Abstract
Microstructural measures from diffusion MRI have been used for classification purposes in neurodegenerative and psychiatric conditions. Novel diffusion reconstruction models can lead to better and more accurate measures of tissue properties: each measure provides different information on white matter microstructure in the brain, revealing different signs of disease. The diversity of computable measures makes it necessary to develop novel classification procedures to capture all of the available information from each measure. Here we introduce a multichannel regularized logistic regression algorithm that classifies individuals’ diagnostic status based on several microstructural measures, derived from their diffusion MRI scans. With the aid of a TV-L1 prior, which ensures sparsity in the classification model, the resulting linear models point to the most classifying brain regions for each of the diffusion MRI measures, giving the method additional descriptive power. We apply our regularized regression approach to classify Alzheimer’s disease patients and healthy controls in the ADNI dataset, based on their diffusion MRI data.
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Acknowledgment
This work is partially supported by an NIH U54 grant to the ENIGMA Center for Worldwide Medicine, Imaging & Genomics.
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Villalon-Reina, J.E. et al. (2017). Using Multiple Diffusion MRI Measures to Predict Alzheimer’s Disease with a TV-L1 Prior. In: Fuster, A., Ghosh, A., Kaden, E., Rathi, Y., Reisert, M. (eds) Computational Diffusion MRI. MICCAI 2016. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-54130-3_13
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DOI: https://doi.org/10.1007/978-3-319-54130-3_13
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