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BundleMAP : Anatomically localized classification, regression, and hypothesis testing in diffusion MRI

Khatami, Mohammad; Schmidt-Wilcke, Tobias; Sundgren, Pia C. LU ; Abbasloo, Amin; Schölkopf, Bernhard and Schultz, Thomas (2017) In Pattern Recognition Letters 63. p.593-600
Abstract

Diffusion MRI (dMRI) provides rich information on the white matter of the human brain, enabling insight into neurological disease, normal aging, and neuroplasticity. We present BundleMAP, an approach to extracting features from dMRI data that can be used for supervised classification, regression, and hypothesis testing. Our features are based on aggregating measurements along nerve fiber bundles, enabling visualization and anatomical interpretation. The main idea behind BundleMAP is to use the ISOMAP manifold learning technique to jointly parametrize nerve fiber bundles. We combine this idea with mechanisms for outlier removal and feature selection to obtain a practical machine learning pipeline. We demonstrate that it increases... (More)

Diffusion MRI (dMRI) provides rich information on the white matter of the human brain, enabling insight into neurological disease, normal aging, and neuroplasticity. We present BundleMAP, an approach to extracting features from dMRI data that can be used for supervised classification, regression, and hypothesis testing. Our features are based on aggregating measurements along nerve fiber bundles, enabling visualization and anatomical interpretation. The main idea behind BundleMAP is to use the ISOMAP manifold learning technique to jointly parametrize nerve fiber bundles. We combine this idea with mechanisms for outlier removal and feature selection to obtain a practical machine learning pipeline. We demonstrate that it increases accuracy of disease detection and estimation of disease activity, and that it improves the power of statistical tests.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Classification, Diffusion MRI, Disease detection, Fiber tracking, Manifold learning, Regression, Support vector machines
in
Pattern Recognition Letters
volume
63
pages
8 pages
publisher
Elsevier
external identifiers
  • scopus:84998996730
  • wos:000389785900050
ISSN
0031-3203
DOI
10.1016/j.patcog.2016.09.020
language
English
LU publication?
yes
id
6325aa4b-2b90-4a1f-9fc9-9acdf3b683ab
date added to LUP
2017-02-03 09:43:12
date last changed
2018-03-18 05:09:02
@article{6325aa4b-2b90-4a1f-9fc9-9acdf3b683ab,
  abstract     = {<p>Diffusion MRI (dMRI) provides rich information on the white matter of the human brain, enabling insight into neurological disease, normal aging, and neuroplasticity. We present BundleMAP, an approach to extracting features from dMRI data that can be used for supervised classification, regression, and hypothesis testing. Our features are based on aggregating measurements along nerve fiber bundles, enabling visualization and anatomical interpretation. The main idea behind BundleMAP is to use the ISOMAP manifold learning technique to jointly parametrize nerve fiber bundles. We combine this idea with mechanisms for outlier removal and feature selection to obtain a practical machine learning pipeline. We demonstrate that it increases accuracy of disease detection and estimation of disease activity, and that it improves the power of statistical tests.</p>},
  author       = {Khatami, Mohammad and Schmidt-Wilcke, Tobias and Sundgren, Pia C. and Abbasloo, Amin and Schölkopf, Bernhard and Schultz, Thomas},
  issn         = {0031-3203},
  keyword      = {Classification,Diffusion MRI,Disease detection,Fiber tracking,Manifold learning,Regression,Support vector machines},
  language     = {eng},
  month        = {03},
  pages        = {593--600},
  publisher    = {Elsevier},
  series       = {Pattern Recognition Letters},
  title        = {BundleMAP : Anatomically localized classification, regression, and hypothesis testing in diffusion MRI},
  url          = {http://dx.doi.org/10.1016/j.patcog.2016.09.020},
  volume       = {63},
  year         = {2017},
}