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.

Automated summary

Dementia is one of the most frightening emergencies for the aging population, and cognitive decline takes several years to develop. Mild cognitive impairment (MCI) is considered an early stage, with about half of the cases progressing to AD. Graph analysis tools and machine learning methods can identify distinctive features of brain aging and aid in early diagnosis and personalized risk evaluation.