PET‐validated EEG‐machine learning algorithm predicts 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 311 subjects with SCD (Aβ+: N = 36, Aβ‐: N = 160) or MCI (Aβ+: N = 54, Aβ‐: N = 61). We randomly excluded data for 76 subjects (SCD, Aβ+: N = 7, Aβ‐: N = 28; MCI, Aβ+: N = 6, Aβ‐: N = 7) for use in subsequent 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 several methods (Random Forest Importance (GBM, XGB), ElasticNet, Whitney‐Mann). We then trained four ML algorithms (SVM, Logistic, KNN, Naive Bayes, Random Forest(GBM/XGB)) using each relevant feature set, yielding 24 models (4 sets * 6 algorithms). The 76 validation data sets were input into each model to compare their performance.ResultThe best‐performing model (random forest importance * SVM) showed 82.9% accuracy, 90.9% sensitivity, 76.7% specificity, and 75% positive predictive value in discriminating Aβ+ from Aβ‐, regardless of MCI/SCD. In MCI alone, the model showed 82.1% accuracy, 90.0% sensitivity, 78.9% specificity, and 81.8% positive predictive value. In SCD alone, it showed, 81.1% accuracy, 92.3% sensitivity, 75% specificity, and 66.7% positive predictive value.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.