背景：本研究旨在探索全脑白质模式作为评估老年人认知障碍和残疾的新型神经影像学生物标志物的潜力。
方法：我们对454名参与者的磁共振成像（MRI）和淀粉样蛋白正电子发射断层扫描（PET）扫描进行了深入分析，重点关注白质模式和白质主体间变异性(WM-ISV)。
结果：白质模式集成模型，结合MRI和淀粉样蛋白PET，在认知障碍和残疾方面表现出明显更高的分类性能。患有阿尔茨海默病（AD）的参与者表现出比主观认知下降的参与者更高的WM-ISV，轻度认知障碍，和血管性痴呆.此外，WM-ISV与血液生物标志物（如胶质纤维酸性蛋白和磷酸化tau-217[p-tau217]）显著相关，认知功能和残疾评分。
结论：我们的结果表明，白质模式分析作为临床决策和确定认知障碍和残疾的辅助神经影像学生物标志物具有显著潜力。
结论：集合模型结合了磁共振成像（MRI）和淀粉样蛋白正电子发射断层扫描（PET），并显示了对认知障碍和残疾的明显更高的分类性能。与主观认知衰退相比，阿尔茨海默病(AD)表现出明显更高的异质性，轻度认知障碍，或者血管性痴呆.受试者间白质变异性（WM-ISV）与基于血液的生物标志物（神经胶质纤维酸性蛋白和磷酸化tau-217[p-tau217]）和AD的多基因风险评分显着相关。白质模式分析作为临床决策过程和确定认知障碍和残疾的辅助神经影像学生物标志物具有重要的潜力。
BACKGROUND: This study aimed to explore the potential of whole brain white matter patterns as novel neuroimaging biomarkers for assessing cognitive impairment and disability in older adults.
METHODS: We conducted an in-depth analysis of magnetic resonance imaging (MRI) and amyloid positron emission tomography (PET) scans in 454 participants, focusing on white matter patterns and white matter inter-subject variability (WM-ISV).
RESULTS: The white matter pattern ensemble model, combining MRI and amyloid PET, demonstrated a significantly higher classification performance for cognitive impairment and disability. Participants with Alzheimer\'s disease (AD) exhibited higher WM-ISV than participants with subjective cognitive decline, mild cognitive impairment, and vascular dementia. Furthermore, WM-ISV correlated significantly with blood-based biomarkers (such as glial fibrillary acidic protein and phosphorylated tau-217 [p-tau217]), and cognitive function and disability scores.
CONCLUSIONS: Our results suggest that white matter pattern analysis has significant potential as an adjunct neuroimaging biomarker for clinical decision-making and determining cognitive impairment and disability.
CONCLUSIONS: The ensemble model combined both magnetic resonance imaging (MRI) and amyloid positron emission tomography (PET) and demonstrated a significantly higher classification performance for cognitive impairment and disability. Alzheimer\'s disease (AD) revealed a notably higher heterogeneity compared to that in subjective cognitive decline, mild cognitive impairment, or vascular dementia. White matter inter-subject variability (WM-ISV) was significantly correlated with blood-based biomarkers (glial fibrillary acidic protein and phosphorylated tau-217 [p-tau217]) and with the polygenic risk score for AD. White matter pattern analysis has significant potential as an adjunct neuroimaging biomarker for clinical decision-making processes and determining cognitive impairment and disability.
