Clinical Study of PET‐validated EEG‐machine learning algorithm predicting brain amyloid pathology in pre‐dementia Alzheimer’s disease

Abstract

AbstractBackgroundCurrently positron emission tomography (PET) is used as the initial or sole biomarker of β‐amyloid (Aβ) brain pathology, which may inhibit Alzheimer’s disease (AD) drug development and clinical use due to cost, access, and tolerability. Machine learning (ML)‐based EEG biomarkers may address these challenges. Previous studies have confirmed their ability to accurately discriminate between normal, mild cognitive impairment (MCI) and Alzheimer’s dementia. However, few quantitative EEG (QEEG) biomarkers have been validated with Aβ PET. We developed a QEEG‐ML algorithm to predict brain Aβ pathology among subjective cognitive decline (SCD) and MCI patients, and validated it using Aβ PET.MethodEEG (19‐channel, eye‐closed, resting‐state) and Aβ PET data were collected from 160 human subjects with MCI (77 Aβ+, 83 Aβ‐). We excluded 45 data (23 Aβ+, 22 Aβ‐) for test verification. QEEG absolute power, relative power, power ratio, and connectivity between channels (iCoherence) comprised the input features, from which the most relevant predictive features were identified using four methods (Random Forest Importance (GBM, XGB), ElasticNet, Whitney‐Mann). We then trained six ML algorithms (SVM, Logistic, KNN, Naive Bayes, Random Forest(GBM/XGB)) using each relevant feature set, yielding 24 models (4 sets * 6 algorithms). The 45 test data sets were input into each model to compare their performance. Then, 111 MCI data (56 Aβ+, 55 Aβ‐) and 165 SCD data (31 Aβ+, 134 Aβ‐) were classified by the model for 3rd validation.ResultIn test validation, the best‐performing model showed 77.8% accuracy, 81.8% sensitivity, 73.9% specificity in discriminating Aβ+ from Aβ‐. In 3rd data validation, the best‐performing model showed 72.1% accuracy, 71.4% sensitivity, 72.7% specificity in MCI. In SCD, it showed, 69.1% accuracy, 64.5% sensitivity, 70.2% specificity.ConclusionThese findings suggest that our novel ML‐based QEEG biomarker can accurately predict the presence of brain Aβ plaque. Additional benefits of such a biomarker include reduced expense, wide availability, and high‐throughput screening and response monitoring. Future studies will assess utility as primary AD screens, adjunctive use with PET, and as a support in clinical rationales for Aβ PET and treatment choice in AD.