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

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.

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Publication Details

Journal
IEEE Journal of Biomedical and Health Informatics
Published
2025-04-01
DOI
https://doi.org/10.1109/jbhi.2025.3556595
Citations
1
Primary Topic
Music Technology and Sound Studies
Type
article
Field-Weighted Citation Impact
1.28

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article

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

Jian Song, Jiaqi Zhang, Zhenghao Fu, Xiang Li et al.
1 citations
IEEE Journal of Biomedical and Health Informatics
Music Technology and Sound Studies
1.28
article

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

Jian Song, Jiaqi Zhang, Zhenghao Fu, Xiang Li, Huanhuan Li, Yaqian Li, Junfeng Gao, Hui Zhou, Xiang Yin, Lijie Gao, Yida He
article en
1 citations

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.

IEEE Journal of Biomedical and Health InformaticsVol. 29(8)
State Ethnic Affairs Commission (CN), Naval University of Engineering (CN), Central Hospital of Wuhan (CN), General Hospital of Central Theater Command, Southern Medical University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 22%
Music Technology and Sound Studies
1.28
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