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

doi:10.64898/2026.02.05.704129

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

2026 · doi:10.64898/2026.02.05.704129

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This exploratory PREVENT-AD cohort study analyzed baseline resting-state fMRI from 90 cognitively normal older adults with a family history of Alzheimer’s, comparing those who later converted to mild cognitive impairment (MCI-C) versus those who remained cognitively stable (MCI-NC) using whole-brain functional network topology at multiple densities. Baseline differences included trend-level lower MoCA scores in MCI-C and higher plasma p-tau217, alongside network alterations: at 12% density, MCI-C showed increased nodal strength, reduced global efficiency, and increased Default Mode Network betweenness centrality, with a trend-level reduction in initial largest connected component. At 16% density, MCI-C had significantly reduced initial LCC and increased nodal strength, with directionally consistent global efficiency reductions; machine learning using multimodal features identified global efficiency and critical drop as the most stable predictors, with k-nearest neighbors achieving nested CV accuracy of 59.6% and test F1-score of 0.56. Limitations explicitly noted in the abstract include an uncorrected group-comparison threshold (p < 0.05) and the exploratory nature of the approach. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Background Identifying early brain-based markers of cognitive decline is critical for preventive strategies in Alzheimer’s 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.

Methods

Baseline resting-state fMRI data were analyzed from 90 cognitively normal adults with a family history of Alzheimer’s (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.

Results

At 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, ηp2=0.050) and reduced global efficiency (F=4.07, p=0.046, ηp2=0.045). Increased betweenness centrality within the Default Mode Network (F=4.07, p=0.046, ηp2=0.045) and trend-level increases in average clustering (F=3.10, p=0.081, ηp2=0.035) were observed. Initial largest connected component (LCC) showed a trend-level decrease (F=3.84, p=0.053, ηp2=0.043). At 16% density, MCI-C exhibited significantly reduced initial LCC (F=4.41, p=0.038, ηp2=0.049) and increased nodal strength (F=4.29, p=0.041, ηp2=0.048), with directionally consistent trend-level reductions in global efficiency (F=3.74, p=0.056, ηp2=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.

Conclusion

Baseline 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. Competing Interest Statement The authors have declared no competing interest.

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