Early dementia diagnosis, MCI‐to‐dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis

INTRODUCTION: Dementia in its various forms represents one of the most frightening emergencies for the aging population. Cognitive decline—including Alzheimer's disease (AD) dementia—does not develop in few days; disease mechanisms act progressively for several years before clinical evidence. METHODS: A preclinical stage, characterized by measurable cognitive impairment, but not overt dementia, is represented by mild cognitive impairment (MCI), which progresses to—or, more accurately, is already in a prodromal form of—AD in about half cases; people with MCI are therefore considered the population at risk for AD deserving special attention for validating screening methods. RESULTS: Graph analysis tools, combined with machine learning methods, represent an interesting probe to identify the distinctive features of physiological/pathological brain aging focusing on functional connectivity networks evaluated on electroencephalographic data and neuropsychological/imaging/genetic/metabolic/cerebrospinal fluid/blood biomarkers. DISCUSSION: On clinical data, this innovative approach for early diagnosis might provide more insight into pathophysiological processes underlying degenerative changes, as well as toward a personalized risk evaluation for pharmacological, nonpharmacological, and rehabilitation treatments.