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A0572
Title: Simultaneous inference in multiple matrix-variate graphs for high-dimensional neural recordings Authors:  Zhao Ren - University of Pittsburgh (United States) [presenting]
Abstract: 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 recording can be viewed as matrix-variate data, where the first dimension is time and the second dimension is space. We exploit the multiple matrix-variate Gaussian Graphical model (MGGM) 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. The 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 very mild assumptions on sample size and non-stationary autoregressive temporal dependence. We demonstrate the efficacy of the new method through both simulations and an experimental study with multiple local field potential (LFP) recordings in Prefrontal Cortex (PFC) and visual area V4 during a memory-guided saccade task.