Mapping topographic structure in white matter pathways with level set trees.

Fiber tractography on diffusion imaging data offers rich potential for describing white matter pathways in the human brain, but characterizing the spatial organization in these large and complex data sets remains a challenge. We show that level set trees--which provide a concise representation of th...

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Autores principales:	Brian P Kent, Alessandro Rinaldo, Fang-Cheng Yeh, Timothy Verstynen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1017cf9ac7b74ee3b2d9ad9f5f30f591
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id	oai:doaj.org-article:1017cf9ac7b74ee3b2d9ad9f5f30f591
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spelling	oai:doaj.org-article:1017cf9ac7b74ee3b2d9ad9f5f30f5912021-11-18T08:24:19ZMapping topographic structure in white matter pathways with level set trees.1932-620310.1371/journal.pone.0093344https://doaj.org/article/1017cf9ac7b74ee3b2d9ad9f5f30f5912014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24714673/?tool=EBIhttps://doaj.org/toc/1932-6203Fiber tractography on diffusion imaging data offers rich potential for describing white matter pathways in the human brain, but characterizing the spatial organization in these large and complex data sets remains a challenge. We show that level set trees--which provide a concise representation of the hierarchical mode structure of probability density functions--offer a statistically-principled framework for visualizing and analyzing topography in fiber streamlines. Using diffusion spectrum imaging data collected on neurologically healthy controls (N = 30), we mapped white matter pathways from the cortex into the striatum using a deterministic tractography algorithm that estimates fiber bundles as dimensionless streamlines. Level set trees were used for interactive exploration of patterns in the endpoint distributions of the mapped fiber pathways and an efficient segmentation of the pathways that had empirical accuracy comparable to standard nonparametric clustering techniques. We show that level set trees can also be generalized to model pseudo-density functions in order to analyze a broader array of data types, including entire fiber streamlines. Finally, resampling methods show the reliability of the level set tree as a descriptive measure of topographic structure, illustrating its potential as a statistical descriptor in brain imaging analysis. These results highlight the broad applicability of level set trees for visualizing and analyzing high-dimensional data like fiber tractography output.Brian P KentAlessandro RinaldoFang-Cheng YehTimothy VerstynenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 4, p e93344 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Brian P Kent Alessandro Rinaldo Fang-Cheng Yeh Timothy Verstynen Mapping topographic structure in white matter pathways with level set trees.
description	Fiber tractography on diffusion imaging data offers rich potential for describing white matter pathways in the human brain, but characterizing the spatial organization in these large and complex data sets remains a challenge. We show that level set trees--which provide a concise representation of the hierarchical mode structure of probability density functions--offer a statistically-principled framework for visualizing and analyzing topography in fiber streamlines. Using diffusion spectrum imaging data collected on neurologically healthy controls (N = 30), we mapped white matter pathways from the cortex into the striatum using a deterministic tractography algorithm that estimates fiber bundles as dimensionless streamlines. Level set trees were used for interactive exploration of patterns in the endpoint distributions of the mapped fiber pathways and an efficient segmentation of the pathways that had empirical accuracy comparable to standard nonparametric clustering techniques. We show that level set trees can also be generalized to model pseudo-density functions in order to analyze a broader array of data types, including entire fiber streamlines. Finally, resampling methods show the reliability of the level set tree as a descriptive measure of topographic structure, illustrating its potential as a statistical descriptor in brain imaging analysis. These results highlight the broad applicability of level set trees for visualizing and analyzing high-dimensional data like fiber tractography output.
format	article
author	Brian P Kent Alessandro Rinaldo Fang-Cheng Yeh Timothy Verstynen
author_facet	Brian P Kent Alessandro Rinaldo Fang-Cheng Yeh Timothy Verstynen
author_sort	Brian P Kent
title	Mapping topographic structure in white matter pathways with level set trees.
title_short	Mapping topographic structure in white matter pathways with level set trees.
title_full	Mapping topographic structure in white matter pathways with level set trees.
title_fullStr	Mapping topographic structure in white matter pathways with level set trees.
title_full_unstemmed	Mapping topographic structure in white matter pathways with level set trees.
title_sort	mapping topographic structure in white matter pathways with level set trees.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/1017cf9ac7b74ee3b2d9ad9f5f30f591
work_keys_str_mv	AT brianpkent mappingtopographicstructureinwhitematterpathwayswithlevelsettrees AT alessandrorinaldo mappingtopographicstructureinwhitematterpathwayswithlevelsettrees AT fangchengyeh mappingtopographicstructureinwhitematterpathwayswithlevelsettrees AT timothyverstynen mappingtopographicstructureinwhitematterpathwayswithlevelsettrees
_version_	1718421785002639360

Mapping topographic structure in white matter pathways with level set trees.

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