Developing an Individualized Deep Connectome Framework for ADRD Analysis

As the most and the second most common type of dementia, AD and Lewy body dementias (LBD), including dementia with Lewy bodies and Parkinson’s disease dementia, account for 65% to 85% individuals with AD/ADRD. Misdiagnosis between AD and ADRD, e.g., AD vs. LBD, will lead to non-beneficial, incomplete, or

even harmful treatment and management options. Comparing to diagnosis and prediction of AD from normal aging, differentiation between AD and LBD is very challenging, due to both mixed pathologies and clinical symptoms. Current MRI-based neuroimaging studies are limited to group-wise analysis between AD and LBD

patients and controls, and there are significant challenges in dealing with the remarkable heterogeneity in AD/ADRD pathologies and clinical symptoms, and in pinpointing specific and subtle abnormalities across different individual AD/ADRD brains. In this project, we will significantly advance and integrate our powerful

methods/tools and apply them to multiple AD/LBD datasets to discover and identify individualized connectome- scale differences between AD and LBD, by leveraging the cutting-edge deep learning techniques. Specifically, we will 1) discover, define and represent individual GyralNets to characterize brain connectome heterogeneity

and AD/LBD related abnormalities for individual AD/LBD patient; 2) learn a cortical surface transformation to align GyralNets from population to individuals using unsupervised spherical networks and 3) develop a new infrastructure to integrate multiple types of connectome data including anatomical, structural and functional

connectome, and characterize, represent and summarize their deep relationship as a “individual connectome signature” by maximizing its prediction capability between AD and LBD.