Sleep is essential in maintaining physiological homeostasis in the brain. While the underlying mechanism is not fully understood, a 'synaptic homeostasis' theory has been proposed that synapses continue to strengthen during awake, and undergo downscaling during sleep. This theory predicts that brain excitability increases with sleepiness. Here, we collected transcranial magnetic stimulation (TMS) measurements in 38 subjects in a 34-hour program, and decoded the relationship between cortical excitability and self-report sleepiness using advanced statistical methods. By utilizing a combination of partial least squares (PLS) regression and mixed-effect models, we identified a robust pattern of excitability changes, which can quantitatively predict the degree of sleepiness. Moreover, we found that synaptic strengthen occurred in both excitatory and inhibitory connections after sleep deprivation. In sum, our study provides supportive evidence for the synaptic homeostasis theory in human sleep and clarifies the process of synaptic strength modulation during sleepiness.
All data generated or analysed during this study are included in the manuscript and supporting files.
- Yi Wu
- Rui-Ping Hu
- Yu-Lian Zhu
- Yi Wu
- Yi Wu
- Zhe Zhang
- Zhe Zhang
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Human subjects: 1. That informed consent, and consent to publish, was obtained2. This study was designed as a prospective self-controlled study. The Ethics Committee of Huashan Hospital approved the study (2017-410) and was registered on the Chinese Clinical Trial Registry (ChiCTR1800016771).
- Laura Dugué, Université de Paris, France
© 2021, Chia et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Complex behaviors depend on the coordinated activity of neural ensembles in interconnected brain areas. The behavioral function of such coordination, often measured as co-fluctuations in neural activity across areas, is poorly understood. One hypothesis is that rapidly varying co-fluctuations may be a signature of moment-by-moment task-relevant influences of one area on another. We tested this possibility for error-corrective adaptation of birdsong, a form of motor learning which has been hypothesized to depend on the top-down influence of a higher-order area, LMAN (lateral magnocellular nucleus of the anterior nidopallium), in shaping moment-by-moment output from a primary motor area, RA (robust nucleus of the arcopallium). In paired recordings of LMAN and RA in singing birds, we discovered a neural signature of a top-down influence of LMAN on RA, quantified as an LMAN-leading co-fluctuation in activity between these areas. During learning, this co-fluctuation strengthened in a premotor temporal window linked to the specific movement, sequential context, and acoustic modification associated with learning. Moreover, transient perturbation of LMAN activity specifically within this premotor window caused rapid occlusion of pitch modifications, consistent with LMAN conveying a temporally localized motor-biasing signal. Combined, our results reveal a dynamic top-down influence of LMAN on RA that varies on the rapid timescale of individual movements and is flexibly linked to contexts associated with learning. This finding indicates that inter-area co-fluctuations can be a signature of dynamic top-down influences that support complex behavior and its adaptation.
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