Yue Kun, Webster Jason, Grabowski Thomas, Jahanian Hesamoddin, Shojaie Ali
Department of Biostatistics, University of Washington, Seattle.
Department of Radiology, University of Washington, Seattle.
bioRxiv. 2024 Jun 3:2024.06.02.597071. doi: 10.1101/2024.06.02.597071.
Alzheimer's disease (AD) has a prolonged latent phase. Sensitive biomarkers of amyloid beta ( ), in the absence of clinical symptoms, offer opportunities for early detection and identification of patients at risk. Current biomarkers, such as CSF and PET biomarkers, are effective but face practical limitations due to high cost and limited availability. Recent blood plasma biomarkers, though accessible, still incur high costs and lack physiological significance in the Alzheimer's process. This study explores the potential of brain functional connectivity (FC) alterations associated with AD pathology as a non-invasive avenue for detection. While current stationary FC measurements lack sensitivity at the single-subject level, our investigation focuses on dynamic FC using resting-state functional MRI (rs-fMRI) and introduces the Generalized Auto-Regressive Conditional Heteroscedastic Dynamic Conditional Correlation (DCC-GARCH) model. Our findings demonstrate the superior sensitivity of DCC-GARCH to CSF status, and offer key insights into dynamic functional connectivity analysis in AD.
阿尔茨海默病（AD）有一个漫长的潜伏期。在没有临床症状的情况下，淀粉样β蛋白（ ）的敏感生物标志物为早期检测和识别有风险的患者提供了机会。目前的生物标志物，如脑脊液和正电子发射断层扫描生物标志物，虽然有效，但由于成本高和可用性有限而面临实际限制。最近的血浆生物标志物虽然容易获取，但仍然成本高昂，并且在阿尔茨海默病进程中缺乏生理意义。本研究探讨了与AD病理相关的脑功能连接（FC）改变作为一种非侵入性检测途径的潜力。虽然目前的静态FC测量在单个体水平上缺乏敏感性，但我们的研究重点是使用静息态功能磁共振成像（rs-fMRI）的动态FC，并引入了广义自回归条件异方差动态条件相关（DCC-GARCH）模型。我们的研究结果证明了DCC-GARCH对脑脊液 状态具有更高的敏感性，并为AD中的动态功能连接分析提供了关键见解。
