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MVD-Fuse: Detection of White Matter Degeneration via Multi-View Learning of Diffusion Microstructure
Shreyas Fadnavis

Detecting neuro-degenerative disorders in early-stage and asymptomatic patients is challenging. Diffusion MRI (dMRI) has shown great success in generating biomarkers for cellular organization at the microscale level using complex biophysical models, but there has never been a consensus on a clinically usable standard model. Here, we propose a new framework (MVD-Fuse) to integrate measures of diverse diffusion models to detect alterations of white matter microstructure. The spatial maps generated by each measure are considered as a different diffusion representation (view), the fusion of these views being used to detect differences between clinically distinct groups. We investigate three different strategies for performing intermediate fusion: neural networks (NN), multiple kernel learning (MKL) and multi-view boosting (MVB). As a proof of concept, we applied MVD-Fuse to a classification of premanifest Huntington's disease (pre-HD) individuals and healthy controls in the TRACK-ON cohort. Our results indicate that the MVD-Fuse boosts predictive power, especially with MKL (0.90 AUC vs 0.85 with the best single diffusion measure). Overall, our results suggest that an improved characterization of pathological brain microstructure can be obtained by combining various measures from multiple diffusion models.

Author Information

Shreyas Fadnavis (Indiana University Bloomington)

Image Processing, Computer Vision, Optimization, Applied Mathematics and Neuroimaging

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