Alzheimer's disease (AD) is the most common neurodegenerative disorder. While AD diagnosis traditionally relies on clinical criteria, recent trends favor a precise biological definition. Existing biomarkers efficiently detect AD pathology but inadequately reflect the extent of cognitive impairment or disease heterogeneity. Alternative tools, such as neuroimaging and neurophysiological techniques, might better assess actual functional impairment.
Objective
To explore a multimodal approach—combining quantitative electroencephalography (EEG), cerebrospinal fluid (CSF), and cognitive assessment—to identify the most effective predictors of cognitive decline in AD patients.
Methods
In this observational study, 28 biologically confirmed AD patients underwent baseline evaluations including high-density EEG, CSF biomarker analysis, and cognitive assessment (Mini-Mental State Examination, MMSE). Cognitive assessment was repeated after one year. The rate of cognitive decline was calculated as monthly MMSE score change.
Results
Median baseline age was 71.2 years. Median monthly MMSE decline was 0.25 points. Patients were classified into slow (≤0.25 MMSE/month) or fast (>0.25 MMSE/month) progressors. Fast progressors had significantly lower baseline individual alpha frequency (IAF) (6.8 Hz versus 9.1 Hz; p = 0.003), lower MMSE at one year (19 versus 24; p = 0.02), and more frequent diabetes and cardiovascular history. A multivariate regression analysis adjusted for age revealed that baseline IAF (p = 0.002), initial MMSE score (p = 0.028), and the p-tau/Aβ42 ratio (p < 0.01) significantly predicted monthly cognitive decline.
Conclusions
Combining quantitative EEG-derived IAF, baseline cognitive status, and CSF biomarkers (p-tau/Aβ42 ratio) enhances prediction of AD progression. This integrated approach better captures disease heterogeneity, highlighting the need for multimodal strategies in the prognosis and management of AD.
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