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Abstract

Alzheimer-Perusini’s disease (AD) is a severe neurodegenerative pathology mostly characterized by memory loss, with aging as a significant risk factor. While normal aging involves non-pathological changes in the brain, pathological aging involves the formation of neuronal plaques, leading to neuronal death and the macroscopic shrinkage of major brain regions. Prodromic dopaminergic alterations also affect the limbic system. We adopt a physics-inspired mathematical operator, the Krankheit-Operator, denoted as , to model brain network impairment caused by a neurological disorder. By acting on a pathological brain, plays a role in modulating disease progression. The evaluation of the -operator is conducted across different stages, from cognitive normal (CN) to Mild Cognitive Impairment (MCI) and AD. Furthermore, by adopting a machine learning-based approach, we also explore the potential use of the -operator as a diagnostic tool for predicting AD progression by starting from rs-fMRI at the initial visit. Our findings are consistent with the literature on the effects of AD on the limbic system, subcortical areas, cerebellum, and temporal lobe.