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Cortical excitability signatures for the degree of sleepiness in human.

ABSTRACT: 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 measurements in 38 subjects in a 34 hr program and decoded the relationship between cortical excitability and self-report sleepiness using advanced statistical methods. By utilizing a combination of partial least squares 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.

SUBMITTER: Chia CH

PROVIDER: S-EPMC8373378 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Cortical excitability signatures for the degree of sleepiness in human.

Chia Chin-Hsuan CH Tang Xin-Wei XW Cao Yue Y Cao Hua-Teng HT Zhang Wei W Wu Jun-Fa JF Zhu Yu-Lian YL Chen Ying Y Lin Yi Y Wu Yi Y Zhang Zhe Z Yuan Ti-Fei TF Hu Rui-Ping RP

eLife 20210727

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 measurements in 38 subjects in a 34 hr program and decoded the relationship between cortical excitability a ...[more]

PMID: 34313218

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Project description:BackgroundLittle is known about the modulation of cortical excitability in the prefrontal cortex during fear processing in humans. Here, we aimed to transiently modulate and test the cortical excitability during fear processing using transcranial magnetic stimulation (TMS) and brain oscillations in theta and alpha frequency bands with electroencephalography (EEG).MethodsWe conducted two separate experiments (no-TMS and TMS). In the no-TMS experiment, EEG recordings were performed during the instructed fear paradigm in which a visual cue (CS+) was paired with an aversive unconditioned stimulus (electric shock), while the other visual cue was unpaired (CS-). In the TMS experiment, in addition the TMS was applied on the right dorsomedial prefrontal cortex (dmPFC). The participants also underwent structural MRI (magnetic resonance imaging) scanning and were assigned pseudo-randomly to both experiments, such that age and gender were matched. The cortical excitability was evaluated by time-frequency analysis and functional connectivity with weighted phase lag index (WPLI). We further linked the excitability patterns with markers of stress coping capability.ResultsAfter visual cue onset, we found increased theta power in the frontal lobe and decreased alpha power in the occipital lobe during CS+ relative to CS- trials. TMS of dmPFC increased theta power in the frontal lobe and reduced alpha power in the occipital lobe during CS+. The TMS pulse increased the information flow from the sensorimotor region to the prefrontal and occipital regions in the theta and alpha bands, respectively during CS+ compared to CS-. Pre-stimulation frontal theta power (0.75-1 s) predicted the magnitude of frontal theta power changes after stimulation (1-1.25 s). Finally, the increased frontal theta power during CS+ compared to CS- was positively correlated with stress coping behavior.ConclusionOur results show that TMS over dmPFC transiently modulated the regional cortical excitability and the fronto-occipital information flows during fear processing, while the pre-stimulation frontal theta power determined the strength of achieved effects. The frontal theta power may serve as a biomarker for fear processing and stress-coping responses in individuals and could be clinically tested in mental disorders.

Project description:Recent work has shown that functional connectivity among cortical neurons is highly varied, with a small percentage of neurons having many more connections than others. Also, recent theoretical developments now make it possible to quantify how neurons modify information from the connections they receive. Therefore, it is now possible to investigate how information modification, or computation, depends on the number of connections a neuron receives (in-degree) or sends out (out-degree). To do this, we recorded the simultaneous spiking activity of hundreds of neurons in cortico-hippocampal slice cultures using a high-density 512-electrode array. This preparation and recording method combination produced large numbers of neurons recorded at temporal and spatial resolutions that are not currently available in any in vivo recording system. We utilized transfer entropy (a well-established method for detecting linear and nonlinear interactions in time series) and the partial information decomposition (a powerful, recently developed tool for dissecting multivariate information processing into distinct parts) to quantify computation between neurons where information flows converged. We found that computations did not occur equally in all neurons throughout the networks. Surprisingly, neurons that computed large amounts of information tended to receive connections from high out-degree neurons. However, the in-degree of a neuron was not related to the amount of information it computed. To gain insight into these findings, we developed a simple feedforward network model. We found that a degree-modified Hebbian wiring rule best reproduced the pattern of computation and degree correlation results seen in the real data. Interestingly, this rule also maximized signal propagation in the presence of network-wide correlations, suggesting a mechanism by which cortex could deal with common random background input. These are the first results to show that the extent to which a neuron modifies incoming information streams depends on its topological location in the surrounding functional network.

Dataset Information

Cortical excitability signatures for the degree of sleepiness in human.

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Cortical excitability signatures for the degree of sleepiness in human.

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