Machine Learning on Visibility Graph Features Discriminates the Cognitive Event-Related Potentials of Patients with Early Alzheimer’s Disease from Healthy Aging

Abstract

We present a framework for electroencephalography (EEG)-based classification between patients with Alzheimer’s Disease (AD) and robust normal elderly (RNE) via a graph theory approach using visibility graphs (VGs). This EEG VG approach is motivated by research that has demonstrated differences between patients with early stage AD and RNE using various features of EEG oscillations or cognitive event-related potentials (ERPs). In the present study, EEG signals recorded during a word repetition experiment were wavelet decomposed into 5 sub-bands (δ,θ,α,β,γ). The raw and band-specific signals were then converted to VGs for analysis. Twelve graph features were tested for differences between the AD and RNE groups, and t-tests employed for feature selection. The selected features were then tested for classification using traditional machine learning and deep learning algorithms, achieving a classification accuracy of 100% with linear and non-linear classifiers. We further demonstrated that the same features can be generalized to the classification of mild cognitive impairment (MCI) converters, i.e., prodromal AD, against RNE with a maximum accuracy of 92.5%. Code is released online to allow others to test and reuse this framework.

Amortization Transformer for Brain Effective Connectivity Estimation from fMRI Data

“…Both functional and effective connectivity can be generated from functional magnetic resonance imaging (fMRI) data, FC describes statistical dependencies between brain regions, while EC is the causal influence that one brain region exerts over another [2,3], which characterizes the causality between brain regions. As different individuals exhibit different EC networks, the discrepancies among EC networks offer an effective way to evaluate normal brain functions and the brain injuries related to some neurodegenerative diseases [1], such as Alzheimer's disease [4], Parkinson's disease [5] and autism spectrum disorder [6]. Therefore, the estimation of brain effective connectivity from fMRI data is a critical scientific problem in the investigation of the human brain connectome.…”