School of Electronic Information, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.
Behav Neurol. 2022 Jul 4;2022:9958525. doi: 10.1155/2022/9958525. eCollection 2022.
Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely employed to examine brain functional connectivity (FC) alterations in various neurological disorders. At present, various computational methods have been proposed to estimate connectivity strength between different brain regions, as the edge weight of FC networks. However, little is known about which model is more sensitive to Alzheimer's disease (AD) progression. This study comparatively characterized topological properties of rs-FC networks constructed with Pearson correlation (PC), dynamic time warping (DTW), and group information guided independent component analysis (GIG-ICA), aimed at investigating the sensitivity and effectivity of these methods in differentiating AD stages. A total of 54 subjects from Alzheimer's Disease Neuroimaging Initiative (ANDI) database, divided into healthy control (HC), mild cognition impairment (MCI), and AD groups, were included in this study. Network-level (global efficiency and characteristic path length) and nodal (clustering coefficient) metrics were used to capture groupwise difference across HC, MCI, and AD groups. The results showed that almost no significant differences were found according to global efficiency and characteristic path length. However, in terms of clustering coefficient, 52 brain parcels sensitive to AD progression were identified in rs-FC networks built with GIG-ICA, much more than PC (6 parcels) and DTW (3 parcels). This indicates that GIG-ICA is more sensitive to AD progression than PC and DTW. The findings also confirmed that the AD-linked FC alterations mostly appeared in temporal, cingulate, and angular areas, which might contribute to clinical diagnosis of AD. Overall, this study provides insights into the topological properties of rs-FC networks over AD progression, suggesting that FC strength estimation of FC networks cannot be neglected in AD-related graph analysis.
静息态功能磁共振成像（rs-fMRI）已广泛应用于研究各种神经退行性疾病的脑功能连接（FC）改变。目前，已经提出了各种计算方法来估计不同脑区之间的连接强度，作为 FC 网络的边权重。然而，对于哪种模型对阿尔茨海默病（AD）进展更敏感，我们知之甚少。本研究采用 Pearson 相关系数（PC）、动态时间 warping（DTW）和基于组信息引导的独立成分分析（GIG-ICA）构建 rs-FC 网络，比较了它们的拓扑特征，旨在研究这些方法在区分 AD 阶段中的敏感性和有效性。本研究共纳入了来自阿尔茨海默病神经影像学倡议（ADNI）数据库的 54 名受试者，分为健康对照组（HC）、轻度认知障碍组（MCI）和 AD 组。使用网络级（全局效率和特征路径长度）和节点（聚类系数）指标来捕捉 HC、MCI 和 AD 组之间的组间差异。结果表明，根据全局效率和特征路径长度，几乎没有发现显著差异。然而，在聚类系数方面，在基于 GIG-ICA 构建的 rs-FC 网络中，发现了 52 个对 AD 进展敏感的脑区，明显多于 PC（6 个脑区）和 DTW（3 个脑区）。这表明 GIG-ICA 比 PC 和 DTW 更能敏感地检测 AD 进展。研究结果还证实，与 AD 相关的 FC 改变主要出现在颞叶、扣带回和角回等区域，这可能有助于 AD 的临床诊断。总之，本研究深入了解了 rs-FC 网络在 AD 进展过程中的拓扑特征，提示在 AD 相关的图分析中不能忽视 FC 网络的 FC 强度估计。
