CONVERSION FROM MCI TO ALZHEIMER'S DISEASE IS PREDICTED BY SOURCES AND COHERENCE OF BRAIN EEG RHYTHMS

Objective. Can quantitative electroencephalography (EEG) predict the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD)? Methods. Sixty-nine subjects fulfilling criteria for MCI were enrolled; cortical connectivity (spectral coherence) and (low resolution brain electromagnetic tomography) sources of EEG rhythms (delta=2-4 Hz; theta=4-8 Hz; alpha 1=8-10.5 Hz; alpha 2=10.5-13 Hz: beta 1=13-20 Hz; beta 2=20-30 Hz; and gamma=30-40) were evaluated at baseline (time of MCI diagnosis) and follow up (about 14 months later). At follow-up, 45 subjects were still MCI (MCI Stable) and 24 subjects were converted to AD (MCI Converted). Results. At baseline, fronto-parietal midline coherence as well as delta (temporal), theta (parietal, occipital and temporal), and alpha 1 (central, parietal, occipital, temporal, limbic) sources were stronger in MCI Converted than stable subjects (P<0.05). Cox regression modeling showed low midline coherence and weak temporal source associated with 10% annual rate AD conversion, while this rate increased up to 40% and 60% when strong temporal delta source and high midline gamma coherence were observed respectively. Interpretation. Low-cost and diffuse computerized EEG techniques are able to statistically predict MCI to AD conversion.