Variability of Spatiotemporal-Rhythmic Network During Inhibitory Control in Repetitive Subconcussion.

Abstract

The inhibitory control dysfunction associated with the cognitive symptoms resulting from repetitive subconcussion (SC) is frequent. Implementing inhibitory control is temporally resolved and is likely related to the dynamic interactions in functional brain networks. However, investigations of the dynamic activity of these brain networks using electroencephalography (EEG) are often limited to specific frequency bands without entirely utilizing the spatiotemporal rhythmic information. Therefore, we proposed an innovative framework for constructing a large-scale spatiotemporal-rhythmic network (STRN) using the dynamic cross-frequency phase synchronization to track cognitive deficits induced by repetitive subconcussion during the inhibitory control. Seventeen parachuters with repeated subconcussive exposure and 17 healthy controls (HC) were subjected to a Stroop task while recording the continuous scalp EEG data. Our results indicated an STRN-specific activation pattern that achieved a high classification performance with an average accuracy of 90.98%, which may serve as a biomarker for identifying the repetitive subconcussion inhibitory control dysfunction. In this STRN state, the SC exhibited mostly lower network rhythmic information interactions than the HC. These findings suggested that the STRN presented in this study could be an effective analytical method for understanding the cognitive dysfunction observed in the repetitive subconcussion and other related conditions. The example code for calculating cross-frequency phase synchronization used to construct the STRN, as well as the code for computing the dynamic measures of STRN states (including frequency of occurrence, mean dwell time, and number of state transitions), is publicly available on GitHub at (https://github.com/Xiang-Li-Scholar/Variability-of-Spatiotemporal-Rhythmic-Network-during-Inhibitory-Control-in-Repetitive-Subconcussion).