PMID- 33403904 OWN - NLM STAT- MEDLINE DCOM- 20211025 LR - 20211025 IS - 2158-0022 (Electronic) IS - 2158-0014 (Linking) VI - 11 IP - 6 DP - 2021 Aug TI - Dynamic Configuration of Coactive Micropatterns in the Default Mode Network During Wakefulness and Sleep. PG - 471-482 LID - 10.1089/brain.2020.0827 [doi] AB - Background: The default mode network (DMN) is a prominent intrinsic network that is observable in many mammalian brains. However, a few studies have investigated the temporal dynamics of this network based on direct physiological recordings. Methods: Herein, we addressed this issue by characterizing the dynamics of local field potentials from the rat DMN during wakefulness and sleep with an exploratory analysis. We constructed a novel coactive micropattern (CAMP) algorithm to evaluate the configurations of rat DMN dynamics, and further revealed the relationship between DMN dynamics with different wakefulness and alertness levels. Results: From the gamma activity (40-80 Hz) in the DMN across wakefulness and sleep, three spatially stable CAMPs were detected: a common low-activity level micropattern (cDMN), an anterior high-activity level micropattern (aDMN), and a posterior high-activity level micropattern (pDMN). A dynamic balance across CAMPs emerged during wakefulness and was disrupted in sleep stages. In the slow-wave sleep (SWS) stage, cDMN became the primary activity pattern, whereas aDMN and pDMN were the major activity patterns in the rapid eye movement sleep stage. In addition, further investigation revealed phasic relationships between CAMPs and the up-down states of the slow DMN activity in the SWS stage. Conclusion: Our study revealed that the dynamic configurations of CAMPs were highly associated with different stages of wakefulness, and provided a potential three-state model to describe the DMN dynamics for wakefulness and alertness. Impact statement In the current study, a novel coactive micropattern (CAMP) method was developed to elucidate fast default mode network (DMN) dynamics during wakefulness and sleep. Our findings demonstrated that the dynamic configurations of DMN activity are specific to different wakefulness stages and provided a three-state DMN CAMP model to depict wakefulness levels, thus revealing a potentially new neurophysiological representation of alertness levels. This work could elucidate the DMN dynamics underlying different stages of wakefulness and have important implications for the theoretical understanding of the neural mechanism of wakefulness and alertness. FAU - Cui, Yan AU - Cui Y AD - The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China. FAU - Li, Min AU - Li M AD - The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China. FAU - Biswal, Bharat AU - Biswal B AD - The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China. AD - Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA. FAU - Jing, Wei AU - Jing W AD - The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China. AD - Department of Physiology, School of Basic Medicine and Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. FAU - Zhou, Changsong AU - Zhou C AD - Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong. FAU - Liu, Huixiao AU - Liu H AD - The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China. FAU - Guo, Daqing AU - Guo D AD - The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China. AD - Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China. FAU - Xia, Yang AU - Xia Y AD - The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China. FAU - Yao, Dezhong AU - Yao D AD - The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China. AD - Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China. AD - School of Electrical Engineering, Zhengzhou University, Zhengzhou, China. LA - eng PT - Journal Article PT - Research Support, Non-U.S. Gov't DEP - 20210419 PL - United States TA - Brain Connect JT - Brain connectivity JID - 101550313 SB - IM MH - Animals MH - *Brain/diagnostic imaging MH - Default Mode Network MH - Magnetic Resonance Imaging MH - Rats MH - Sleep MH - *Wakefulness OTO - NOTNLM OT - coactive micropattern OT - default mode network OT - dynamic configuration OT - up-down states OT - wakefulness and sleep EDAT- 2021/01/07 06:00 MHDA- 2021/10/26 06:00 CRDT- 2021/01/06 08:40 PHST- 2021/01/07 06:00 [pubmed] PHST- 2021/10/26 06:00 [medline] PHST- 2021/01/06 08:40 [entrez] AID - 10.1089/brain.2020.0827 [doi] PST - ppublish SO - Brain Connect. 2021 Aug;11(6):471-482. doi: 10.1089/brain.2020.0827. Epub 2021 Apr 19.