时间标签：数据日期 2026-10-31 · 生成时间 2026-11-01 05:30（Asia/Shanghai）｜ 检索窗口 PubMed edat 2026/10/28–2026/10/31 ｜ 情报发生日期 2026-10-31
研究问题
To address this gap, we introduce a graph neural network (GNN) that uses morphometric features—cortical thickness, surface area, curvature, gray/white matter intensity ratio (GWR), sulcal depth—to estimate LBA across the cortical surface at high spatial resolution (mean inter-vertex distance = 1.37 mm).；Regional LBA gaps are significantly associated with neuropsychological measures of AD-related cognitive impairment, linking cortical aging patterns to clinical outcomes.；The difference between BA and CA—termed BA gap (BAG)—reflects advanced (BAG > 0 y) or delayed (BAG < 0 y) brain aging, and may serve as a candidate biomarker of excessive aging observed in neurodegenerative disorders such as AD [18-20].
方法
Trained on cortical surface meshes extracted from the MRIs of cognitively normal (CN) adults (N = 14,423), our model achieves lower mean absolute error (MAE) than the existing state-of-the-art while identifying more biologically plausible patterns of aging in Alzheimer’s disease (AD) on the ADNI dataset.；All participants provided written informed consent at their respective contributing institutions.；Model evaluation was performed using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, which included 1,129 scans from CN participants, 153 scans from MCI subjects, and 477 scans from individuals with a clinical diagnosis of AD [51].；For ADNI CN adults, inclusion criteria are: no memory complaints, a Clinical Dementia Rating (CDR) of zero, no significant impairment in cognitive function or activities of daily living, and a score of at least nine on the Logical Memory II subscal
核心结论
Trained on cortical surface meshes extracted from the MRIs of cognitively normal (CN) adults (N = 14,423), our model achieves lower mean absolute error (MAE) than the existing state-of-the-art while identifying more biologically plausible patterns of aging in Alzheimer’s disease (AD) on the ADNI dataset.；AD subjects demonstrate significant aging across the entire cortex, particularly within medial temporal regions and associated cortical networks.；Regional LBA gaps are significantly associated with neuropsychological measures of AD-related cognitive impairment, linking cortical aging patterns to clinical outcomes. ‖ 结论：All intellectual content and conclusions are the authors’ own.
局限
Although the effects of chronological age (CA) on cortical aging have been studied extensively, CA alone does not adequately capture the heterogeneity of aging effects across individuals.；While these intensities encode subtle molecular and structural interactions within brain tissue [38], their relationship to cortical morphology is complex [39], limiting the biological interpretability of provided LBAs.；Methods Data. 𝑇𝑇1w MRI scans were aggregated across multiple sources to enhance the generalizability of our model and findings.
对本项目的价值
★☆☆☆☆ 脑老化是最大未满足需求，也是终点最难量化的领域；biomarker 与认知读数选择最敏感。队列：ADNI；最大样本量约 14,423。
全文来源
出版商 OA PDF（正文 70,717 字符，已过 DOI 逐字 / 标题指纹归属校验）
原文题录
- 英文标题：Graph Neural Network Reveals the Cortical Morphology of Local Brain Aging in Normal Cognition and Alzheimer's Disease.
- 期刊与标识：Neuroinformatics · PMID 42955542 · DOI 10.1007/s12021-026-09808-y
A 级证据分级：A 级（同行评议人体研究）
本文属人体研究证据，但需注意：观察性或小样本干预的结论不等于已证实临床获益；效应量与长期安全性仍需更大规模、更长随访的试验验证。
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