Resting-state EEG activity predicts frontoparietal network reconfiguration and improved attentional performance

Jacek Rogala,Andrzej Wróbel,Rafał Krauz,Ewa Kublik

doi:10.1038/s41598-020-61866-7

Abstract

Mounting evidence indicates that resting-state EEG activity is related to various cognitive functions. To trace physiological underpinnings of this relationship, we investigated EEG and behavioral performance of 36 healthy adults recorded at rest and during visual attention tasks: visual search and gun shooting. All measures were repeated two months later to determine stability of the results. Correlation analyses revealed that within the range of 2–45 Hz, at rest, beta-2 band power correlated with the strength of frontoparietal connectivity and behavioral performance in both sessions. Participants with lower global beta-2 resting-state power (gB2rest) showed weaker frontoparietal connectivity and greater capacity for its modifications, as indicated by changes in phase correlations of the EEG signals. At the same time shorter reaction times and improved shooting accuracy were found, in both test and retest, in participants with low gB2rest compared to higher gB2rest values. We posit that weak frontoparietal connectivity permits flexible network reconfigurations required for improved performance in everyday tasks.

Highlights

Mounting evidence indicates that resting-state EEG activity is related to various cognitive functions
Consistent with the test-retest changes observed in the phase-locking value (PLV) and EEG band powers, we found alterations in the ERP courses only in the low global beta-2 resting-state power (gB2rest) group
We show that strong frontoparietal connections restrict network reconfiguration capacity and deteriorate behavioral performance

Summary

Introduction

Mounting evidence indicates that resting-state EEG activity is related to various cognitive functions. Correlation analyses revealed that within the range of 2–45 Hz, at rest, beta-2 band power correlated with the strength of frontoparietal connectivity and behavioral performance in both sessions. We hypothesized that specific electrophysiological signatures of spontaneous, individual EEG activity in large-scale networks would predict cognitive performance. To address this issue, we focused on spatial attention, one of the most broadly investigated and well-understood types of cognitive performance. The only www.nature.com/scientificreports reproducible predictor of attentional performance that was valid for both tasks during test and retest session was the resting-state beta-2 band (22–29 Hz) activity averaged over all electrodes, which negatively correlated with behavioral performance. We suggest that weaker ongoing beta-2 oscillations in long-range networks facilitate plastic changes in response to cognitive load, which subsequently leads to improved behavioral performance

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Results

Conclusion