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

Mohammad Khatami, Tobias Schmidt-Wilcke, Pia C. Sundgren, Amin Abbasloo, Bernhard Schölkopf, Thomas Schultz

Research output: Contribution to journalArticlepeer-review

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 accuracy of disease detection and estimation of disease activity, and that it improves the power of statistical tests.

Original languageEnglish
Pages (from-to)593-600
Number of pages8
JournalPattern Recognition
Volume63
DOIs
Publication statusPublished - 2017 Mar 1

Subject classification (UKÄ)

  • Radiology, Nuclear Medicine and Medical Imaging

Free keywords

  • Classification
  • Diffusion MRI
  • Disease detection
  • Fiber tracking
  • Manifold learning
  • Regression
  • Support vector machines

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