SARS-CoV-2 Slows Brain Rhythms with more Severe Effects in Younger Individuals

Coronavirus disease secondary to infection by SARS-CoV-2 (COVID19 or C19) causes respiratory illness, as well as severe neurological symptoms that have not been fully characterized. In a previous study, we developed a computational pipeline for the automated, rapid, high-throughput and objective analysis of brain encephalography (EEG) rhythms. In this retrospective study, we used this pipeline to define the quantitative EEG changes in patients with a PCR-positive diagnosis of C19 (n=31) in the intensive care unit (ICU) of Cleveland Clinic, compared to a group of age-matched PCR-negative (n=38) control patients in the same ICU setting. Qualitative assessment of EEG by two independent teams of electroencephalographers confirmed prior reports with regards to the high prevalence of diffuse encephalopathy in C19 patients, although the diagnosis of encephalopathy was inconsistent between teams. Quantitative analysis of EEG showed distinct slowing of brain rhythms in C19 patients compared to control (enhanced delta power and attenuated alpha-beta power). Surprisingly, these C19-related changes in EEG power were more prominent in patients below age 70. Moreover, machine learning algorithms showed consistently higher accuracy in the binary classification of patients as C19 versus control using EEG power for subjects below age 70 compared to older ones, providing further evidence for the more severe impact of SARS-CoV-2 on brain rhythms in younger individuals irrespective of PCR diagnosis or symptomatology, and raising concerns over potential long-term effects of C19 on brain physiology in the adult population and the utility of EEG monitoring in C19 patients.

Activation of the Brain During Motor Imagination Task with Auditory Stimulation

Background: Auditory feedback is one of the most important feedback in cognitive process. It plays an important guiding role in cognitive motor process. However, previous studies on auditory stimuli mainly focused on the cognitive effects of auditory stimuli on cortex, while the role of auditory feedback stimuli in motor imagery tasks is still unclear.Methods: 18 healthy subjects were recruited to complete the motor imagination task stimulated by meaningful words and meaningless words. In order to explore the role of auditory stimuli in motor imagination tasks, we studied EEG power spectrum, frontal parietal mismatch negativity (MMN) and inter test phase-locked consistency (ITPC). one-way Analysis of Variance (ANOVA) and Least Significant Difference (LSD) correction were used to test the differences between the two experimental groups and the differences of different bands in each experimental group.Results: EEG power spectrum analysis showed that the activity of contralateral motor cortex was significantly increased under the stimulation of meaningful words, and the amplitude of mismatch negative wave was also significantly increased. ITPC is mainly concentrated in μ, α and γ bands in the process of motor imagery task guided by the auditory stimulus of meaningful words, while it is mainly concentrated in the β band under the meaningless words stimulation.Conclusions: This results may be due to the influence of auditory cognitive process on motor imagery. We speculate that there may be a more complex mechanism for the effect of auditory stimulation on the inter test phase lock consistency. When the stimulus sound has the corresponding meaning to the motor action, the parietal motor cortex may be more affected by the prefrontal cognitive cortex, thus changing its normal response mode. This mode change is caused by the joint action of motor imagination, cognitive and auditory stimuli. This study provides a new insight into the neural mechanism of motor imagery task guided by auditory stimuli, and provides more information on the activity characteristics of the brain network in motor imagery task by cognitive auditory feedback.

Monitoring Brain State and Behavioral Performance during Repetitive Visual Stimulation

We tested whether changes in prestimulus neural activity predict behavioral performance (decision time and errors) during a prolonged visual task. The task was to classify ambiguous stimuli—Necker cubes; manipulating the degree of ambiguity from low ambiguity (LA) to high ambiguity (HA) changed the task difficulty. First, we assumed that the observer’s state changes over time, which leads to a change in the prestimulus brain activity. Second, we supposed that the prestimulus state produces a different effect on behavioral performance depending on the task demands. Monitoring behavioral responses, we revealed that the observer’s decision time decreased for both LA and HA stimuli during the task performance. The number of perceptual errors lowered for HA, but not for LA stimuli. EEG analysis revealed an increase in the prestimulus 9–11 Hz EEG power with task time. Finally, we found associations between the behavioral and neural estimates. The prestimulus EEG power negatively correlated with the decision time for LA stimuli and the erroneous responses rate for HA stimuli. The obtained results confirm that monitoring prestimulus EEG power enables predicting perceptual performance on the behavioral level. The observed different time-on-task effects on the LA and HA stimuli processing may shed light on the features of ambiguous perception.

Altered electroencephalography resting state network coherence in remitted MDD

Individuals with remitted depression are at greater risk for subsequent depression and therefore may provide a unique opportunity to understand the neurophysiological correlates underlying the risk of depression. Research has identified abnormal resting-state electroencephalography (EEG) power metrics and functional connectivity patterns associated with major depression, however little is known about these neural signatures in individuals with remitted depression. We investigate the spectral dynamics of 64-channel EEG surface power and source-estimated network connectivity during resting states in 37 individuals with depression, 56 with remitted depression, and 49 healthy adults that did not differ on age, education, and cognitive ability across theta, alpha, and beta frequencies. Average reference spectral EEG surface power analyses identified greater left and midfrontal theta in remitted depression compared to healthy adults. Using Network Based Statistics, we also demonstrate within and between network alterations in LORETA transformed EEG source-space coherence across the default mode, fronto-parietal, and salience networks where individuals with remitted depression exhibited enhanced coherence compared to those with depression, and healthy adults. This work builds upon our currently limited understanding of resting EEG connectivity in depression, and helps bridge the gap between aberrant EEG power and brain network connectivity dynamics in this disorder. Further, our unique examination of remitted depression relative to both healthy and depressed adults may be key to identifying brain-based biomarkers for those at high risk for future, or subsequent depression.

Oscillatory population-level activity of dorsal raphe serotonergic neurons sculpts sleep structure

Dorsal raphe (DR) 5HT neurons are involved in regulating sleep/wake transitions. Previous studies demonstrated that single unit activity of DR 5HT neurons is high during wakefulness, decreases during non rapid eye movement (NREM) sleep, and ceases during rapid eye movement (REM) sleep. However, characteristics of the population level activity of DR 5HT neurons, which can influence the entire brain, are largely unknown. Here we measured population activities of 5 HT neurons in male and female mouse DR across the sleep/wake cycle by a ratiometric fiber photometry system. We found a slow oscillatory activity of compound intracellular Ca2+ signals during NREM sleep. The trough of concave 5HT activity increased along with sleep progression, but the 5HT activity level always returned to that seen in wake periods. When the trough reached the minimum level and remained there, REM sleep initiated. We also found a unique coupling of the oscillatory 5HT activity and EEG power fluctuation, suggesting that EEG fluctuation is a proxy for 5HT activity. Optogenetic activation of 5HT neurons during NREM sleep triggered a high EMG power and induced wakefulness. Optogenetic inhibition induced REM sleep or sustained NREM with an EEG power increase and EEG fluctuation. These manipulations demonstrated a causal role of DR 5HT neurons in sculpting sleep/wake structure. We also observed EEG fluctuations in human males during NREM sleep, implicating the existence of 5HT oscillatory activity in humans. We propose that NREM sleep is not a monotonous state, but that it is dynamically regulated by the oscillatory population activity of DR 5HT neurons.

Preliminary Study on Changes of Sleep EEG Power and Plasma Melatonin in Male Patients With Major Depressive Disorder After 8 Weeks Treatment

Objective: To clarify the effects of escitalopram on sleep EEG power in patients with Major depressive disorder (MDD).Method: Polysomnography (PSG) was detected overnight, and blood samples were collected at 4 h intervals over 24 h from 13 male healthy controls and 13 male MDD patients before and after treatment with escitalopram for 8 weeks. The outcome measures included plasma melatonin levels, sleep architecture, and the sleep EEG power ratio.Results: Compared with healthy controls, MDD patients presented abnormalities in the diurnal rhythm of melatonin secretion, including peak phase delayed 3 h and a decrease in plasma melatonin levels at night and an increase at daytime, accompanied by sleep disturbances, a decrease in low-frequency bands and an increase in high-frequency bands, and the dominant right-side brain activity. Several of these abnormalities (abnormalities in the diurnal rhythm of melatonin secretion, partial sleep architecture parameters) persisted for at least the 8-week testing period.Conclusions: Eight weeks of treatment with escitalopram significantly improved subjective sleep perception and depressive symptoms of patients with MDD, and partially improved objective sleep parameters, while the improvement of circadian rhythm of melatonin was limited.

Sources of variation in the spectral slope of the sleep EEG

Building on previous work linking changes in the electroencephalogram (EEG) spectral slope to arousal level, Lendner et al. (2021) reported that wake, non rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep exhibit progressively steeper 30-45 Hz slopes, interpreted in terms of increasing cortical inhibition. Here we sought to replicate Lendner et al.'s scalp EEG findings (based on 20 individuals) in a larger sample of 11,630 individuals from multiple cohorts in the National Sleep Research Resource (NSRR). In a final analytic sample of N = 10,255 distinct recordings, there was unambiguous statistical support for the hypothesis that, within individuals, the mean spectral slope grows steeper going from wake to NREM to REM sleep. We found that the choice of mastoid referencing scheme modulated the extent to which electromyogenic or electrocardiographic artifacts were likely to bias 30-45 Hz slope estimates, as well as other sources of technical, device-specific bias. Nonetheless, within individuals, slope estimates were relatively stable over time. Both cross-sectionally and longitudinal, slopes tended to become shallower with increasing age, particularly for REM sleep; males tended to show flatter slopes than females across all states. Although conceptually distinct, spectral slope did not predict sleep state substantially better than other summaries of the high frequency EEG power spectrum (>20 Hz, in this context) including beta band power, however. Finally, to more fully describe sources of variation in the spectral slope and its relationship to other sleep parameters, we quantified state-dependent differences in the variances (both within and between individuals) of spectral slope, power and interhemispheric coherence, as well as their covariances. In contrast to the common conception of the REM EEG as relatively wake-like (i.e. 'paradoxical' sleep), REM and wake were the most divergent states for multiple metrics, with NREM exhibiting intermediate profiles. Under a simplified modelling framework, changes in spectral slope could not, by themselves, fully account for the observed differences between states, if assuming a strict power law model. Although the spectral slope is an appealing, theoretically inspired parameterization of the sleep EEG, here we underscore some practical considerations that should be borne in mind when applying it in diverse datasets. Future work will be needed to fully characterize state-dependent changes in the aperiodic portions of the EEG power spectra, which appear to be consistent with, albeit not fully explained by, changes in the spectral slope.