Prediction of Cognitive Task Activations via Resting-State Functional Connectivity Networks: An EEG Study

Luyao Wang, Jian Zhang, Tiantian Liu, Duanduan Chen, Dikun Yang, Ritsu Go, Jinglong Wu, Tianyi Yan


1 被引用数 (Scopus)


Objective: The resting state is an internal state that is closely related to neural activation and the performance of tasks. Studying the relationship between the resting and task states is helpful for understanding the organization of information processing. It remains unclear how information is translated between these two states. Methods: In this study, we focused on electroencephalography (EEG) data because its high time resolution allowed us to study processing both overall and in detail. Resting-state functional connectivity (FC) networks were constructed in the time and frequency domains. Results: FC constructed by synchronization of signals in the time domain was suitable for predicting event-related potential activation. In addition, FC measured by phase distributions had superior prediction accuracy for predicting spectral power. Conclusion: Our findings suggest that there is intrinsic organization across the two states. Furthermore, the activity flow modeled in different domains could reflect different levels of neuronal activation. Significance: Changes in neural activity across resting and task states on a subsecond time scale can be detected by EEG, which is helpful for understanding the underlying mechanisms of illness and therapeutic outcomes.

ジャーナルIEEE Transactions on Cognitive and Developmental Systems
出版ステータスPublished - 3月 1 2022

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能


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