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 (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.

Resting-state aperiodic neural activity as a novel objective marker of daytime somnolence

“…Mechanistically, the accumulation of sleep need is proposed to result from long-term potentiation (LTP) due to incidental learning and information processing during wakeful behaviour, resulting in saturation of synaptic strength in the cerebral cortex [13][14][15] . Data supporting this position are drawn from studies showing that: i) neural firing rates increase as a function of time spent awake, and then decrease over sleep 16 ; ii) molecular and electrophysiological markers of LTP are positively related to hours of wakefulness, while markers of global synaptic depression are linked with time spent in subsequent sleep 14 ; and, iii) using transcranial magnetic stimulation to directly measure neural firing responses, there is a noted increase in synaptic strength in both excitatory and inhibitory cortical connections in response to prolonged wakefulness 17 . Taken together, this body of work demonstrates that somnolence may result from plastic changes in the brain that accumulate over wakefulness.…”

Considerations towards a neurobiologically-informed EEG measurement of sleepiness

“…Subjective report data on experienced sleepiness tracked, but did not precisely match, these relationships. There is a substantial correlation between alpha power and the aperiodic slope, and between alpha and theta power [45] , and this may be of particular interest in the current context, given that alpha and theta power values have been seen as major markers of the accumulation of sleep need in the individual [50,51] . This shared explanatory power may suggest circadian effects in aperiodic networks.…”

Blink-related arousal network surges are shaped by cortical vigilance states

“…The HCP project collected information on participants' 'alertness', as part of the Mini Mental State Examination and the recent sleep pro le as part of the Pittsburgh Sleep Quality Index, but they did not collect self-reports of 'sleepiness' or 'alertness' before or during the scan (https://www.humanconnectome.org/storage/app/media/documentation/s1200/HCP_S1200_Release_Reference_Manual.pdf). Assessment of sleepiness could be quite informative since there are individual differences in responses to sleep deprivation, and concomitantly cortical excitability and reaction times will be affected by this variability (Chia et al, 2021). For instance, information about the amount and quality of sleep in the night prior to the fMRI scan might have been important to assess the in uences on the eye-closure durations, drowsiness, and cortical excitability.…”

“…In contrast for the Cerebellum greater peaks were observed in the Vigilant than the Drowsy state in Visual, Sensory Motor, Dorsal and Ventral Attention ROIs, though greater minimal values were observed for the Drowsy state in all ROIs.Pre-blink maximum and post-blink minimum and vigilance regression analysisUsing the values from the curves depicted in Figs. 1, 2 and 3, we extracted pre-blink maximum and post-blink minimum values at corresponding lags and computed the correlations between vigilance level (continuous eye closure ratio in y-axis, higher values longer closures) and the correlation values and time lags (x-axis, higher values more positive lags) obtained from the cross-correlation analysis reported across runs (Regression plots are presented in Supplementary Figs [9][10][11]…”