Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 100864, People's Republic of China.
Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114, United States.
Cereb Cortex. 2023 Oct 9;33(20):10649-10659. doi: 10.1093/cercor/bhad312.
Alzheimer's disease can be detected early through biomarkers such as tau positron emission tomography (PET) imaging, which shows abnormal protein accumulations in the brain. The standardized uptake value ratio (SUVR) is often used to quantify tau-PET imaging, but topological information from multiple brain regions is also linked to tau pathology. Here a new method was developed to investigate the correlations between brain regions using subject-level tau networks. Participants with cognitive normal (74), early mild cognitive impairment (35), late mild cognitive impairment (32), and Alzheimer's disease (40) were included. The abnormality network from each scan was constructed to extract topological features, and 7 functional clusters were further analyzed for connectivity strengths. Results showed that the proposed method performed better than conventional SUVR measures for disease staging and prodromal sign detection. For example, when to differ healthy subjects with and without amyloid deposition, topological biomarker is significant with P < 0.01, SUVR is not with P > 0.05. Functionally significant clusters, i.e. medial temporal lobe, default mode network, and visual-related regions, were identified as critical hubs vulnerable to early disease conversion before mild cognitive impairment. These findings were replicated in an independent data cohort, demonstrating the potential to monitor the early sign and progression of Alzheimer's disease from a topological perspective for individual.
阿尔茨海默病可以通过 tau 正电子发射断层扫描（PET）等生物标志物进行早期检测，tau PET 成像显示大脑中异常蛋白质的积累。标准化摄取值比（SUVR）常用于定量 tau-PET 成像，但来自多个大脑区域的拓扑信息也与 tau 病理学相关。在这里，开发了一种新方法来使用基于个体的 tau 网络研究大脑区域之间的相关性。参与者包括认知正常（74 人）、早期轻度认知障碍（35 人）、晚期轻度认知障碍（32 人）和阿尔茨海默病（40 人）。从每个扫描中构建异常网络以提取拓扑特征，并进一步分析 7 个功能聚类的连接强度。结果表明，与传统的 SUVR 测量相比，该方法在疾病分期和前驱期标志物检测方面表现更好。例如，当区分有和无淀粉样蛋白沉积的健康受试者时，拓扑生物标志物具有统计学意义（P<0.01），而 SUVR 没有统计学意义（P>0.05）。功能上显著的聚类，即内侧颞叶、默认模式网络和与视觉相关的区域，被确定为轻度认知障碍之前疾病转化的关键枢纽。这些发现被独立的数据队列复制，表明从拓扑角度监测个体阿尔茨海默病的早期标志物和进展具有潜力。
