Wu Qianying, Lei Hui, Mao Tianxin, Deng Yao, Zhang Xiaocui, Jiang Yali, Zhong Xue, Detre John A, Liu Jianghong, Rao Hengyi
Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai 201613, China.
Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Brain Sci. 2023 May 19;13(5):825. doi: 10.3390/brainsci13050825.
Resting-state functional magnetic resonance imaging (fMRI) with graph theoretical modeling has been increasingly applied for assessing whole brain network topological organization, yet its reproducibility remains controversial. In this study, we acquired three repeated resting-state fMRI scans from 16 healthy controls during a strictly controlled in-laboratory study and examined the test-retest reliability of seven global and three nodal brain network metrics using different data processing and modeling strategies. Among the global network metrics, the characteristic path length exhibited the highest reliability, whereas the network small-worldness performed the poorest. Nodal efficiency was the most reliable nodal metric, whereas betweenness centrality showed the lowest reliability. Weighted global network metrics provided better reliability than binary metrics, and reliability from the AAL90 atlas outweighed those from the Power264 parcellation. Although global signal regression had no consistent effects on the reliability of global network metrics, it slightly impaired the reliability of nodal metrics. These findings provide important implications for the future utility of graph theoretical modeling in brain network analyses.
采用图论模型的静息态功能磁共振成像（fMRI）已越来越多地用于评估全脑网络拓扑组织，但其可重复性仍存在争议。在本研究中，我们在严格控制的实验室研究中，对16名健康对照者进行了三次重复的静息态fMRI扫描，并使用不同的数据处理和建模策略，检验了七种全局和三种节点脑网络指标的重测信度。在全局网络指标中，特征路径长度表现出最高的信度，而网络小世界性质的信度最差。节点效率是最可靠的节点指标，而中介中心性的信度最低。加权全局网络指标比二值指标具有更好的信度，并且来自AAL90图谱的信度高于来自Power264脑区划分的信度。虽然全局信号回归对全局网络指标的信度没有一致的影响，但它会轻微损害节点指标的信度。这些发现为图论模型在脑网络分析中的未来应用提供了重要启示。
