Abstract
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> 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. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> 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. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> 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. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> 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. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Significance:</i> 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.
Highlights
Electroencephalography (EEG) studies investigated task-evoked activation due to the high time resolution, providing a better understanding of how cognitive processes emerge
We constructed an activity flow mapping model to test whether resting-state functional connectivity (FC) networks could predict cognitive task activations in motion and imaginary motion tasks (Fig. 1b)
The well-known phase coherence (PHC) method basically resembles the conventional statistic for circular data [23]
Summary
Electroencephalography (EEG) studies investigated task-evoked activation due to the high time resolution, providing a better understanding of how cognitive processes emerge. Event-related potentials (ERPs) mark neuronal responses to stimuli in the time domain and have been linked to attention [1], change detection [2], and control processes [3]. Yan are with Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China
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