Peak-alpha frequency varies across individuals and mental states, but it also forms a negative gradient from posterior to anterior regions in association with increases in cortical thickness and connectivity reflecting the cortical hierarchy in temporal integration. Tracking the spatial standard deviation of peak-alpha frequency in scalp EEG, we observed that the posterior-to-anterior gradient dynamically formed and dissolved. Periods of high spatial standard deviation yielded robustly negative posterior-to-anterior gradients—the gradient state—while periods of low spatial standard deviation yielded globally converged peak-alpha frequency—the uniform state. Our analyses suggest that the fluctuations between the gradient and uniform states are associated with two separate variables: (1) coordinated variations in peak-alpha frequency in anterior regions and (2) coordinated variations in peak-alpha power in central regions driven by posterior regions. These variables each accounted for ~25% of the state fluctuations, were uncorrelated with each other, and together accounted for ~50% of the state fluctuations. These results reflect general mechanisms as they replicated while participants engaged in a variety of behavioral tasks with their eyes closed (breath focus, vigilance, working memory, mental arithmetic, and generative thinking). Overall, our results suggest that the spatial pattern of peak-alpha frequency dynamically fluctuates between the gradient state, potentially facilitating information influx and temporal integration toward anterior regions, and the uniform state, potentially facilitating global communication, with the state fluctuations controlled by at least two distinct mechanisms, an anterior mechanism that directly adjusts peak-alpha frequencies and a posterior mechanism that indirectly adjusts them by influencing synchronization.

Last modified

• 01/31/2024

Creator

• Suzuki, Satoru

Contributors

• Marcia Grabowecky
• Max Kailler Smith

DOI

• https://doi.org/10.21985/n2-d0bt-a448

Publisher

• Northwestern University

Date created

• 2021/07/20

Resource type

• Dataset

License

• Open Data Commons Attribution License (ODC-By)

Total Size

• 5.86 GB