摘要
睡眠和清醒之间的差异对人类的健康至关重要, 清醒和睡眠之间的转换紊乱伴随脑部疾病, 因此需要深入研究其具体特征. 本研究引入网络可控性揭示大脑脑电活动中频率成分的功能特异性. 具体来说, 我们采用一个公开的颅内立体脑电图数据集. 首先, 记录受试者清醒和睡眠条件下的脑电信号, 经过降噪、 伪迹去除等预处理方法, 通过带通滤波提取亚慢波 (0.1~1 Hz)、 δ (1~4 Hz)、 θ (4~8 Hz)、 α (8~13 Hz)、 β (13~30 Hz) 和 γ (30~45 Hz) 波段振荡. 其次, 利用锁相值 (PLV) 和不重叠滑动时间窗从时间窗脑电神经振荡中提取动态功能连通性. 最后, 在这些时变大脑网络上计算平均和模态网络的可控性. 初步结果显示, 清醒和睡眠状态下, 不同频段脑电活动在额顶网络 (FPN)、 显著网络 (SN) 和默认模式网络 (DMN) 存在显著差异, 即不同频率成分的脑电信号以不同网络控制策略参与大脑清醒和睡眠. 网络可控性揭示了清醒和睡眠条件下的潜在大脑动力学, 网络可控性和动态功能网络的结合为表征大脑清醒和睡眠阶段的区别提供了新的度量方法.
This is a preview of subscription content, log in via an institution to check access.
References
Andrillon T, Burns A, Mackay T, et al., 2021. Predicting lapses of attention with sleep-like slow waves. Nat Commun, 12: 3657. https://doi.org/10.1038/s41467-021-23890-7
Beynel L, Deng LF, Crowell CA, et al., 2020. Structural controllability predicts functional patterns and brain stimulation benefits associated with working memory. J Neurosci, 40(35):6770–6778. https://doi.org/10.1523/JNEUROSCI.0531-20.2020
Cheng W, Rolls ET, Ruan HT, et al., 2018. Functional connectivities in the brain that mediate the association between depressive problems and sleep quality. JAMA Psychiatry, 75(10):1052–1061. https://doi.org/10.1001/jamapsychiatry.2018.1941
Cornblath EJ, Tang E, Baum GL, et al., 2019. Sex differences in network controllability as a predictor of executive function in youth. NeuroImage, 188:122–134. https://doi.org/10.1016/j.neuroimage.2018.11.048
Doelling KB, Assaneo MF, 2021. Neural oscillations are a start toward understanding brain activity rather than the end. PLoS Biol, 19(5):e3001234. https://doi.org/10.1371/journal.pbio.3001234
Gu S, Pasqualetti F, Cieslak M, et al., 2015. Controllability of structural brain networks. Nat Commun, 6:8414. https://doi.org/10.1038/ncomms9414
Ioannides AA, 2018. Neurofeedback and the neural representation of self: lessons from awake state and sleep. Front Hum Neurosci, 12:142. https://doi.org/10.3389/fnhum.2018.00142
Kenett YN, Medaglia JD, Beaty RE, et al., 2018. Driving the brain towards creativity and intelligence: a network control theory analysis. Neuropsychologia, 118(Pt A):79–90. https://doi.org/10.1016/j.neuropsychologia.2018.01.001
Kinreich S, Podlipsky I, Jamshy S, et al., 2014. Neural dynamics necessary and sufficient for transition into pre-sleep induced by EEG NeuroFeedback. NeuroImage, 97:19–28. https://doi.org/10.1016/j.neuroimage.2014.04.044
Klimesch W, 2018. The frequency architecture of brain and brain body oscillations: an analysis. Eur J Neurosci, 48(7): 2431–2453. https://doi.org/10.1111/ejn.14192
Liao ZL, Tan YF, Qiu YJ, et al., 2018. Interhemispheric functional connectivity for Alzheimer’s disease and amnestic mild cognitive impairment based on the triple network model. J Zhejiang Univ-Sci B (Biomed & Biotechnol), 19(12):924–934. https://doi.org/10.1631/jzus.B1800381
Medaglia JD, Harvey DY, Kelkar AS, et al., 2021. Language tasks and the network control role of the left inferior frontal gyrus. eNeuro, 8(5):ENEURO.0382–20.2021. https://doi.org/10.1523/ENEURO.0382-20.2021
Parkes L, Moore TM, Calkins ME, et al., 2021. Network controllability in transmodal cortex predicts positive psychosis spectrum symptoms. Biol Psychiatry, 90(6):409–418. https://doi.org/10.1016/j.biopsych.2021.03.016
Sarasso S, D’Ambrosio S, Fecchio M, et al., 2020. Local sleeplike cortical reactivity in the awake brain after focal injury. Brain, 143(12):3672–3684. https://doi.org/10.1093/brain/awaa338
Tang BQ, Zhang WJ, Deng SK, et al., 2022. Age-associated network controllability changes in first episode drug-naïve schizophrenia. BMC Psychiatry, 22:26. https://doi.org/10.1186/s12888-021-03674-5
Wu Y, Zhao WR, Chen XY, et al., 2020. Aberrant awake spontaneous brain activity in obstructive sleep apnea: a review focused on resting-state EEG and resting-state fMRI. Front Neurol, 11:768. https://doi.org/10.3389/fneur.2020.00768
Xue SW, Lee TW, Guo YH, 2018. Spontaneous activity in medial orbitofrontal cortex correlates with trait anxiety in healthy male adults. J Zhejiang Univ-Sci B (Biomed & Biotechnol), 19(8):643–653. https://doi.org/10.1631/jzus.B1700481
Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 62207021), the Basic Research Program for the Natural Science of Shaanxi Province of China (No. 2022JM134), and the Start-up Foundation from Xi’an International Studies University (No. KYQDF202138). We acknowledge the Open iEEG Atlas Project from the Montreal Neurological Institute (MNI), Canada. We are grateful to all the participants in this study.
Author information
Authors and Affiliations
Contributions
Yan HE performed the data analysis and manuscript writing. Zhiqiang YAN performed the statistical analysis. Wenjia ZHANG contributed to the editing of the manuscript. Jie DONG contributed to the data analysis. Hao YAN contributed to the study design. All authors have read and approved the final manuscript, and therefore, have full access to all the data in the study and take responsibility for the integrity and security of the data.
Corresponding author
Ethics declarations
Yan HE, Zhiqiang YAN, Wenjia ZHANG, Jie DONG, and Hao YAN declare that they have no conflict of interest.
All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2013. Informed consent was obtained from all patients for being included in the study.
Additional information
Materials and methods
Detailed methods are provided in the electronic supplementary materials of this paper.
Supplementary information
Materials and methods; Tables S1-S3
Supplementary materials
Rights and permissions
About this article
Cite this article
He, Y., Yan, Z., Zhang, W. et al. Network controllability analysis of awake and asleep conditions in the brain. J. Zhejiang Univ. Sci. B 24, 458–462 (2023). https://doi.org/10.1631/jzus.B2200393
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/jzus.B2200393