Title:Topological Biomarkers of Alzheimer’s Disease from Functional Brain Network Analysis
Volume: 22
Issue: 8
Author(s): Soudeh Behrouzinia and Alireza Khanteymoori*
Affiliation:
- Department of Psychology, University of Freiburg, Freiburg, Germany
Keywords:
Alzheimer’s disease, brain networks, complex networks, multiplex networks, controllability, neurodegeneration.
Abstract:
Introduction: 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.
Methods: 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.
Results: 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.
Discussion: 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.
Conclusion: 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.
