NeuroMap: Functional Brain Mapping of Alzheimer’s Disease and Frontotemporal Dementia using EEG and Graph Theory

Abstract

Abstract Alzheimer’s disease (AD) and Frontotemporal Dementia (FTD) represent significant global health challenges, characterized by cognitive impairments and often regarded as disconnection syndromes with unclear pathological mechanisms and diagnostic complexities. While electroencephalography (EEG) offers a promising, cost-effective avenue for identifying neurological biomarkers, its analysis faces challenges such as volume conduction and interrater variability. Previous network analysis approaches in this domain have often relied on binary networks, potentially overlooking crucial information in weighted connections, and suboptimal community detection algorithms like Louvain, which can produce unreliable results. This study addresses these limitations by performing a comprehensive network analysis of AD and FTD using EEG data, with electrodes as nodes and functional connectivity measures as edges. The methodology involves rigorous EEG pre-processing, including Butterworth bandpass filtering, Artifact Subspace Reconstruction (ASR), and Independent Component Analysis (ICA). Data is segmented into 12.288-second epochs and filtered into five distinct frequency bands (delta, theta, alpha, beta, gamma). Functional connectivity is computed using the Phase Lag Index (PLI) for delta and theta bands, and Amplitude Envelope Correlation with Leakage Correction (AEC-c) for alpha, beta, and gamma bands, applying specific thresholds. Adjacency matrices are constructed, averaged across epochs, and then at the group level. Key network parameters (e.g., mean node degree, clustering coefficient, path length, efficiency) and centrality measures are calculated. Crucially, the study employs the Leiden algorithm and average-linkage hierarchical clustering for community detection, overcoming the shortcomings of prior methods. Furthermore, this research includes the development of an online EEG analysis system to facilitate user upload, processing, visualization of results (adjacency matrices, network parameters, centrality measures, clusters), and data export. This work aims to provide deeper insights into the topological architecture and functional organization of brain networks in AD and FTD, ultimately contributing to more objective, data-driven diagnostic methods and a practical tool for clinical and research applications.