ArticleCommunications medicine2025
Integrating individualized connectome with amyloid pathology improves predictive modeling of future cognitive decline.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Amyloid-beta relates to default and attention network connectivity during working memory retrieval.Brain communications · 2026Article
- Integrating individualized connectome with amyloid pathology improves predictive modeling of future cognitive decline.Communications medicine · 2025Article
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6 authors.
Funding
Abstract
backgroundThe deposition of amyloid-β (Aβ) in the human brain is a hallmark of Alzheimer's disease and is associated with cognitive decline. Aβ pathology is traditionally assessed at the whole-brain level across neocortical regions using positron emission tomography (PET). However, these measures often show weak associations with future cognitive impairment. A more sensitive pathology metric is needed to quantify early Aβ burden and better predict cognitive decline. Here, we aim to develop a network-based metric of Aβ burden to improve early prediction of cognitive decline in aging populations.
methodsWe integrated subject-specific brain connectome information with Aβ-PET measures to construct a network-based metric of Aβ burden. Cross-validated predictive modeling was used to evaluate the performance of this metric in predicting longitudinal cognitive decline. Furthermore, we identified a neuropathological signature pattern linked to future cognitive decline, and we validated this pattern in an independent cohort.
resultsOur results demonstrate that incorporating individualized structural connectome, but not functional connectome, information into Aβ measures enhances predictive performance for prospective cognitive decline. The identified neuropathological signature pattern is reproducible across cohorts.
conclusionThese findings advance our understanding of the spatial patterns of Aβ pathology and its relationship to brain networks, highlighting the potential of connectome-informed network-based metrics for Aβ-PET imaging in identifying individuals at higher risk of cognitive decline.
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