Application of probabilistically weighted graphs to image-based diagnosis of Alzheimer's disease using diffusion MRI

Syeda Maryam; Laura McCrackin; Mark Crowley; Yogesh Rathi; Oleg Michailovich

doi:10.1117/12.2254164

3 March 2017 Application of probabilistically weighted graphs to image-based diagnosis of Alzheimer's disease using diffusion MRI

Syeda Maryam, Laura McCrackin, Mark Crowley, Yogesh Rathi, Oleg Michailovich

Author Affiliations +

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101342F (2017) https://doi.org/10.1117/12.2254164
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

The world’s aging population has given rise to an increasing awareness towards neurodegenerative disorders, including Alzheimers Disease (AD). Treatment options for AD are currently limited, but it is believed that future success depends on our ability to detect the onset of the disease in its early stages. The most frequently used tools for this include neuropsychological assessments, along with genetic, proteomic, and image-based diagnosis. Recently, the applicability of Diffusion Magnetic Resonance Imaging (dMRI) analysis for early diagnosis of AD has also been reported. The sensitivity of dMRI to the microstructural organization of cerebral tissue makes it particularly well-suited to detecting changes which are known to occur in the early stages of AD. Existing dMRI approaches can be divided into two broad categories: region-based and tract-based. In this work, we propose a new approach, which extends region-based approaches to the simultaneous characterization of multiple brain regions. Given a predefined set of features derived from dMRI data, we compute the probabilistic distances between different brain regions and treat the resulting connectivity pattern as an undirected, fully-connected graph. The characteristics of this graph are then used as markers to discriminate between AD subjects and normal controls (NC). Although in this preliminary work we omit subjects in the prodromal stage of AD, mild cognitive impairment (MCI), our method demonstrates perfect separability between AD and NC subject groups with substantial margin, and thus holds promise for fine-grained stratification of NC, MCI and AD populations.

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Syeda Maryam, Laura McCrackin, Mark Crowley, Yogesh Rathi, and Oleg Michailovich "Application of probabilistically weighted graphs to image-based diagnosis of Alzheimer's disease using diffusion MRI", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101342F (3 March 2017); https://doi.org/10.1117/12.2254164

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