Altered Baseline Brain Network Topology in High-Risk Individuals Progressing to Mild Cognitive Impairment

Abstract

BackgroundIdentifying early brain-based markers of cognitive decline is critical for preventive strategies in Alzheimers disease. Individuals with a familial risk may exhibit subtle functional brain changes years before clinical symptoms emerge. This exploratory study examined whether baseline functional brain network topology differentiates high-risk cognitively normal older adults who later progress to mild cognitive impairment (MCI) from those who remain cognitively stable. MethodsBaseline resting-state fMRI data were analyzed from 90 cognitively normal adults with a family history of Alzheimers (PREVENT-AD cohort), classified longitudinally as converters (MCI-C; n=45) or non-converters (MCI-NC; n=45). Whole-brain functional networks were analyzed across multiple thresholds; primary results are reported at 12% network density, with robustness verified at 16% density. Group differences were assessed using ANCOVA or Rank ANCOVA (controlling for age, sex, and education) at an uncorrected threshold (p < 0.05). Predictive utility was evaluated via a 100-repetition nested cross-validation machine-learning framework on a multimodal feature set combining functional network metrics, average cortical thickness, and plasma p-tau217, with covariates included within training folds. ResultsAt baseline, MCI-C participants were older, had fewer years of education, exhibited higher plasma p-tau217 levels, and showed trend-level lower MoCA scores. At 12% density, MCI-C showed increased average nodal strength (F=4.50, p=0.036, p2=0.050) and reduced global efficiency (F=4.07, p=0.046, p2=0.045). Increased betweenness centrality within the Default Mode Network (F=4.07, p=0.046, p2=0.045) and trend-level increases in average clustering (F=3.10, p=0.081, p2=0.035) were observed. Initial largest connected component (LCC) showed a trend-level decrease (F=3.84, p=0.053, p2=0.043). At 16% density, MCI-C exhibited significantly reduced initial LCC (F=4.41, p=0.038, p2=0.049) and increased nodal strength (F=4.29, p=0.041, p2=0.048), with directionally consistent trend-level reductions in global efficiency (F=3.74, p=0.056, p2=0.042). In machine learning, the k-nearest neighbors classifier showed the most stable performance (nested CV accuracy=59.6%; test F1-score=0.56). Feature stability analysis identified global efficiency (selected in 25.8% of iterations) and critical drop (19.4%) as the most consistent predictors. ConclusionBaseline disruptions in functional network integration precede clinical conversion to MCI. The consistent selection of graph-theoretical metrics, particularly global efficiency and critical drop, as top predictors suggests that functional network reorganization provides unique information for classification before widespread cortical atrophy emerges.