ArticlebioRxiv : the preprint server for biology2025
Multiscale Metabolic Covariance Networks Uncover Stage-Specific Biomarker Signatures Across the Alzheimer's Disease Continuum.
Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Connectomics studies analyze neural connections and their roles in cognition and disease. Beyond regional comparisons, recent research has revealed inter-regional brain relationships via graph theory of brain network connectivity. Within these networks, path length measures a network's efficiency in communication. These connections can be quantified as inter-subject covariance networks related to functional connectivity, with alterations reported in neurodegenerative diseases. Methods: Retrospective analysis of ADNI Findings: In AD subjects, whole brain metabolic connectivity revealed an increase in entropy, connection strength, and clustering coefficients, which indicates brain network reorganization as compensatory mechanisms of pathological disruption. As AD advances, path lengths between brain regions decrease from CN to MCI; however, path lengths significantly increased in AD. RSEA indicated functional changes in motor, memory, language, and cognition functions related to disease progression. Interpretation: Metabolic covariance analysis of whole brain, and regional connectomics, track with AD progression. Moreover, path lengths permitted AD stages determination via alterations in brain connectivity. Furthermore, RSEA facilitated the identification of functional changes based on metabolic readouts. Funding: NIH grant T32AG071444.
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