Network connectivity revealed through non-negative matrix factorization in rat medial prefrontal cortex during working memory task

Abstract

Working memory (WM) is necessary for higher cognitive functions. To understand the mechanism of WM from the view of network connectivity becomes a hot topic in the field of neuroscience. The purpose of this work is to develop a sparse causal analysis (SCA) based on non-negative matrix factorization (NMF), and to investigate dynamic connections among the multi-channel spikes during WM. 16-channels spikes were obtained from 4 SD rats (10 tasks for each rat) at prefrontal cortex with implanted microelectrode arrays during WM tasks in Y-maze and transformed to continuous series. Spike series in the original recoding space were projected into the sparse space via NMF with the dimensional reduction. The G-causality (GC) values in the sparse space were calculated to analyze the connections among the spikes directly. Furthermore, the causal density (Cd) and global efficiency (E) were selected to describe the network connections quantitatively. The results show that: in the sparse space, the GC increased from 0.028±0.007 at the beginning of the task to the maximum value 0.100±0.022, Cd likewise from 0.235±0.057 to 0.483±0.056 and E from 0.350±0.065 to 0.616±0.058. The values of GC, Cd and E in the sparse space were significant higher than those in the original space, especially the maximum values (p<;0.01). These findings could lead to help understanding the mechanism of connections during WM: the connections in the spike networks changed dynamically, and the strongest connection in the brain happened before the behavior. The addition, the sparse causal network method could improve the identification about network connectivity of spikes during WM and might provide an effective analysis for the connectivity of neural signals in cognitive functions.