用于分析全脑网络数据的两部分混合效应建模框架
A Two-Part Mixed-Effects Modeling Framework For Analyzing Whole-Brain Network Data
摘要：
全脑网络分析仍然是神经影像学研究的先锋，在过去十年中变得突出。网络科学方法促进了这些分析，并允许将大脑作为一个集成系统进行检查。然而，建模和比较网络组的统计方法已经落后。将多变量统计方法与网络科学相融合是开发这些方法的最佳途径。为此，我们提出了一个两部分的混合效应建模框架，允许建模的概率连接（存在/不存在的边缘）和连接的强度，如果它存在。该框架内的模型能够量化结果（例如，疾病状态）和大脑中的连接模式，同时通过包括混杂协变量来减少虚假的相关性。它们还可以根据连接结构预测结果，反之亦然，模拟网络以更好地了解拓扑变化的正常范围，并利用组信息对网络进行阈值处理。因此，它们提供了一种全面的方法来研究系统水平的大脑特性，以进一步了解正常和异常的大脑功能。
英文摘要：
Whole-brain network analyses remain the vanguard in neuroimaging research, coming to prominence within the last decade. Network science approaches have facilitated these analyses and allowed examining the brain as an integrated system. However, statistical methods for modeling and comparing groups of networks have lagged behind. Fusing multivariate statistical approaches with network science presents the best path to develop these methods. Toward this end, we propose a two-part mixed-effects modeling framework that allows modeling both the probability of a connection (presence/absence of an edge) and the strength of a connection if it exists. Models within this framework enable quantifying the relationship between an outcome (e.g., disease status) and connectivity patterns in the brain while reducing spurious correlations through inclusion of confounding covariates. They also enable prediction about an outcome based on connectivity structure and vice versa, simulating networks to gain a better understanding of normal ranges of topological variability, and thresholding networks leveraging group information. Thus, they provide a comprehensive approach to studying system level brain properties to further our understanding of normal and abnormal brain function.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
