The study of complex brain networks, where structural or functional connections are evaluated to create an interconnected representation of the brain, has grown tremendously over the past decade. Many of the statistical network science tools for analyzing brain networks have been developed for cross-sectional studies and for the analysis of static networks. However, with both an increase in longitudinal study designs and an increased interest in the neurological network changes that occur during the progression of a disease, sophisticated methods for longitudinal brain network analysis are needed. We propose a paradigm for longitudinal brain network analysis over patient cohorts, with the key challenge being the adaptation of Stochastic Actor-Oriented Models to the neuroscience setting. Stochastic Actor-Oriented Models are designed to capture network dynamics representing a variety of influences on network change in a continuous-time Markov chain framework. Network dynamics are characterized through both endogenous (i.e. network related) and exogenous effects, where the latter include mechanisms conjectured in the literature. We outline an application to the resting-state functional magnetic resonance imaging setting with data from the Alzheimer’s Disease Neuroimaging Initiative study. We draw illustrative conclusions at the subject level and make a comparison between elderly controls and individuals with Alzheimer’s disease.

中文翻译：

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神经科学中患者队列的纵向复杂网络分析范式

在过去十年中，对复杂大脑网络的研究，其中结构或功能连接被评估以创建大脑的相互关联的表示，已经取得了巨大的发展。许多用于分析大脑网络的统计网络科学工具已被开发用于横断面研究和静态网络分析。然而，随着纵向研究设计的增加和对疾病进展过程中发生的神经网络变化的兴趣增加，需要用于纵向脑网络分析的复杂方法。我们提出了一种对患者群组进行纵向脑网络分析的范例，关键挑战是随机面向演员的模型适应神经科学环境。面向随机参与者的模型旨在捕获网络动态，该动态表示在连续时间马尔可夫链框架中对网络变化的各种影响。网络动态通过内生（即网络相关）和外生效应来表征，后者包括文献中推测的机制。我们使用来自阿尔茨海默病神经影像学倡议研究的数据概述了静息状态功能磁共振成像设置的应用。我们在主题层面得出说明性结论，并对老年对照和阿尔茨海默病患者进行比较。网络相关）和外生效应，后者包括文献中推测的机制。我们使用来自阿尔茨海默病神经影像学倡议研究的数据概述了静息状态功能磁共振成像设置的应用。我们在主题层面得出说明性结论，并对老年对照和阿尔茨海默病患者进行比较。网络相关）和外生效应，后者包括文献中推测的机制。我们使用来自阿尔茨海默病神经影像学倡议研究的数据概述了在静息状态功能磁共振成像环境中的应用。我们在主题层面得出说明性结论，并对老年对照和阿尔茨海默病患者进行比较。