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

- Manifold learning is used to achieve a joint parametrization of fiber bundles from diffusion MRI.
- Diffusion parameters can be plotted along the bundle.
- Anatomically localized and interpretable features are extracted.
- Increased accuracy for supervised classification and regression is demonstrated.
- Increased power for hypothesis testing is shown.

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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