Topological Biomarkers of Alzheimer's Disease from Functional Brain Network Analysis

Alzheimer's disease is a progressive neurodegenerative condition characterized by the gradual deterioration of cognitive functions. Early identification of functional brain changes is crucial for timely diagnosis and effective intervention. This study employs multiplex network analysis to examine alterations in brain connectivity topology associated with Alzheimer's Disease, to identify early biomarkers and uncover potential therapeutic targets.

This study presents a secondary cross-sectional analysis based on a publicly available EEG dataset comprising spectral coherence measurements from 25 patients with clinically diagnosed Alzheimer's Disease (AD) and 25 age- and gender-matched Healthy Controls (HC). Functional connectivity matrices were generated across seven distinct frequency bands, with each brain region modeled as a network node and inter-regional coherence values represented as weighted edges. These matrices were then used to construct multiplex brain networks, which were rigorously analyzed using graph-theoretical approaches. The analysis encompassed key metrics, including modularity, centrality measures (Betweenness and MultiRank), motif distribution, and network controllability, to characterize and compare the underlying patterns of functional brain organization in AD and healthy aging.

Networks associated with AD exhibited significantly reduced modularity, disrupted centrality patterns, and a higher occurrence of 2 and 3-node motifs, indicating local reorganization of connectivity. Additionally, the spatial distribution of driver nodes was markedly altered in AD. Centrality analyses revealed a pronounced shift in network hubs toward the temporal and insular cortices, suggesting compensatory or pathological reallocation of influence. Controllability assessments demonstrated a lower energy requirement for network control in AD, accompanied by increased inter-layer fragmentation, reflecting compromised integrative function across frequency bands.

The findings revealed specific topological alterations, including reduced modularity, altered centrality, and decreased controllability, all of which are closely linked to AD-related network degeneration. By leveraging multi-frequency EEG data, the multiplex approach shows significant clinical potential for monitoring disease progression and supporting personalized treatments, with the ability to detect subtle connectivity disruptions before cognitive symptoms manifest.

Multiplex network analysis reveals distinct and robust alterations in the functional brain architecture of individuals with Alzheimer's Disease. These network-level disruptions offer valuable insights into the pathophysiology of AD and highlight potential avenues for early diagnosis and targeted therapeutic strategies aimed at preserving cognitive function.