Longitudinal brain imaging data facilitate the monitoring of structural and functional alterations in individual brains across time, offering essential understanding of dynamic neurobiological mechanisms. Such data improve sensitivity for detecting early biomarkers of disease progression and enhance the evaluation of intervention effects. While recent matrix-response regression models can relate static brain networks to external predictors, there remain few statistical methods for longitudinal brain networks, especially those derived from high-dimensional imaging data. We introduce a matrix-response generalized linear mixed model that accommodates longitudinal brain networks and identifies edges whose connectivity is influenced by external predictors. An efficient Monte Carlo Expectation-Maximization algorithm is developed for parameter estimation. Extensive simulations demonstrate effective identification of covariate-related network components and accurate parameter estimation. We further demonstrate the usage of the proposed method through applications to diffusion tensor imaging (DTI) and functional MRI (fMRI) datasets.

翻译：纵向脑影像数据能够监测个体大脑结构和功能随时间的动态变化，为理解神经生物学动态机制提供关键洞见。此类数据可提高疾病进展早期生物标志物检测的灵敏度，并增强干预效果评估的准确性。尽管现有的矩阵响应回归模型能够建立静态脑网络与外部预测因子之间的关联，但针对纵向脑网络——尤其是源自高维影像数据的纵向网络——的统计分析方法仍然匮乏。本文提出一种矩阵响应广义线性混合模型，该模型适用于纵向脑网络分析，并能识别连接强度受外部预测因子影响的网络边。我们开发了一种高效的蒙特卡洛期望最大化算法进行参数估计。大量仿真实验表明，该方法能有效识别协变量相关的网络成分并实现精确的参数估计。我们进一步通过扩散张量成像和功能磁共振成像数据集的实证分析，展示了所提方法的应用价值。