A Comparative Study of EEG-Based Nonlinear Effective Connectivity for Classifying Alzheimer’s Disease and Frontotemporal Dementia

Background

Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are progressive neurodegenerative disorders affecting over 100 million people worldwide. Effective connectivity (EC), which quantifies directional influence between brain regions, offers insight into disrupted neural circuit dynamics. This study aimed to identify neurophysiological biomarkers of AD and FTD and elucidate mechanisms distinguishing them from cognitively normal (CN) controls. Event-related EEG signals from 88 subjects (36 AD, 23 FTD, 29 CN) were analysed.