The study, recently published in the scientific journal GeroScience, demonstrates that analyzing nocturnal brain waves using machine learning techniques makes it possible to non-invasively identify early neural alterations and classify patients into three distinct biological subgroups.
This research focuses on sleep during a high-activity period in which the brain carries out cellular clearance of metabolic waste, including the beta-amyloid protein.
The research team analyzed a unique database combining nocturnal electrical activity recordings—known as polysomnographies—with data on protein expression obtained from patients' cerebrospinal fluid.
The research team used AI to analyze this electrical activity and identify certain changes that may detect the accumulation of proteins in the brain, which will later lead to neurodegenerative diseases.
This approach could provide a cost-effective screening pathway that is feasible from the patient's own home to detect preclinical Alzheimer's while simultaneously treating sleep disorders, thereby helping to slow the progression of cognitive decline.
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