Baboo Gautam Kumar, Prasad Raghav, Mahajan Pranav, Baths Veeky
Department of Biological Sciences, Birla Institute of Technology and Science (BITS), Pilani-K.K. Birla Goa Campus, Zuarinagar, Sancoale, Goa, India.
Department of Computer Science and Information Systems, Birla Institute of Technology and Science (BITS), Pilani-K.K. Birla Goa Campus, Zuarinagar, Sancoale, Goa, India.
Ann Neurosci. 2022 Oct;29(4):209-224. doi: 10.1177/09727531221117633. Epub 2022 Aug 22.
The study of brain networks, particularly the spread of disease, is made easier thanks to the network theory. The aberrant accumulation of beta-amyloid plaques and tau protein tangles in Alzheimer's disease causes disruption in brain networks. The evaluation scores, such as the mini-mental state examination (MMSE) and neuropsychiatric inventory questionnaire, which provide a clinical diagnosis, are affected by this build-up.
The percolation of beta-amyloid/tau tangles and their impact on cognitive tests are still unspecified.
Percolation centrality could be used to investigate beta-amyloid migration as a characteristic of positron emission tomography (PET)-image-based networks. The PET-image-based network was built utilizing a public database containing 551 scans published by the Alzheimer's Disease Neuroimaging Initiative. Each image in the Julich atlas has 121 zones of interest, which are network nodes. Furthermore, the influential nodes for each scan are computed using the collective influence algorithm.
For five nodal metrics, analysis of variance (ANOVA; < .05) reveals the region of interest (ROI) in gray matter (GM) Broca's area for Pittsburgh compound B (PiB) tracer type. The GM hippocampus area is significant for three nodal metrics in the case of florbetapir (AV45). Pairwise variance analysis of the clinical groups reveals five to twelve statistically significant ROIs for AV45 and PiB, respectively, that can distinguish between pairs of clinical situations. Based on multivariate linear regression, the MMSE is a trustworthy evaluation tool.
Percolation values suggest that around 50 of the memory, visual-spatial skills, and language ROIs are critical to the percolation of beta-amyloids within the brain network when compared to the other extensively used nodal metrics. The anatomical areas rank higher with the advancement of the disease, according to the collective influence algorithm.
得益于网络理论，脑网络研究，尤其是疾病传播的研究变得更加容易。阿尔茨海默病中β-淀粉样蛋白斑块和tau蛋白缠结的异常积累会导致脑网络紊乱。诸如简易精神状态检查表（MMSE）和神经精神科问卷等提供临床诊断的评估分数会受到这种积累的影响。
β-淀粉样蛋白/tau缠结的渗流及其对认知测试的影响仍未明确。
渗流中心性可用于研究β-淀粉样蛋白的迁移，作为基于正电子发射断层扫描（PET）图像的网络的一个特征。基于PET图像的网络是利用一个公共数据库构建的，该数据库包含由阿尔茨海默病神经成像计划发布的551次扫描。朱利希图谱中的每张图像有121个感兴趣区域，这些区域是网络节点。此外，使用集体影响算法计算每次扫描的有影响力节点。
对于五个节点指标，方差分析（ANOVA；P <.05）揭示了匹兹堡化合物B（PiB）示踪剂类型在灰质（GM）布洛卡区的感兴趣区域（ROI）。在氟代贝他吡（AV45）的情况下，GM海马区对于三个节点指标具有显著性。临床组的成对方差分析分别揭示了AV45和PiB的五到十二个具有统计学显著性的ROI，这些ROI可以区分成对的临床情况。基于多元线性回归，MMSE是一个可靠的评估工具。
渗流值表明，与其他广泛使用的节点指标相比，约50个记忆、视觉空间技能和语言ROI对于脑网络内β-淀粉样蛋白的渗流至关重要。根据集体影响算法，随着疾病的进展，解剖区域的排名会更高。
