Revealing heterogeneity in mild cognitive impairment based on individualized structural covariance network

作者：Xiaotong Wei, Ronglong Xiong, Ping Xu, Tingting Zhang, Junjun Zhang, Zhenlan Jin, Ling Li · 发表于：Alzheimer s Research & Therapy · 年份：2025 · DOI：10.1186/s13195-025-01752-4 · 被引用次数：8 · 研究领域：Dementia and Cognitive Impairment Research、Functional Brain Connectivity Studies、Alzheimer's disease research and treatments

BACKGROUND: Mild cognitive impairment (MCI) is a heterogeneous disorder with significant individual variabilities in clinical and biological features. Abnormal inter-regional structural covariance suggests disruption of the brain structural network in MCI. Most studies have examined group-level structural covariance alterations while ignoring individual-level differences. Hence, we aimed to investigate the heterogeneity of MCI using individual differential structural covariance network (IDSCN) analysis. METHODS: T1-weighted images of 596 MCI patients and 309 cognitively normal (CN) were collected from the ADNI database as discovery dataset, and 122 MCI and 117 CN from the OASIS-3 dataset as validation cohort. We constructed each patient's IDSCN using regional gray matter volume and applied K-means clustering analysis to identify MCI subtypes based on significantly altered covariance edges. Then, clinical features, brain structure, and gene expression profiles were evaluated for each subtype. RESULTS: In the ADNI dataset, MCI patients exhibited significant alterations in structural covariance edges, mainly involving the hippocampus, parahippocampal gyrus, and amygdala. Two robust MCI subtypes were identified. Subtype 1 showed faster disease progression relative to subtype 2, which was validated in the independent OASIS-3 dataset. Significant differences between two subtypes were found in clinical cognition and biomarkers, cerebral atrophy patterns, and enriched genes for metal...