Using high angular resolution diffusion imaging data to discriminate cortical regions.

Brodmann's 100-year-old summary map has been widely used for cortical localization in neuroscience. There is a pressing need to update this map using non-invasive, high-resolution and reproducible data, in a way that captures individual variability. We demonstrate here that standard HARDI data...

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Autores principales:	Zoltan Nagy, Daniel C Alexander, David L Thomas, Nikolaus Weiskopf, Martin I Sereno
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ae886af91dd5444c9d810653981a56ec
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spelling	oai:doaj.org-article:ae886af91dd5444c9d810653981a56ec2021-11-18T07:45:19ZUsing high angular resolution diffusion imaging data to discriminate cortical regions.1932-620310.1371/journal.pone.0063842https://doaj.org/article/ae886af91dd5444c9d810653981a56ec2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23691102/?tool=EBIhttps://doaj.org/toc/1932-6203Brodmann's 100-year-old summary map has been widely used for cortical localization in neuroscience. There is a pressing need to update this map using non-invasive, high-resolution and reproducible data, in a way that captures individual variability. We demonstrate here that standard HARDI data has sufficiently diverse directional variation among grey matter regions to inform parcellation into distinct functional regions, and that this variation is reproducible across scans. This characterization of the signal variation as non-random and reproducible is the critical condition for successful cortical parcellation using HARDI data. This paper is a first step towards an individual cortex-wide map of grey matter microstructure, The gray/white matter and pial boundaries were identified on the high-resolution structural MRI images. Two HARDI data sets were collected from each individual and aligned with the corresponding structural image. At each vertex point on the surface tessellation, the diffusion-weighted signal was extracted from each image in the HARDI data set at a point, half way between gray/white matter and pial boundaries. We then derived several features of the HARDI profile with respect to the local cortical normal direction, as well as several fully orientationally invariant features. These features were taken as a fingerprint of the underlying grey matter tissue, and used to distinguish separate cortical areas. A support-vector machine classifier, trained on three distinct areas in repeat 1 achieved 80-82% correct classification of the same three areas in the unseen data from repeat 2 in three volunteers. Though gray matter anisotropy has been mostly overlooked hitherto, this approach may eventually form the foundation of a new cortical parcellation method in living humans. Our approach allows for further studies on the consistency of HARDI based parcellation across subjects and comparison with independent microstructural measures such as ex-vivo histology.Zoltan NagyDaniel C AlexanderDavid L ThomasNikolaus WeiskopfMartin I SerenoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 5, p e63842 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zoltan Nagy Daniel C Alexander David L Thomas Nikolaus Weiskopf Martin I Sereno Using high angular resolution diffusion imaging data to discriminate cortical regions.
description	Brodmann's 100-year-old summary map has been widely used for cortical localization in neuroscience. There is a pressing need to update this map using non-invasive, high-resolution and reproducible data, in a way that captures individual variability. We demonstrate here that standard HARDI data has sufficiently diverse directional variation among grey matter regions to inform parcellation into distinct functional regions, and that this variation is reproducible across scans. This characterization of the signal variation as non-random and reproducible is the critical condition for successful cortical parcellation using HARDI data. This paper is a first step towards an individual cortex-wide map of grey matter microstructure, The gray/white matter and pial boundaries were identified on the high-resolution structural MRI images. Two HARDI data sets were collected from each individual and aligned with the corresponding structural image. At each vertex point on the surface tessellation, the diffusion-weighted signal was extracted from each image in the HARDI data set at a point, half way between gray/white matter and pial boundaries. We then derived several features of the HARDI profile with respect to the local cortical normal direction, as well as several fully orientationally invariant features. These features were taken as a fingerprint of the underlying grey matter tissue, and used to distinguish separate cortical areas. A support-vector machine classifier, trained on three distinct areas in repeat 1 achieved 80-82% correct classification of the same three areas in the unseen data from repeat 2 in three volunteers. Though gray matter anisotropy has been mostly overlooked hitherto, this approach may eventually form the foundation of a new cortical parcellation method in living humans. Our approach allows for further studies on the consistency of HARDI based parcellation across subjects and comparison with independent microstructural measures such as ex-vivo histology.
format	article
author	Zoltan Nagy Daniel C Alexander David L Thomas Nikolaus Weiskopf Martin I Sereno
author_facet	Zoltan Nagy Daniel C Alexander David L Thomas Nikolaus Weiskopf Martin I Sereno
author_sort	Zoltan Nagy
title	Using high angular resolution diffusion imaging data to discriminate cortical regions.
title_short	Using high angular resolution diffusion imaging data to discriminate cortical regions.
title_full	Using high angular resolution diffusion imaging data to discriminate cortical regions.
title_fullStr	Using high angular resolution diffusion imaging data to discriminate cortical regions.
title_full_unstemmed	Using high angular resolution diffusion imaging data to discriminate cortical regions.
title_sort	using high angular resolution diffusion imaging data to discriminate cortical regions.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/ae886af91dd5444c9d810653981a56ec
work_keys_str_mv	AT zoltannagy usinghighangularresolutiondiffusionimagingdatatodiscriminatecorticalregions AT danielcalexander usinghighangularresolutiondiffusionimagingdatatodiscriminatecorticalregions AT davidlthomas usinghighangularresolutiondiffusionimagingdatatodiscriminatecorticalregions AT nikolausweiskopf usinghighangularresolutiondiffusionimagingdatatodiscriminatecorticalregions AT martinisereno usinghighangularresolutiondiffusionimagingdatatodiscriminatecorticalregions
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Using high angular resolution diffusion imaging data to discriminate cortical regions.

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