Prediction of Cognitive Progression Due to Alzheimer's Disease in Normal Subjects Based on Individual Default Mode Network Metabolic Connectivity Strength

Abstract

BackgroundPredicting cognitive decline in those already Aβ positive or Tau positive among the aging population poses clinical challenges. In Alzheimer's disease (AD) research, intra-default mode network (DMN) connections play a pivotal role in diagnosis. This paper proposes metabolic connectivity within the DMN as a supplementary biomarker to the AT(N) framework. MethodsExtracting data from 1292 subjects in the Alzheimer’s Disease Neuroimaging Initiative, we collected paired T1-weighted structural MRI and 18F-labeled-fluorodeoxyglucose positron emission computed tomography (PET) scans. Individual metabolic DMN networks were constructed, and metabolic connectivity (MC) strength in DMN was assessed. In the cognitively unimpaired (CU) group, the Cox model identified CU(MC+), high-risk subjects, with Kaplan–Meier survival analyses and hazard ratio (HR) revealing MC strength's predictive performance. Spearman correlation analyses explored relationships between MC strength, AT(N) biomarkers, and clinical scales. DMN standard uptake value ratio (SUVR) provided comparative insights in the analyses. ResultsBoth MC strength and SUVR exhibit gradual declines with cognitive deterioration, displaying significant intergroup differences. Survival analyses indicate enhanced Aβ and Tau prediction with both metrics, with MC strength outperforming SUVR. Combined MC strength and Aβ yield optimal predictive performance (HR = 9.29), followed by MC strength and Tau (HR = 8.92). In CU(MC+), MC strength correlates significantly with CSF Aβ42 and AV45 PET SUVR (r = 0.22, -0.19). Generally, MC strength's correlation with AT(N) biomarkers exceeded SUVR. ConclusionsIndividuals with normal cognition and disrupted DMN metabolic connectivity face an elevated cognitive decline risk linked to Aβ, preceding metabolic issues.