implements multi-objective optimization based on the non-dominated sorting genetic algorithm II. Its performance is examined in two experiments using data from ten subjects for optimization and six for testing generalization. The first uses a seed-based tracking algorithm, iFOD2, and objectives for sensitivity and specificity of region-level connectivity. The second uses a global tracking algorithm and a more refined set of objectives: distance-weighted coverage, true/false positive ratio, projection coincidence, and commissural passage. In both experiments, with optimized parameters compared to default parameters, fiber tracking performance was significantly improved in coverage and fiber length. Improvements were more prominent using global tracking with refined objectives, achieving an average fiber length from 10 to 17 mm, voxel-wise coverage of axonal tracts from 0.9 to 15%, and the correlation of target areas from 40 to 68%, while minimizing false positives and impossible cross-hemisphere connections. Optimized parameters showed good generalization capability for test brain samples in both experiments, demonstrating the flexible applicability of our framework to different tracking algorithms and objectives. These results indicate the importance of data-driven adjustment of fiber tracking algorithms and support the validity of dMRI-based tractography, if appropriate adjustments are employed.Carlos Enrique GutierrezHenrik SkibbeKen NakaeHiromichi TsukadaJean LienardAkiya WatakabeJunichi HataMarco ReisertAlexander WoodwardYoko YamaguchiTetsuo YamamoriHideyuki OkanoShin IshiiKenji DoyaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-18 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Carlos Enrique Gutierrez Henrik Skibbe Ken Nakae Hiromichi Tsukada Jean Lienard Akiya Watakabe Junichi Hata Marco Reisert Alexander Woodward Yoko Yamaguchi Tetsuo Yamamori Hideyuki Okano Shin Ishii Kenji Doya Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference
description	Abstract Diffusion-weighted magnetic resonance imaging (dMRI) allows non-invasive investigation of whole-brain connectivity, which can reveal the brain’s global network architecture and also abnormalities involved in neurological and mental disorders. However, the reliability of connection inferences from dMRI-based fiber tracking is still debated, due to low sensitivity, dominance of false positives, and inaccurate and incomplete reconstruction of long-range connections. Furthermore, parameters of tracking algorithms are typically tuned in a heuristic way, which leaves room for manipulation of an intended result. Here we propose a general data-driven framework to optimize and validate parameters of dMRI-based fiber tracking algorithms using neural tracer data as a reference. Japan’s Brain/MINDS Project provides invaluable datasets containing both dMRI and neural tracer data from the same primates. A fundamental difference when comparing dMRI-based tractography and neural tracer data is that the former cannot specify the direction of connectivity; therefore, evaluating the fitting of dMRI-based tractography becomes challenging. The framework implements multi-objective optimization based on the non-dominated sorting genetic algorithm II. Its performance is examined in two experiments using data from ten subjects for optimization and six for testing generalization. The first uses a seed-based tracking algorithm, iFOD2, and objectives for sensitivity and specificity of region-level connectivity. The second uses a global tracking algorithm and a more refined set of objectives: distance-weighted coverage, true/false positive ratio, projection coincidence, and commissural passage. In both experiments, with optimized parameters compared to default parameters, fiber tracking performance was significantly improved in coverage and fiber length. Improvements were more prominent using global tracking with refined objectives, achieving an average fiber length from 10 to 17 mm, voxel-wise coverage of axonal tracts from 0.9 to 15%, and the correlation of target areas from 40 to 68%, while minimizing false positives and impossible cross-hemisphere connections. Optimized parameters showed good generalization capability for test brain samples in both experiments, demonstrating the flexible applicability of our framework to different tracking algorithms and objectives. These results indicate the importance of data-driven adjustment of fiber tracking algorithms and support the validity of dMRI-based tractography, if appropriate adjustments are employed.
format	article
author	Carlos Enrique Gutierrez Henrik Skibbe Ken Nakae Hiromichi Tsukada Jean Lienard Akiya Watakabe Junichi Hata Marco Reisert Alexander Woodward Yoko Yamaguchi Tetsuo Yamamori Hideyuki Okano Shin Ishii Kenji Doya
author_facet	Carlos Enrique Gutierrez Henrik Skibbe Ken Nakae Hiromichi Tsukada Jean Lienard Akiya Watakabe Junichi Hata Marco Reisert Alexander Woodward Yoko Yamaguchi Tetsuo Yamamori Hideyuki Okano Shin Ishii Kenji Doya
author_sort	Carlos Enrique Gutierrez
title	Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference
title_short	Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference
title_full	Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference
title_fullStr	Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference
title_full_unstemmed	Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference
title_sort	optimization and validation of diffusion mri-based fiber tracking with neural tracer data as a reference
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/7a8bd2e00494415ab53835d71b0da8dc
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Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference

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