EEG network features and APOE ε4 for amyloid classification in mild cognitive impairment

Resting-state electroencephalography (EEG) may provide information complementary to apolipoprotein E ε4 (APOE ε4) for identifying amyloid pathology in mild cognitive impairment. We evaluated this hypothesis in an exploratory cross-sectional study of 93 patients, including 54 with amyloid-positive positron emission tomography, using EEG functional connectivity, graph features and 11 classifiers under nested cross-validation. In the original partition, extreme gradient boosting with all domains achieved an area under the receiver operating characteristic curve (AUC) of 0.806 versus 0.682 for its APOE-only model (paired difference 0.124, 95% confidence interval 0.010–0.238). The median paired difference across 30 additional partitions was 0.088. Among non-carriers, the combined model identified 11 of 16 amyloid-positive patients, with 10 false positives among 31 amyloid-negative patients. The gain was smaller against carrier status used directly (difference 0.057, −0.043–0.157). Nesting classifier selection reduced the median AUC to 0.734, and alternative EEG epoch samples yielded AUCs of 0.571–0.773 on the original partition. These exploratory findings support further evaluation of complementary EEG and genetic information, while demonstrating dependence on the comparator, model selection and epoch sampling. Reliable EEG estimation and external validation are required before clinical use.