Multilayer Network Analysis of Brain Signals for Detecting Alzheimer's Disease

Sean M. Nguyen, Mohammad Amin Basiri, Sina Khanmohammadi

Abstract: Human neuroimaging datasets provide rich multi-scale spatiotemporal information about the state of the brain. Most current methods, such as spectral analysis, focus on a single facet of these datasets and do not take full advantage of the inherent spatiotemporal information. Here, we consider a multilayer cross-frequency functional connectivity analysis to capture the complex spatiotemporal features of neural datasets at multiple scales and show that such features could potentially provide a better description of the neural activity. We demonstrate the effectiveness of this approach by applying the proposed method to capture disruptions of cross-frequency brain connections in Alzheimer’s patients. More specifically, we compared the multi-scale features extracted from electroencephalogram (EEG) data with traditional features in a machine learning framework to distinguish Alzheimer’s patients from control subjects. Our results show that such multi-scale features improve the prediction accuracy when compared to traditional feature extraction methods in EEG analysis.