The demented brain wiring undergoes several changes with dementia progression. However, in early dementia stages, particularly early mild cognitive impairment (eMCI), these remain challenging to spot. Hence, developing accurate diagnostic techniques for eMCI identification is critical for early intervention to prevent the onset of Alzheimer’s Disease (AD). There is a large body of machine-learning based research developed for classifying different brain states (e.g., AD vs MCI). These works can be fundamentally grouped into two categories. The first uses correlational methods which aim to identify the most correlated features for diagnosis. The second includes discriminative methods which identify brain features that distinguish between two brain states. However, existing methods examine these correlational and discriminative brain data independently, overlooking the complementary information provided by both techniques, which could prove to be useful in the classification of patients with dementia. On the other hand, how early dementia affects cortical brain connections in morphology remains largely unexplored. To address these limitations, we propose a joint correlational and discriminative ensemble learning framework for eMCI diagnosis that leverages a novel brain network representation, derived from the cortex. Our framework outperformed several state-of-the-art methods by 4-7% including independent correlational and discriminative methods.
Name: Rory Raeper
Supervisor: Dr. Islem Rekik