Quantitative EEG and apolipoprotein E-genotype improve classification of patients with suspected Alzheimer's disease

To establish a model for better identification of patients in very early stages of Alzheimer's disease, AD (including patients with amnestic MCI) using high-resolution EEG and genetic data.; A total of 26 patients in early stages of probable AD and 12 patients with amnestic MCI were included. Both groups were similar in age and education. All patients had a comprehensive neuropsychological examination and a high resolution EEG. Relative band power characteristics were calculated in source space (LORETA inverse solution for spectral data) and compared between groups. A logistic regression model was calculated including relative band-power at the most significant location, ApoE status, age, education and gender.; Differences in the delta band at 34 temporo-posterior source locations (p>.01) between AD and MCI groups were detected after correction for multiple comparisons. Classification slightly increased when ApoE status was added (p=.06 maximum likelihood test). Adjustment of analyses for the confounding factors age, gender and education did not alter results.; Quantitative EEG (qEEG) separates between patients with amnestic MCI and patients in early stages of probable AD. Adding information about Apo ε4 allele frequency slightly enhances diagnostic accuracy.; qEEG may help identifying patients who are candidates for possible benefit from future disease modifying treatments.