Dynamic networks have been increasingly used to characterize brain connectivity that varies during resting and task states. In such characterizations, a connectivity network is typically measured at each time point for a subject over a common set of nodes representing brain regions, together with rich subject-level information. A common approach to analyzing such data is an edge-based method that models the connectivity between each pair of nodes separately. However, such approach may have limited performance when the noise level is high and the number of subjects is limited, as it does not take advantage of the inherent network structure. To better understand if and how the subject-level covariates affect the dynamic brain connectivity, we introduce a semi-parametric dynamic network response regression that relates a dynamic brain connectivity network to a vector of subject-level covariates. A key advantage of our method is to exploit the structure of dynamic imaging coefficients in the form of high-order tensors. We develop an efficient estimation algorithm and evaluate the efficacy of our approach through simulation studies. Finally, we present our results on the analysis of a task-related study on social cognition in the Human Connectome Project, where we identify known sex-specific effects on brain connectivity that cannot be inferred using alternative methods.

翻译：动态网络越来越多地被用于描述在静息态和任务态下变化的大脑连接性。在这种描述中，通常在每个时间点为受试者测量一个连接网络，该网络基于一组代表脑区的共同节点，同时结合了丰富的受试者层面信息。分析这类数据的一种常见方法是基于边的模型，该模型分别对每对节点之间的连接性进行建模。然而，当噪声水平较高且受试者数量有限时，这种方法可能表现有限，因为它未能利用固有的网络结构。为了更好地理解受试者层面协变量是否以及如何影响动态大脑连接性，我们引入了一种半参数动态网络响应回归方法，该方法将动态大脑连接网络与受试者层面协变量向量关联起来。我们方法的一个关键优势是利用高阶张量形式的动态成像系数结构。我们开发了一种高效的估计算法，并通过模拟研究评估了我们方法的有效性。最后，我们展示了在人类连接组项目中对一项社会认知任务相关研究的分析结果，其中我们识别出已知的性别特异性对大脑连接性的影响，而使用替代方法无法推断出这些影响。