Algebraic Connectivity Enhances Hyperedge Specificity in the Alzheimer's Disease Continuum

Abstract: Functional MRI is a neuroimaging technique aiming at analyzing the functional activity of the brain by measuring blood-oxygen-level-dependent signals throughout the brain. The derived functional features can be used for investigating brain alterations in neurological and psychiatric disorders. In this work, we employed the hypergraph structure to model high-order functional relations across brain regions, introducing algebraic connectivity (a(G)) for estimating the hyperedge weights. The hypergraph structure was derived from healthy controls to build a common topological structure across individuals. The considered cohort included subjects covering the Alzheimer's disease (AD) continuum, that is both mild cognitive impairment and AD patients. Statistical analysis was performed using the hyperedges' weights as features to assess the differences across the three groups. Additionally, a mediation analysis was performed to evaluate the effectiveness and reliability of the a(G) values, representing the functional information, as the mediator between tau-PET levels, a key biomarker of AD, and cognitive scores. The proposed approach outperformed state-of-the-art methods in identifying a larger number of hyperedges statistically different across groups. Among these, two hyperedges belonging to salience ventral attention and somatomotor networks showed a partial mediation effect between the tau biomarkers and cognitive decline. These results suggested that the a(G) can be an effective approach for extracting the hyperedge weights, including important functional information that resides in the brain areas forming the hyperedges.