(1) Background: Network analysis allows investigators to explore the many facets of brain networks, particularly the proliferation of disease. One of the hypotheses behind the disruption in brain networks in Alzheimer’s disease is the abnormal accumulation of beta-amyloid plaques and tau protein tangles. In this study, the potential use of percolation centrality to study beta-amyloid movement was studied as a feature of given PET image-based networks; (2) Methods: The PET image-based network construction is possible using a public access database - Alzheimer’s Disease Neuroimaging Initiative, which provided 551 scans. For each image, the Julich atlas provides 121 regions of interest, which are the network nodes. Besides, using the collective influence algorithm, the influential nodes for each scan are calculated; (3) Analysis of variance (p<0.05) yields the region of interest Gray Matter Broca’s Area for PiB tracer type for five nodal metrics. In comparison, AV45: the Gray Matter Hippocampus region is significant for three of the nodal metrics. Pairwise variance analysis between the clinical groups yields five and twelve statistically significant ROIs for AV45 and PiB, capable of distinguishing between pairs of clinical conditions. Multivariate linear regression between the percolation centrality values for nodes and psychometric assessment scores reveals Mini-Mental State Examination is reliable(4) Conclusion: percolation centrality effectively (41% of ROIs) indicates that the regions of interest that are part of the memory, visual-spatial skills, and language are crucial to the percolation of beta-amyloids within the brain network to the other widely used nodal metrics. Ranking the regions of interest based on the collective influence algorithm indicates the anatomical areas strongly influencing the beta-amyloid network.

中文翻译：

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使用渗透中心性和集体影响算法跟踪 β-淀粉样蛋白斑块的进展和影响：一项使用 PET 图像的研究

(1) 背景：网络分析使研究人员能够探索大脑网络的许多方面，尤其是疾病的扩散。阿尔茨海默病大脑网络中断背后的假设之一是 β-淀粉样斑块和 tau 蛋白缠结的异常积累。在这项研究中，作为给定的基于 PET 图像的网络的一个特征，研究了渗透中心性在研究 β-淀粉样蛋白运动中的潜在用途；(2)方法：基于PET图像的网络构建可以使用公共访问数据库——阿尔茨海默病神经影像学倡议，提供551次扫描。对于每张图像，Julich 图集提供了 121 个感兴趣的区域，它们是网络节点。此外，使用集体影响算法，计算每次扫描的影响节点；(3)方差分析(p<0。05) 产生五个节点度量的 PiB 示踪剂类型的感兴趣区域灰质布洛卡区。相比之下，AV45：灰质海马区域对于三个节点指标很重要。临床组之间的成对方差分析为 AV45 和 PiB 产生了五个和十二个具有统计学意义的 ROI，能够区分临床条件对。节点的渗透中心性值与心理测量评估分数之间的多元线性回归揭示了简易精神状态检查是可靠的 (4) 结论：有效的渗透中心性（ROI 的 41%）表明感兴趣的区域是记忆、视觉的一部分- 空间技能和语言对于大脑网络中的 β-淀粉样蛋白渗透到其他广泛使用的节点指标至关重要。