BundleMAP: Anatomically Localized Classification, Regression, and Hypothesis Testing in Diffusion MRI

Mohammad Khatami, Tobias Schmidt-Wilcke, Pia C. Sundgren, Amin Abbasloo, Bernhard Schölkopf, and Thomas Schultz
In: Pattern Recognition (2017), 63(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 accuracy of disease detection and estimation of disease activity, and that it improves the power of statistical tests.

Images

Download Paper

Download Paper

Bibtex

@ARTICLE{Khatami:PR2017,
    author = {Khatami, Mohammad and Schmidt-Wilcke, Tobias and Sundgren, Pia C. and Abbasloo, Amin and Sch{\"o}lkopf,
              Bernhard and Schultz, Thomas},
     pages = {593--600},
     title = {BundleMAP: Anatomically Localized Classification, Regression, and Hypothesis Testing in Diffusion
              MRI},
   journal = {Pattern Recognition},
    volume = {63},
      year = {2017},
  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.}
}