Structural brain connectome embedding for Alzheimer's disease

Abstract

Neurodegenerative diseases are known to alter brain connectivity. Alzheimer’s Disease (AD) is the most common one among these diseases. Although, many re searches have been made to understand AD, there are still more to explore about the complicated nature of AD. To solve these mysteries, features extracted from connec tomes are widely used. Following the poor specificity of global connectome features, more recently focus has been shifted towards substructures as potential biomarkers. A new model, inspired by the Deepwalk, is proposed to represent these substructures in this thesis. The model treats each individual connectome as a unique graph and learns nodal embeddings per connectome by means of a random walk and a neural network approach. The learned nodal embeddings are used as latent representations of local connectivity and their discriminative power is assessed in SVM based leave-one-out ex periments over a cohort of 91 individuals. Promising results were obtained for AD-SCI / AD-MCI / MCI-SCI / AD-MCI-SCI classification tasks. Apart from classification, such latent representations of local connectivity may serve as an appropriate space to define the continuum of neurodegenerative disease progression temporally and spatially which means nodal embeddings can be utilized for monitoring disease progression