Time-locked activities of resting-state networks that transiently occur in human resting-state brain activities

Spontaneous brain activities at rest are organized into resting-state networks, such as the default mode network. While the default mode network is assumed to interact with other networks to integrate distributed information, its fine-scale temporal dynamics remain unclear. Here, we demonstrate how the default mode network interacts with other networks at millisecond-scale temporal resolution.

Using a large dataset of resting-state magnetoencephalography and our recently proposed method, we estimated the spatiotemporal patterns that recur in resting-state brain activity and then examined the interactions across the resting-state networks in these patterns. In the alpha/beta band component of the spatiotemporal patterns, the default mode network and the visual/sensorimotor network alternately appear with a lag of about 24/13 ms, corresponding to about π/2 rad in the alpha/beta oscillation. These alternating and orthogonal activities of the resting-state networks may reflect the brain’s strategy to achieve both the integration and separation of visual and sensorimotor information via the default mode network.

Furthermore, we reveal how these activities consecutively move between the default mode network and the visual/sensorimotor network in tens of milliseconds. They move in different ways depending on their direction, suggesting that distinct mechanisms generate the movements in different directions. Data collection and sharing for this project was provided by the Cambridge Centre for Ageing and Neuroscience (CamCAN).

Data collection for this project was also provided by HCP Young Adult. This research was supported by the Acquisition, Technology & Logistics Agency (ATLA) (JPJ004596), the Japan Agency for Medical Research and Development (AMED) (JP25wm0625420), JST K Program (JPMJKP25Y7), and JSPS KAKENHI (JP21K07269, JP24K15691). The CamCAN project was funded by the UK Biotechnology and Biological Sciences Research Council (grant number BB/H008217/1), along with support from the UK Medical Research Council and University of Cambridge, UK.

Computational Brain Dynamics Team, RIKEN Center for Advanced Intelligence Project, Soraku-gun, Kyoto, Japan Department of Computational Brain Imaging, ATR Neural Information Analysis Laboratories, Soraku-gun, Kyoto, Japan Yusuke Takeda, Nobuo Hiroe & Okito Yamashita The authors declare no competing interests. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material.

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