Machine Learning to Predict Brain Amyloid Pathology in Pre-dementia Alzheimer's Disease Using QEEG Features and Genetic Algorithm Heuristic.

Nam Heon Kim,Seong Hye Choi,Seung Wan Kang,Dong Won Yang

doi:10.3389/fncom.2021.755499

Abstract

The use of positron emission tomography (PET) as the initial or sole biomarker of β-amyloid (Aβ) brain pathology may inhibit Alzheimer’s disease (AD) drug development and clinical use due to cost, access, and tolerability. We developed a qEEG-ML algorithm to predict Aβ pathology among subjective cognitive decline (SCD) and mild cognitive impairment (MCI) patients, and validated it using Aβ PET. We compared QEEG data between patients with MCI and those with SCD with and without PET-confirmed beta-amyloid plaque. We compared resting-state eyes-closed electroencephalograms (EEG) patterns between the amyloid positive and negative groups using relative power measures from 19 channels (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2), divided into eight frequency bands, delta (1–4 Hz), theta (4–8 Hz), alpha 1 (8–10 Hz), alpha 2 (10–12 Hz), beta 1 (12–15 Hz), beta 2 (15–20 Hz), beta 3 (20–30 Hz), and gamma (30–45 Hz) calculated by FFT and denoised by iSyncBrain®. The resulting 152 features were analyzed using a genetic algorithm strategy to identify optimal feature combinations and maximize classification accuracy. Guided by gene modeling methods, we treated each channel and frequency band of EEG power as a gene and modeled it with every possible combination within a given dimension. We then collected the models that showed the best performance and identified the genes that appeared most frequently in the superior models. By repeating this process, we converged on a model that approximates the optimum. We found that the average performance increased as this iterative development of the genetic algorithm progressed. We ultimately achieved 85.7% sensitivity, 89.3% specificity, and 88.6% accuracy in SCD amyloid positive/negative classification, and 83.3% sensitivity, 85.7% specificity, and 84.6% accuracy in MCI amyloid positive/negative classification.

Highlights

Dementia is a fatal disorder characterized by progressive decline in two or more cognitive abilities including memory, language, executive and visuospatial functions, personality, and behavior (Braak and Braak, 1995)
EEG power of amyloid positive group was stronger than amyloid negative group in following frequency bands and channels: absolute power—delta F3, F7, Fz, Cz, C4,Pz, relative power— delta Fp2, F3, Fz, F4, F7, C3, Cz, C4, Pz, P4, T6, O1, and beta1 Fp1, Fp2, F3, Fz, F4, C3
EEG power of amyloid positive group was weaker in following regions: absolute power— gamma C3, T4, relative power—alpha Fp1, F3, Fz, C3, Cz, C4, Pz, T6, and gamma Fp2, F7, F8, C4, T3, T4

Summary

Introduction

Dementia is a fatal disorder characterized by progressive decline in two or more cognitive abilities including memory, language, executive and visuospatial functions, personality, and behavior (Braak and Braak, 1995). Alzheimer’s disease (AD) is the most common cause (nearly 70%) of dementia worldwide. AD is accompanied by the accumulation of β−amyloid plaques and neurofibrillary tangles of hyperphosphorylated tau protein, causing progressive neurodegeneration in specific brain regions (Hyman et al, 2012; Kang et al, 2015). Alzheimer’s disease is difficult to diagnose in its early stages because the cognitive decline can be subtle. Beta-amyloid is a well-characterized diagnostic index of AD and its accumulation can be used to predict progression from mild cognitive impairment (MCI) to dementia (Schipke et al, 2012). Cerebrospinal fluid and positron emission tomography (PET) biomarkers, combined with relatively new clinical criteria, can help diagnose AD, they are both invasive and costly (Dubois et al, 2014)

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