As large-scale neural recordings become common, many neuroscientific investigations are focused on identifying functional connectivity from spatio-temporal measurements in two or more brain areas across multiple sessions. Spatial-temporal data in neural recordings can be represented as matrix-variate data, with time as the first dimension and space as the second. In this paper, we exploit the multiple matrix-variate Gaussian Graphical model to encode the common underlying spatial functional connectivity across multiple sessions of neural recordings. By effectively integrating information across multiple graphs, we develop a novel inferential framework that allows simultaneous testing to detect meaningful connectivity for a target edge subset of arbitrary size. Our test statistics are based on a group penalized regression approach and a high-dimensional Gaussian approximation technique. The validity of simultaneous testing is demonstrated theoretically under mild assumptions on sample size and non-stationary autoregressive temporal dependence. Our test is nearly optimal in achieving the testable region boundary. Additionally, our method involves only convex optimization and parametric bootstrap, making it computationally attractive. We demonstrate the efficacy of the new method through both simulations and an experimental study involving multiple local field potential (LFP) recordings in the Prefrontal Cortex (PFC) and visual area V4 during a memory-guided saccade task.

翻译：随着大规模神经记录变得普遍，许多神经科学研究聚焦于从多个会话中两个或多个脑区的时空测量中识别功能连接性。神经记录中的时空数据可以表示为矩阵变量数据，其中时间作为第一维度，空间作为第二维度。本文利用多矩阵变量高斯图模型来编码跨多个神经记录会话的潜在空间功能连接性。通过有效整合跨多个图的信息，我们开发了一种新颖的推断框架，允许同时检验以检测任意大小的目标边子集的有意义连接性。我们的检验统计量基于组惩罚回归方法和高维高斯逼近技术。在样本量和非平稳自回归时间依赖性的温和假设下，同时检验的有效性得到了理论证明。我们的检验在达到可检验区域边界方面近乎最优。此外，我们的方法仅涉及凸优化和参数自助法，使其在计算上具有吸引力。我们通过仿真实验和一项涉及记忆引导眼跳任务期间前额叶皮层（PFC）和视觉区域V4的多个局部场电位（LFP）记录的实验研究，证明了新方法的有效性。