Biology subjects

Simor, P.

Publications and source records attributed to Simor, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Theta-band brain synchronization supports the immediate and post-sleep dynamics of memory recall in children

Memory engrams emerge from dynamic, coordinated interactions among synchronized functional brain networks. Yet, how these networks are selectively reactivated upon cued recall and gradually (re)organized over time, especially through sleep, remains poorly understood. Using magnetoencephalography (MEG), sleep electroencephalography (EEG) and behavioral measures, we investigated the spatiotemporal neural dynamics of cued recall memory during immediate and post-sleep (i.e., 90-minute post-learning nap) recall sessions in school-aged children. Results showed that immediate recall engaged a temporally ordered sequence of theta-band phase synchronization across two transiently synchronized networks: an early (150-350 ms) network involving the ventral visual pathway and left medial temporal lobe (MTL), followed by a later (550-750 ms) network encompassing the bilateral MTL and widespread neocortical associative regions. Post-sleep recall was associated, relative to wakefulness, with strengthened theta-band phase synchronization between the left MTL and widespread bilateral neocortical regions in a similar late window (450-650 ms). Post-sleep theta-band synchronization and memory gains in performance positively correlated with slow oscillation-spindle coupling during the post-learning nap. Altogether, these findings highlight oscillatory and spatiotemporal dynamics of memory recall networks and suggest that sleep, possibly driven by slow-oscillation-spindle coupling mechanisms, supports the efficient reinstatement of memory traces in the developing brain.

neuroscience↗

bioRxiv2026

Disentangling the Relationship Between Mind Wandering and Symptom Dimensions in a Non-Clinical Sample: ADHD as the Primary Driver

Introduction: Mind wandering has been linked to a wide range of psychiatric conditions, yet most studies have examined these associations in isolation. Given the substantial comorbidity across the psychopathological spectrum, it remains unclear whether specific symptom dimensions uniquely and independently predict mind wandering once their overlap is accounted for. Methods: To address this, we adopted a dimensional approach in a non-clinical sample (N = 384), simultaneously modeling seven symptom dimensions: ADHD, depression, obsessive-compulsive tendencies, schizotypy, autistic traits, hypomania, and eating disorder symptoms. Results: At the bivariate level, mind wandering correlated positively with all symptom dimensions. However, when the substantial shared variance across dimensions was accounted for in both frequentist and Bayesian multivariate regression models, only ADHD symptoms consistently emerged as a unique predictor ({beta} = 0.52; BF > 1000). Hypomania symptoms showed a weak, inconsistent positive association across analyses. All remaining predictors yielded negligible unique contributions, with Bayes factors supporting the null hypothesis. Conclusion: These findings suggest that once overlap between symptom dimensions is accounted for, ADHD symptoms remain the only consistent predictor of trait-level mind wandering. We speculate that previously reported associations between mind wandering and other psychopathological symptom dimensions may largely reflect variance shared across the symptom battery rather than dimension-specific mechanisms.

neuroscience↗

bioRxiv2025

A functional trade-off between executive control and implicit statistical learning is dynamically gated by mind wandering

Human cognition must balance goal-directed behavior with the need to learn from environmental regularities. Mind wandering (MW), a state of attentional decoupling from the task at hand, is paradoxically associated with both executive failures and enhanced implicit statistical learning, yet the direct relationship between these phenomena remains unclear. Here, we provide direct behavioral evidence for a functional trade-off between these competing demands. Using a task that concurrently measured response inhibition, statistical learning, and self-reported task focus, we show that MW is associated with impaired inhibitory control but enhanced learning of probabilistic sequences. Critically, we reveal that these effects are related: the magnitude of the learning enhancement during MW depends on the efficacy of response inhibition. These findings demonstrate that transient lapses in top-down executive control are associated with the enhanced implicit extraction of environmental statistics, supporting neurocompetition models, and framing MW as a cognitive state that may be evolutionarily preserved to promote the unsupervised acquisition of predictive models.

neuroscience↗

bioRxiv2024

Slow-wave sleep is associated with nucleus accumbens volume in elderly adults

Slow-wave sleep (SWS) is essential for restorative neural processes, and its decline is associated with both healthy and pathological ageing. Building on previous rodent research, this longitudinal study identified a significant association between nucleus accumbens (NAcc) volume and SWS duration in cognitively unimpaired older adults. Our findings support the involvement of the NAcc in ageing-related modulation of SWS and suggest potential therapeutic targets for improving SWS.

neuroscience↗

bioRxiv2024

Heartbeat related activity in the anterior thalamus differs between phasic and tonic REM sleep periods

Rapid eye movement (REM) sleep is a fundamental sleep state associated with diverse functions from elemental physiological processes to higher order neurocognitive functions. A growing body of research indicates that REM sleep with eye movements (phasic REM) differs from REM periods without ocular activity (tonic) in terms of spontaneous and evoked neural responses. Studies using auditory stimulation consistently observed enhanced evoked responses in tonic versus phasic REM, indicating that the external processing is largely diminished when the eyes move during REM sleep. Whereas exteroceptive processing during sleep is widely studies, investigation on interoception (the processing of bodily signals) during sleep is scarce, and limited to scalp electroencephalographic recordings. Here we studied interoceptive processing in a group of epileptic patients (N = 11) by measuring their heartbeat-related neural activity in the anterior nucleus of the thalamus (ANT) during phasic and tonic REM sleep and resting wakefulness. Evoked potentials and beta-low gamma spectral power locked to the heartbeat were significantly different in phasic REM compared to tonic REM and wakefulness. Heartbeat-related neural signals exhibited pronounced inter-trial phase synchronization at lower (7-20 Hz) oscillatory activity in all vigilance states, but reduced gamma synchronization at later time points in phasic REM only. Tonic REM and wakefulness did not show significant differences in heartbeat-related activity in the ANT. Our findings indicate that heartbeat-related neural activity is detectable at the level of the ANT, showing distinct signatures of interoceptive processing in phasic REM compared to tonic REM and wakefulness.

neuroscience↗

bioRxiv2024

Aperiodic neural activity distinguishes between phasic and tonic REM sleep

IntroductionTraditionally categorized as a uniform sleep phase, rapid eye movement (REM) sleep exhibits substantial heterogeneity with its phasic and tonic constituents showing marked differences regarding neuronal network activity, environmental alertness and information processing. Here, we investigate how tonic and phasic states differ with respect to aperiodic neural activity, a marker of arousal levels, sleep stages, depth of sleep and sleep intensity. We also attempt to challenge the binary categorisation of REM sleep states by introducing graduality into their definition. Specifically, we quantify the intensity of phasic oculomotor events and investigate their temporal relationships with aperiodic activity. MethodWe analyzed 57 polysomnographic recordings from three open-access datasets of healthy young volunteers aged 21.7{+/-}1.4 years. REM sleep heterogeneity was assessed using either binary phasic-tonic categorization or quantification of eye movement (EM) amplitudes detected by electrooculography with the YASA algorithm. Slopes of the aperiodic power component measured by electroencephalography in the low (2-30Hz) and high (30-48Hz) frequency bands were calculated using the Irregularly Resampled Auto-Spectral Analysis. For statistical analyses, we used ANOVA, Spearman correlations and cross-correlations. ResultsThe binary approach revealed that the phasic state is characterized by steeper low-band aperiodic slopes compared to the tonic state with the strongest effect observed over the frontal area. The phasic state also showed flatter high-band slopes with the strongest effect over central and parietal areas. The gradual approach confirmed this result further showing that higher EM amplitudes are linked to steeper low-band and flatter high-band aperiodic slopes. The temporal analysis within REM episodes revealed that aperiodic activity preceding or following EM events did not cross-correlate with EM amplitudes. ConclusionThis study demonstrates that aperiodic slopes can serve as a reliable objective marker able to differentiate between phasic and tonic constituents of REM sleep and reflect the intensity of phasic oculomotor events for instantaneous measurements. However, EM events could not be predicted by preceding aperiodic activity and vice versa, at least not with scalp electroencephalography.

neuroscience↗

bioRxiv2024

MIND WANDERING DURING IMPLICIT LEARNING IS ASSOCIATED WITH INCREASED PERIODIC EEG ACTIVITY AND IMPROVED EXTRACTION OF HIDDEN PROBABILISTIC PATTERNS

Mind wandering, occupying 30-50% of our waking time, remains an enigmatic phenomenon in cognitive neuroscience. Predominantly viewed negatively, mind wandering is often associated with detrimental impacts on attention-demanding (model-based) tasks in both natural settings and laboratory conditions. Mind wandering however, might not be detrimental for all cognitive domains. We proposed that mind wandering may facilitate model-free processes, such as probabilistic learning, which relies on the automatic acquisition of statistical regularities with minimal attentional demands. We administered a well-established implicit probabilistic learning task combined with mind wandering thought probes in healthy adults (N = 37, 30 females). To explore the neural correlates of mind wandering and probabilistic learning, participants were fitted with high-density electroencephalography. Our findings indicate that probabilistic learning was not only immune to periods of mind wandering, but was positively associated with it. Spontaneous, as opposed to deliberate mind wandering, was particularly beneficial for extracting the probabilistic patterns hidden in the visual stream. Additionally, cortical oscillatory activity in the low-frequency (slow and delta) range, indicative of covert sleep-like states, was associated with both mind wandering and improved probabilistic learning, particularly in the early stages of the task. Given the importance of probabilistic implicit learning in predictive processing, our findings provide novel insights into the potential cognitive benefits of task-unrelated thoughts in addition to shedding light on its neural mechanisms. This surprising benefit challenges the predominant view of mind wandering as solely detrimental and highlights its complex role in human cognition, especially in memory consolidation. Statement of significanceMind wandering poses an unresolved puzzle for cognitive neuroscience: it is associated with poor performance in various cognitive domains, yet humans spend 30-50% of their waking time mind wandering. We proposed that mind wandering may be beneficial for less attention-demanding cognitive processes requiring automatic, habitual learning. We assessed an implicit probabilistic learning task measuring the ability to extract (without awareness) hidden regularities from the information stream. Participants showed superior performance in probabilistic learning during periods of mind wandering, especially when such task-unrelated thoughts occurred spontaneously without intention. Moreover, mind wandering and probabilistic learning were both associated with slow frequency neural activity, suggesting that mind wandering may reflect a transient, offline state facilitating rapid learning and memory consolidation.

neuroscience↗

bioRxiv2024

Sleep Fragmentation Modulates the Neurophysiological Correlates of Cognitive Fatigue

Cognitive fatigue (CF) is a critical factor affecting performance and well-being. It can be altered in suboptimal sleep quality conditions, e.g., in patients suffering from obstructive sleep apnea who experience both intermittent hypoxia and sleep fragmentation (SF). Understanding the neurophysiological basis of SF in healthy individuals can provide insights to improve cognitive functioning in disrupted sleep conditions. In this electroencephalographical (EEG) study, we investigated in 16 healthy young participants the impact of experimentally induced SF on the neurophysiological correlates of CF measured before, during, and after practice on the TloadDback, a working memory task tailored to each individuals maximal cognitive resources. Participants spent two times three consecutive nights in the laboratory, once in an undisrupted sleep (UdS) condition and once in a SF condition induced by non-awakening auditory stimulations, counterbalanced, and performed the TloadDback task both in a high (HCL) and a low (LCL) cognitive load condition. EEG activity was recorded during wakefulness in the 5-minutes resting state immediately before and after, as well as during the 16-minutes of the TloadDback task practice. In the high cognitive load under sleep fragmentation (HCL/SF) condition, high beta power increased during the TloadDback indicating heightened cognitive effort, and beta and alpha power increased in the post- vs. pre task resting state, suggesting a relaxation rebound. In the low cognitive load/undisturbed sleep (LCL/UdS) condition, low beta activity increased suggesting a relaxed focus, as well as mid beta activity associated with active thinking. These findings highlight the dynamic impact of SF on the neurophysiological correlates of CF and underscore the importance of sleep quality and continuity to maintain optimal cognitive functioning.

neuroscience↗

bioRxiv2023

Multi-night EEG reveals positive association between sleep efficiency and hippocampal subfield volumes in healthy aging

Age-related atrophy of the human hippocampus and the enthorinal cortex starts accelerating at around age 60. Due to the contributions of these regions to many cognitive functions seamlessly used in everyday life, this can heavily impact the lives of elderly people. The hippocampus is not a unitary structure and mechanisms of its age-related decline appear to differentially affect its subfields. Human and animal studies have suggested that altered sleep is associated with hippocampal atrophy. Yet, we know little about subfield specific effects of altered sleep in healthy aging and their effect on cognition. Here, in a sample of 118 older adults (Mage = 63.25 years), we examined the association between highly reliable hippocampal subfield volumetry, sleep measures derived from multi-night recordings of portable electroencephalography and episodic memory. Objective sleep efficiency - but not self-report measures of sleep - was associated with entorhinal cortex volume when controlling for age. Age-related differences in subfield volumes were associated with objective sleep efficiency, but not with self-report measures of sleep. Moreover, older adults characterized by a common multivariate pattern of subfield volumes that contributed to positive sleep- subfield volume associations, showed lower rates of forgetting. Our results showcase the benefit of objective sleep measures in identifying potential contributors of age-related differences in brain-behavior couplings.

neuroscience↗

bioRxiv2023

A complementary contribution of REM sleep after targeted memory reactivation during NREM sleep for memory consolidation

Presentation of learning-related cues during NREM sleep has been shown to improve memory consolidation. Past studies suggest that REM sleep may contribute to the beneficial effect of reactivating memories during NREM sleep, but the relationship between REM sleep and induced reactivations in NREM remains unclear. We investigated whether a naturally ensuing episode of REM sleep is necessary for prior NREM targeted memory reactivation (TMR) to exert a beneficial effect on memory consolidation. Nineteen participants learned the association between prior or non-prior known objects and their names (pseudowords) in a within-subject multiple session experiment, in which TMR was subsequently performed either before (Pre-REM) or after (Post-REM) the final REM sleep episode of the night. While word-picture association recall measures did not differ between TMR conditions, we found better name recognition based on confidence ratings for words reactivated during Pre-REM TMR in contrast with associations cued during Post-REM TMR. In addition, we found distinct associations between cue-evoked sigma activity, subsequent REM theta power and TMR memory benefits which were contingent upon the level of relatedness with prior knowledge for the learned material. Although TMR may be less effective during the second half of the night, our findings suggest an interplay between NREM and REM sleep oscillatory activity for memory reactivation and consolidation processes.

neuroscience↗

bioRxiv2023

Multivariate prediction of cognitive performance from the sleep electroencephalogram

Human cognitive performance is a key function whose biological foundations have been partially revealed by genetic and brain imaging studies. The sleep electroencephalogram (EEG) is tightly linked to structural and functional features of the central nervous system and serves as another promising biomarker. We used data from MrOS, a large cohort of older men and cross- validated regularized regression to link sleep EEG features to cognitive performance in cross- sectional analyses. In independent validation samples 2.5-10% of variance in cognitive performance can be accounted for by sleep EEG features, depending on the covariates used. Demographic characteristics accounted for more covariance between sleep EEG and cognition than health variables, and consequently reduced this association by a greater degree, but even with the strictest covariate sets a statistically significant association was present. Sigma power in NREM and beta power in REM sleep were associated with better cognitive performance, while theta power in REM sleep was associated with worse performance, with no substantial effect of coherence and other sleep EEG metrics. Our findings show that cognitive performance is associated with the sleep EEG (r=0.283), with the strongest effect ascribed to spindle- frequency activity. This association becomes weaker after adjusting for demographic (r=0.186) and health variables (r=0.155), but its resilience to covariate inclusion suggest that it also partially reflects trait-like differences in cognitive ability.

neuroscience↗

bioRxiv2022

Scale-free and oscillatory spectral measures of sleep stages in humans

Power spectra of sleep electroencephalograms (EEG) comprise two main components: a decaying power-law corresponding to the aperiodic neural background activity, and spectral peaks present due to neural oscillations.Traditional band-based spectral methods ignore this fundamental structure of the EEG spectra and thus are susceptible to misrepresenting the underlying phenomena. A fitting method that attempts to separate and parameterize the aperiodic and periodic spectral components called fitting oscillations & one over f (FOOOF) was applied to a set of annotated whole-night sleep EEG recordings of 251 subjects from a wide age range (4-69 years). Most of the extracted parameters exhibited sleep stage sensitivity; significant main effects and interactions of sleep stage, age, sex, and brain region were found. The spectral slope (describing the steepness of the aperiodic component) showed especially large and consistent variability between sleep stages (and low variability between subjects), making it a candidate indicator of sleep states. The limitations and arisen problems of the FOOOF method are also discussed, possible solutions for some of them are suggested.

bioinformatics↗

bioRxiv2021

The sleep EEG envelope: a novel, neuronal firing-based human biomarker

Sleep EEG reflects instantaneous voltage differences relative to a reference, while its spectrum reflects the degree to which it is comprised of oscillations at various frequencies. In contrast, the envelope of the sleep EEG reflects the instantaneous amplitude of oscillations at specific frequencies, and its spectrum reflects the rhythmicity of the occurrence of these oscillations. The ordinary sleep EEG and its spectrum have been extensively studied and its individual stability and relationship to various demographic characteristics, psychological traits and pathologies is well known. In contrast, the envelope spectrum has not been extensively studied before. In two studies, we explored the generating mechanisms and utility of studying the envelope of the sleep EEG. First, we used human invasive data from cortex-penetrating microelectrodes and subdural grids to demonstrate that the sleep EEG envelope spectrum reflects local neuronal firing. Second, we used a large database of healthy volunteers to demonstrate that the scalp EEG envelope spectrum is highly stable within individuals, especially in NREM sleep, and that it is affected by age and sex. Multivariate models based on a learning algorithm could predict both age (r=0.6) and sex (r=0.5) with considerable accuracy from the EEG envelope spectrum. With age, oscillations characteristically shifted from a 4-5 second rhythm to higher rhythms. The envelope spectrum was not associated with general cognitive ability (IQ). Our results demonstrate that the sleep envelope spectrum is a promising, neuronal firing-based biomarker of various demographic and disease-related phenotypes.

neuroscience↗

bioRxiv2021

Sleep spindles, ripples, and interictal epileptiform discharges in the human anterior and mediodorsal thalamus

Sleep spindles are major oscillatory components of Non-Rapid Eye Movement (NREM) sleep, reflecting hyperpolarization-rebound sequences of thalamocortical neurons, the inhibition of which is caused by the NREM-dependent activation of GABAergic neurons in the reticular thalamic nucleus. Reports suggest a link between sleep spindles and several forms of interictal epileptic discharges (IEDs) which are considered as expressions of pathological off-line neural plasticity in the central nervous system. Here we investigated the relationship between thalamic sleep spindles, IEDs and ripples in the anterior and mediodorsal nuclei (ANT and MD) of epilepsy patients. Whole-night LFP from the ANT and MD were co-registered with scalp EEG/polysomnography by using externalized leads in 15 epilepsy patients undergoing Deep Brain Stimulation protocol. Slow ([~]12 Hz) and fast ([~]14 Hz) sleep spindles were present in the human ANT and MD. Roughly, one third of thalamic sleep spindles were associated with IEDs or ripples. Both IED- and ripple-associated spindles were longer than pure spindles. IED-associated thalamic sleep spindles were characterized by broadband increase in thalamic and cortical activity, both below and above the spindle frequency range, whereas ripple-associated thalamic spindles exceeded pure spindles in terms of 80-200 Hz thalamic, but not cortical activity as indicated by time-frequency analysis. These result show that thalamic spindles coupled with IEDs are reflected at the scalp slow and beta-gamma oscillation as well. IED density during sleep spindles in the MD, but not in the ANT was identified as correlates of years spent with epilepsy, whereas no signs of pathological processes were correlated with measures of ripple and spindle association. Furthermore, the density of ripple-associated sleep spindles in the ANT showed a positive correlation with general intelligence. Our findings indicate the complex and multifaceted role of the human thalamus in sleep spindle-related physiological and pathological neural plasticity.

neuroscience↗

bioRxiv2021

Circadian modulation of human sleep spindle frequency

Homeostatic and circadian processes play a pivotal role in determining sleep structure, timing and quality. In sharp contrast with the wide accessibility of the EEG index of sleep homeostasis, an electrophysological measure of the circadian modulation of sleep is still non-available. Evidence suggests that sleep spindle frequencies decelerate during biological night. In order to test the feasibility of measuring this marker in common polysomnographic protocols, the Budapest-Munich database of sleep records (N = 251 healthy subjects, 122 females, age range: 4-69 years), as well as an afternoon nap sleep record database (N = 112 healthy subjects, 30 females, age range: 18-30 years) were analysed by the Individual Adjustment Method of sleep spindle analysis. Slow and fast sleep spindle frequencies were characterized by U-shaped overnight dynamics, with highest values in the first and the fourth-to-fifth sleep cycle and the lowest values in the middle of the sleeping period (cycles 2-3). Age-related attenuation of sleep spindle deceleration was evident. Estimated phases of the nadirs in sleep spindle frequencies were advanced in children as compared to other age groups. Additionally, nap sleep spindles were faster than night sleep spindles (0.57 and 0.39 Hz difference for slow and fast types, respectively). The fine frequency resolution analysis of sleep spindles is a feasible method of measuring the assumed circadian modulation of sleep. Moreover, age-related attenuation of circadian sleep modulation might be measurable by assessing the overnight dynamics in sleep spindle frequency. Phase of the minimal sleep spindle frequency is a putative biomarker of chronotype.

neuroscience↗

bioRxiv2020

Similarity of brain activity patterns during learning and subsequent resting state predicts memory consolidation

Long-term memory depends on memory consolidation that seems to rely on learning-induced changes in the brain activity. Here, we introduced a novel approach analyzing continuous EEG data to study learning-induced changes as well as trait-like characteristics in brain activity underlying consolidation. Thirty-one healthy young adults performed a learning task and their performance was retested after a short (~1h) delay, that enabled us to investigate the consolidation of serial-order and probability information simultaneously. EEG was recorded during a pre- and post-learning rest period and during learning. To investigate the brain activity associated with consolidation performance, we quantified similarities in EEG functional connectivity of learning and pre-learning rest (baseline similarity) as well as learning and post-learning rest (post-learning similarity). While comparable patterns of these two could indicate trait-like similarities, changes in similarity from baseline to post-learning could indicate learning-induced changes, possibly spontaneous reactivation. Individuals with higher learning-induced changes in alpha frequency connectivity (8.5-9.5 Hz) showed better consolidation of serial-order information. This effect was stronger for more distant channels, highlighting the role of long-range centro-parietal networks underlying the consolidation of serial-order information. The consolidation of probability information was associated with learning-induced changes in delta frequency connectivity (2.5-3 Hz) and seemed to be dependent on more local, short-range connections. Beyond these associations with learning-induced changes, we also found substantial overlap between the baseline and post-learning similarity and their associations with consolidation performance, indicating that stable (trait-like) differences in functional connectivity networks may also be crucial for memory consolidation. Significance statementWe studied memory consolidation in humans by characterizing how similarity in neural oscillatory patterns during learning and rest periods supports consolidation. Previous studies on similarity focused on learning-induced changes (including reactivation) and neglected the stable individual characteristics that are present over resting periods and learning. Moreover, learning-induced changes are predominantly studied invasively in rodents or with neuroimaging or event-related electrophysiology techniques in humans. Here, we introduced a novel approach that enabled us 1) to reveal both learning-induced changes and trait-like individual differences in brain activity and 2) to study learning-induced changes in humans by analyzing continuous EEG. We investigated the consolidation of two types of information and revealed distinct learning-induced changes and trait-like characteristics underlying the different memory processes.

neuroscience↗

bioRxiv2020

A new set of composite, non-redundant electroencephalogram measures of non-rapid eye movement sleep based on the power law scaling of the Fourier spectrum

A novel method for deriving composite, non-redundant measures of non-rapid eye movement (NREM) sleep electroencephalogram (EEG) is developed on the basis of the power law scaling of the Fourier spectra. Measures derived are the spectral intercept, the slope (spectral exponent), as well as the maximal whitened spectral peak amplitude and frequency in the sleep spindle range. As a proof of concept, we apply these measures on a large sleep EEG dataset (N = 175; 81 females; age range: 17-60 years) with previously demonstrated effects of age, sex and intelligence. As predicted, aging is associated with decreased overall spectral slopes (increased exponents) and whitened spectral peak amplitudes in the spindle frequency range. In addition, age associates with decreased sleep spindle spectral peak frequencies in the frontal region. Women were characterized by higher spectral intercepts and higher spectral peak frequencies in the sleep spindle range. No sex differences in whitened spectral peak amplitudes of the sleep spindle range were found. Intelligence correlated positively with whitened spectral peak amplitudes of the spindle frequency range in women, but not in men. Last, age-related increases in spectral exponents did not differ in subjects with average and high intelligence. Our findings replicate and complete previous reports in the literature, indicating that the number of variables describing NREM sleep EEG can be effectively reduced in order to overcome redundancy and Type I statistical errors in future electrophysiological studies of sleep. Author summaryGiven the tight reciprocal relationship between sleep and wakefulness, the objective description of the complex neural activity patterns characterizing human sleep is of utmost importance in understanding the several facets of brain function, like sex differences, aging and cognitive abilities. Current approaches are either exclusively based on visual impressions expressed in graded levels of sleep depth (W, N1, N2, N3, REM), whereas computerized quantitative methods provide an almost infinite number of potential metrics, suffering from significant redundancy and arbitrariness. Our current approach relies on the assumptions that the spontaneous human brain activity as reflected by the scalp-derived electroencephalogram (EEG) are characterized by coloured noise-like properties. That is, the contribution of different frequencies to the power spectrum of the signal are best described by power law functions with negative exponents. In addition, we assume, that stages N2-N3 are further characterized by additional non-random (non-noise like, sinusoidal) activity patterns, which are emerging at specific frequencies, called sleep spindles (9-18 Hz). By relying on these assumptions we were able to effectively reduce 191 spectral measures to 4: (1) the spectral intercept reflecting the overall amplitude of the signal, (2) the spectral slope reflecting the constant ratio of low over high frequency power, (3) the frequency of the maximal sleep spindle activity and (4) the amplitude of the sleep spindle spectral peak. These 4 measures were efficient in characterizing known age-effects, sex-differences and cognitive correlates of sleep EEG. Future clinical and basic studies are supposed to be significantly empowered by the efficient data reduction provided by our approach.

neuroscience↗

bioRxiv2018

The paradox of Rapid Eye Movement sleep in the light of oscillatory activity and functional synchronization during phasic and tonic microstates

Rapid Eye Movement (REM) sleep is a peculiar neural state showing a combination of muscle atonia and intense cortical activity. REM sleep is usually considered as a unitary state in neuroscientific research; however, it is composed of two different microstates: phasic and tonic REM. These differ in awakening thresholds, sensory processing, and cortical oscillations. Nevertheless, studies examining cortical oscillations during REM microstates are scarce, and used low spatial sampling. Here, we analyzed the data of 18 healthy individuals assessed by high-density sleep EEG recordings. We systematically contrasted phasic and tonic REM periods in terms of topographical distribution, source localization, as well as local, global and long-range synchronization of frequency-specific cortical activity. Tonic periods showed relatively increased high alpha and beta power over frontocentral derivations. In addition, higher frequency components of beta power exhibited increased global synchronization during tonic compared to phasic states. In contrast, in phasic periods we found increased power and synchronization of low frequency oscillations coexisting with increased and synchronized gamma activity. Source localization revealed several multimodal, higher-order associative, as well as sensorimotor areas as potential sources of increased high alpha/beta power during tonic compared to phasic REM. Increased gamma power in phasic REM was attributed to medial prefrontal and right lateralized temporal areas associated with emotional processing. Our findings emphasize the heterogeneous nature of REM sleep, expressed in two microstates with remarkably different neural activity. Considering the microarchitecture of REM sleep may provide new insights into the mechanisms of REM sleep in health and disease.\n\nSignificance StatementRapid Eye movement (REM) is composed of the alternation of two markedly different microstates: phasic and tonic REM. Here we present the first high-resolution topographical study that systematically contrasted spectral power, neural synchronization and brain topography of spontaneous cortical oscillations across phasic and tonic microstates. Phasic REM was characterized by the combination of synchronized low frequency oscillations resembling deeper sleep stages and high frequency oscillations reflecting intense cortical activity. In contrast, tonic REM sleep showed increased oscillatory activity in the alpha and beta ranges that may reflect a shift towards increased environmental alertness resembling resting wakefulness. Exploring the neurophysiological aspects of REM microstates may shed new light on the mechanisms of REM sleep in healthy and pathological conditions.

neuroscience↗