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Supplementary data for "A comparative evaluation of voxel-based spatial mapping in diffusion tensor imaging" Version 1

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Overview

Year:
2016
Contributor:
Cabeen, Ryan P. (creator)
Subject:
Reproducibile research
Neuroimaging
White Matter
Diffusion tensor imaging
Spatial mapping
Evaluation
Aging
DOI
https://doi.org/10.7301/Z0ZC80SW

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Description

Abstract:
This archive contains raw experimental results from a comparative evaluation of methods for automated voxel-based spatial mapping in diffusion tensor imaging studies. Such methods are an essential step in computational pipelines and provide anatomically comparable measurements across a population in atlas-based studies. To better understand their strengths and weaknesses, we tested a total of eight methods for voxel-based spatial mapping in two types of diffusion tensor templates. The methods were evaluated with respect to scscan-rescan reliability and an application to normal aging. The archive contains full volumetric datasets to enable the exploration of specific brain areas, diffusion parameters, and statistical results that were not specifically presented in the associated paper.
Notes:
Funder information: NThis research is funded by NIH/NINDS grant R01 NS052470, NIH/NIMH grant R01, MH085604, and NIH grant R01 EB004155.

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Access Conditions

Use and Reproduction
This work is licensed under a Creative Commons Attribution 4.0 International License

Citation

Cabeen, Ryan P., "Supplementary data for 'A comparative evaluation of voxel-based spatial mapping in diffusion tensor imaging' Version 1 " (2016). Brown University Open Data Collection. Brown Digital Repository. Brown University Library. https://doi.org/10.7301/Z0ZC80SW

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    Brown University Open Data Collection

    This collection contains open and publicly-funded data sets created by Brown University faculty and student researchers. Increasingly, publishers, and funders are requiring that protocols, data sets, metadata, and code underlying published research be retained and preserved, their locations cited within …
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