Multiscale metabolic covariance networks uncover stage-specific biomarker signatures across the Alzheimer's disease continuum.

INTRODUCTION: Functional connectomics studies leverage the power of interregional brain relationships using graph theory of glycolytic metabolism to establish neural connections and their roles in cognition and disease and to monitor therapeutic responses. METHODS: Using a retrospective clinical population (N = 431) from ADNI, we evaluated disease changes using metabolic covariance analysis. In addition, we developed a novel region set enrichment analysis (RSEA) to detect brain functional changes based on metabolic variations. Results were aligned with transcriptomic signatures and clinical cognitive assessments (CCAs). RESULTS: Our findings highlight sexual dimorphic changes across the disease spectrum, which suggest brain network reorganization occurs as compensatory mechanisms due to pathological disruptions. RSEA indicated functional changes in motor, memory, language, and cognitive functions related to disease progression, and these changes were supported by transcriptomic signatures. DISCUSSION: Together, metabolic covariance analysis, regional connectomics, and RSEA allow for AD progression tracking and functional alteration identification based on metabolic readouts, consistent with CCA.