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Simultaneous Inference in Multiple Matrix-Variate Graphs for High-Dimensional Neural Recordings by Liu, Zongge; Bong, Heejong; Ren, Zhao; Smith, Matthew A.; Kass, Robert E. is a scholarly article available to read on EtoBox.

What is Simultaneous Inference in Multiple Matrix-Variate Graphs for High-Dimensional Neural Recordings about?

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 parametr

Author

Liu, Zongge; Bong, Heejong; Ren, Zhao; Smith, Matthew A.; Kass, Robert E.

Published

2024

Language

EN