Biology subjects

Bodizs, R.

Publications and source records attributed to Bodizs, R..

At least 19 recordsLinked to original sources

bioRxiv2026

When sleep reports diverge from physiology: correlates of sleep discrepancy in a health population

Study Objectives. Sleep characteristics are often estimated using self-reports, which may differ from objective measurements, a phenomenon known as sleep discrepancy. However, the factors associated with the degree of sleep discrepancy remain poorly understood. Methods. In the current study, a large healthy participant sample of the Budapest Sleep, Experiences and Traits Study (total N=267, 1899 nights) completed a 7-day protocol including mobile EEG recordings and sleep diaries, and also provided questionnaire-based reports of habitual sleep. We compared analogous sleep metrics from these three modalities, and used cross-validated LASSO regression to investigate demographic, psychological and lifestyle-related factors associated with increased sleep discrepancy across all three modalities, at both between- and within-participant levels. Results. Daily diaries estimated EEG-based sleep timing accurately (mean r=0.83), but were less accurate for sleep onset latency and quality. In contrast, questionnaire reports of habitual sleep provided inaccurate measures of even sleep timing (mean r=0.49) and considerably misestimated sleep timing and duration. Insomnia and depressive symptoms, napping, co-sleeping and personality traits were associated with increased sleep discrepancy. Conclusion. In healthy adults, questionnaires about habitual sleep provide only moderately accurate and biased estimates of actual sleep. Daily diaries provide considerably more accurate estimates, but sleep onset latency and physiological sleep quality is estimated by all self-reports less accurately than sleep timing. Sleep discrepancy is also present in healthy participants, it is particularly and its degree is affected by non-pathological characteristics. Long-term monitoring by daily diaries or wearables should be preferred to self-report questionnaires to measure sleep.

neuroscience↗

bioRxiv2025

A broken power-law model of heart rate variability spectra in sleep

AimsThe aim of the study was to introduce a parametric description of RR-interval spectra using a broken power-law model, in addition to the parametrization of oscillatory peaks. Furthermore, to use this model to evaluate effects of age, sex and sleep architecture on overnight heart rate variability (HRV) in healthy subjects. Methods & ResultsFrom a polysomnography database, 215 whole-night, high quality electrocardiograms (ECGs) were extracted. The fractal and oscillatory power-spectral densities (PSDs) were calculated from evenly resampled RR-interval time-series, then a broken power-law model was fitted using piecewise linear regression to the double-logarithmic PSD, determining a custom breaking point in the fractal component, and allowing for two independent spectral slopes in the lower and higher frequency domains. The two-slope model provided a more optimal description compared to linear regression in all cases, even when penalizing increased model complexity. Peak detection was applied to the oscillatory component in the LF (0.04-0.15 Hz) and HF (0.15-0.4 Hz) bands, extracting the frequency and prominence of the dominant peak from each. The high frequency domain intercept, the breaking point frequency and the LF peak frequency decreased significantly with age. Both slopes were flatter in females, while the high domain intercept and the HF peak prominence was significantly increased. Waking after sleep onset and lightest sleep (N1) were associated with lower intercept values, while REM sleep had an opposite effect. ConclusionsThe broken power-law model proved to be more appropriate for the description of RR-interval spectra than the single-slope model, and also captured effects of age, sex and sleep structure that were corroborated by the literature. We would like to highlight that while HRV changes are often assumed to be of oscillatory origin, the fractal component has a major contribution to the total PSD, and thus to all measures derived from it.

physiology↗

bioRxiv2025

Spectral features of heart rate variability in Williams syndrome during sleep

AimsThe study aims to reveal the spectral alterations of heart rate variability (HRV) in Williams syndrome (WS) during sleep, using a two-slope, broken power-law model, taking into account the multi-fractal properties of the RR-interval spectra, including effects of aging and associations with sleep structure indicators. Methods & ResultsExtracting ECG recordings from a polysomnography database of 20 subjects with WS and 20 age and sex matched typically developing (TD) controls, RR-interval time series were constructed, then the fractal and oscillatory power-spectral densities computed using the Irregular-Resampling Auto-Spectral Analysis (IRASA) method. The fractal component was parametrized with a piecewise-linear function, that allowed for a custom breaking point in the power spectral density (PSD), and separate slope and intercept values in the lower and higher frequency domains. The frequency and prominence of the dominant peak was extracted from the LF (0.04-0.15 Hz) and HF (0.15-0.4 Hz) bands. Strong WS vs TD group differences were found in the frequency of the breaking point, high domain slope, intercept and HF peak prominence. Analysis of the LF peak frequency revealed age-dependent decrease in the TD, but not in the WS group, while generally decreased values in WS independent of age, potentially reflected accelerated aging, characteristic of the syndrome. As fractal parameters were correlated, a main component was identified using principal component analysis that described the typical alterations in the fractal spectrum in WS, and was correlated with sleep structure indices as well. ConclusionsThe broken power-law model proved to be successful in characterizing the fractal component of HRV spectra, furthermore it captured alterations in cardiac regulation in WS. A main spectral feature of WS was identified in the fractal component, being associated with sleep quality indicators, a possible biomarker of the degree of general autonomic deregulation inherent to the syndrome.

physiology↗

bioRxiv2024

Sleep Deprivation in Mice: Looking Beyond the Slow Wave Rebound

Sleep is a fundamental process by which the brain achieves an optimal computational regime; it has been suggested that criticality is an appropriate theoretical framework in which such a process can be understood. Studying the critical and fractal dynamics of the brain involves modelling the nonlinearity of brain signals (such as EEG or ECoG) yielding, among others, the following metrics: spectral slope, spectral intercept, spectral knee, and normalized spectral entropy. Therefore, the present study investigates the nonlinear and critical dynamics of the brain in relation to sleep deprivation in mice, by comparing the sensitivity of the above-mentioned metrics to classical band- limited spectral indices. Mice were exposed to a 9 day-long sleep deprivation paradigm with baseline, sleep deprivation, and recovery phases. Spectral parameters were computed using the FOOOF algorithm. The results suggest that the classical approach (slow wave activity; 0.75-4.5 Hz) to neural signal processing differentiates between baseline sleep and rebound sleep only during the NREM phase. In contrast, the spectral slope and the spectral intercept both capture sleep deprivation related effects during REM and NREM episodes as well. This is particularly notable considering that the spectral knee is shifted towards higher frequencies, essentially rendering the spectral slope unreflective of slow wave activity - traditionally considered the biomarker of sleep homeostasis. Lastly, normalized spectral entropy fails to differentiate between baseline sleep and sleep following sleep deprivation in mice. These results support the sensitivity of fractal spectral parameters indexing the intricate balance between sleep and wake states.

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

Effect of sleep deprivation on fractal and oscillatory spectral measures of the sleep EEG: a window on basic regulatory processes

Sleep is vital for sustaining life; therefore, reliable measurement of its regulatory processes is of significant importance in research and medicine. Here we examine the effect of extended wakefulness on the putative indicators of fundamental sleep regulatory processes (spectral slope and spindle frequency) proposed by the Fractal and Oscillatory Adjustment model of sleep regulation by involving a healthy young adult sample in a 35-hour long sleep deprivation protocol. Wearable headband EEG-derived results revealed that NREM sleep electroencephalogram (EEG) spectral slope estimated in the 2-48 Hz range is an accurate indicator of the predicted changes in sleep depth induced by sleep deprivation (steepened slopes in recovery sleep) or by the overnight dissipation of sleep pressure (flattening slopes during successive sleep cycles). While the baseline overnight dynamics of the center frequency of the sleep spindle oscillations followed a U-shaped curve, and the timing of its minimum (the presumed phase indicator) correlated with questionnaire-based chronotype metrics as predicted, a different picture emerged during recovery sleep. Advanced recovery sleep advanced the timing of the minima of the oscillatory spindle frequency, reduced considerably its relationship with chronotype, but retained partially its U-shaped overnight evolution. Overall, our study supports the use of the spectral slope of the sleep EEG as a homeostatic marker of wake-sleep regulation, in addition, encourages further research on the EEG-derived measure of the circadian rhythm, primarily focusing on its interaction with the homeostatic process.

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

Sleep homeostasis in a naturalistic setting

Sleep, especially NREM sleep depth is homeostatically regulated, as sleep pressure builds up during wakefulness and diminishes during deep sleep. Previous evidence from this phenomenon, however, mainly stems from experimental studies which may not generalize to an ecologically valid setting. In the current study, we used a dataset of 246 individuals sleeping for at least seven nights each with a mobile EEG headband according to their ordinary daily schedule to investigate the effect of time spent in wakefulness on sleep characteristics. Increased time in wakefulness prior to sleep was associated with decreased sleep onset latency, increased sleep efficiency, a larger percentage of N3 sleep, and higher delta activity. Moreover, increased sleep pressure resulted in an increase in both the slope and the intercept of the sleep EEG spectrum. As predicted, PSD effects were most prominent in the earliest hours of sleep. Our results demonstrate that experimental findings showing increased sleep depth after extended wakefulness generalize to ecologically valid settings, and that time spent awake is an important determinant of sleep characteristics on the subsequent night. Our findings are evidence for the efficacy of sleep restriction, a behavioral technique already widely used in clinical settings, as a simple but powerful method to improve the objective quality of sleep in those with sleep problems.

neuroscience↗

bioRxiv2023

Fractal cycles of sleep: a new aperiodic activity-based definition of sleep cycles

Nocturnal human sleep consists of 4 - 6 ninety-minute cycles defined as episodes of non-rapid eye movement (non-REM) sleep followed by an episode of REM sleep. While sleep cycles are considered fundamental components of sleep, their functional significance largely remains unclear. One of the reasons for a lack of research progress in this field is the absence of a data-driven definition of sleep cycles. Here, we proposed to base such a definition on fractal (aperiodic) neural activity, a well-established marker of arousal and sleep stages. We explored temporal dynamics of fractal activity during nocturnal sleep using electroencephalography. Based on the observed pattern of fractal fluctuations, we introduced a new concept of fractal activity-based cycles of sleep or "fractal cycles" for short, defined as a time interval during which fractal activity descends from its local maximum to its local minimum and then leads back to the next local maximum. Next, we assessed correlations between fractal and classical (i.e., non-REM - REM) sleep cycle durations. We also studied cycles with skipped REM sleep, i.e., the cycles where the REM phase is expected to appear except that it does not, being replaced by lightening of sleep. Regarding the sample, we examined fractal cycles in healthy adults (age range: 18 - 75 years, n = 205) as well as in children and adolescents (range: 8 - 17 years, n = 21), the group characterized by deeper sleep and a higher frequency of cycles with skipped REM sleep. Further, we studied fractal cycles in major depressive disorder (n = 111), the condition characterized by altered REM sleep (in addition to its clinical symptoms). We found that fractal and classical cycle durations (89 {+/-} 34 min vs 90 {+/-} 25 min) correlated positively (r = 0.5, p < 0.001). Cycle-to-cycle overnight dynamics showed an inverted U-shape of both fractal and classical cycle durations and a gradual decrease in absolute amplitudes of the fractal descents and ascents from early to late cycles. In adults, the fractal cycle duration and participants age correlated negatively (r = -0.2, p = 0.006). Children and adolescents had shorter fractal cycles compared to young adults (76 {+/-} 34 vs 94 {+/-} 32 min, p < 0.001). The fractal cycle algorithm detected cycles with skipped REM sleep in 91 - 98% of cases. Medicated patients with depression showed longer fractal cycles compared to their own unmedicated state (107 {+/-} 51 min vs 92 {+/-} 38 min, p < 0.001) and age-matched controls (104 {+/-} 49 vs 88 {+/-} 31 min, p < 0.001). In conclusion, fractal cycles are an objective, quantifiable, continuous and biologically plausible way to display sleep neural activity and its cycles. They are useful in healthy adult and pediatric populations as well as in patients with major depressive disorder. Fractal cycles should be extensively studied to advance theoretical research on sleep structure. Highlights- Fractal activity-based cycles of sleep or "fractal cycles" for short is a new concept based on cyclic changes in fractal (aperiodic) neural activity during sleep. - Durations of fractal and classical cycles correlate, and both show an inverted U-shape when seen from early to late cycles. - The fractal cycle algorithm is effective in detecting cycles with skipped REM sleep. - Older healthy adults shower shorter fractal - but not classical - cycle durations. - Fractal cycle duration is shorter in children and adolescents compared to young adults. - In major depressive disorder, antidepressant medication is associated with longer fractal cycles.

neuroscience↗

bioRxiv2023

Comparing manual and automatic artifact detection in sleep EEG recordings

All sleep EEG recordings can be contaminated by artifacts. Both visual and automatic methods have been developed to mark such erroneous segments of EEG data. Here we systematically explore the effect of artifacts on the sleep EEG power spectrum density (PSD), and we compare gold-standard visual detections to a simple automatic detector using Hjorth parameters to identify artifacts. We find that most distortions in the all-night average PSD occur because of a small minority of highly anomalous artifacts, which mainly affect the beta and gamma frequency ranges and NREM delta. Visual and automatic detections only show moderate agreement in which data segments are artefactual. However, the resulting all-night average PSD is highly similar across all methods, and PSDs calculated with all methods successfully recover the known correlations of PSD with age and sex. No parameter settings of the automatic detector clearly outperformed others. Additionally, we show that accurate average PSD estimates can be recovered from just a fraction of available data epochs. Our results suggest that artifacts represent a minor and easily solvable problem in sleep EEG recordings. Most visually identified artifacts do not seriously distort estimates of mid-frequency activity in the sleep EEG spectrum, and distortions to low and high frequencies can be eliminated using a simple automatic detection method nearly as well as with visual detections. These findings show that the visual inspection of EEG data is not necessary to eliminate the effects of artifacts, which is encouraging for the expected performance of automatic preprocessing in large sleep EEG databases.

neuroscience↗

bioRxiv2023

Persistently increased post-stress activity of paraventricular thalamic neurons is essential for the emergence of stress-induced maladaptive behavior

ABSTRACTTraumatic events can immediately lead to debilitating symptoms collectively called Acute Stress Disorder (ASD), however the mechanisms of ASD are poorly understood. Using a rodent model of ASD here we identify a crucial communication bottleneck between the brainstem and the forebrain, the calretinin-positive neurons in the paraventricular thalamus (PVT/CR+), that controls ASD. We show that following a single acute stress event, the pre-sleep behavior of the mice is altered for several days in parallel with a persistent increase in the firing rate of PVT/CR+ cells. Optogenetic inhibition of PVT/CR+ neuronal activity after the stress event for one hour was sufficient to rescue both the ASD symptoms and the prolonged increase in PVT/CR+ firing rate. Inhibition applied 5 days later was still able to ameliorate some of the symptoms. These data suggest that post-stress activity of PVT/CR+ neurons play a critical role in mediating the acute forms of stress-related affective dysfunctions. One-Sentence SummaryPost-stress inhibition of paraventricular thalamic neurons prevents the emergence of acute stressed phenotype.

neuroscience↗

bioRxiv2023

Daily rhythms, demographics and health: findings from a nationally representative survey

The timing of daily activity in humans have been associated with various demographic and health-related factors, but the possibly complex patterns of confounding and interaction between these has not been systematically explored. We use data from Hungarostudy 2021, a nationally representative survey of 7000 Hungarians to assess the relationship between self- reported chronotype, social jetlag (using the Munich Chronotype Questionnaire), demographic variables and self-reported health and demographic variables, including ethnic and sexual minority membership. Supporting the validity of self-reports, participants with later chronotypes reported the lowest daytime sleepiness at a later clock time. We found that older age, female sex, a more eastward and southward geographical position, residence in a smaller settlement, less education and income, religiousness and cohabiting with small children were associated with an earlier chronotype. Younger age, higher education and income, and cohabiting with small children were associated with increased social jetlag. Of the 48 health-related variables surveyed, the relationship with both chronotype and social jetlag were mostly accounted for by age, sex, and socioeconomic effects, but we identified alcohol consumption, smoking, and physical activity as predictors of both social jetlag and chronotype, while a number of disorders were either positively or negatively associated with chronotype and social jetlag. Our findings from a large, nationally representative sample indicate that both biological and social factors influence chronotype and identified both demographic and health-related variables as risk factors for social jetlag. Our results, however, do not support a causal relationship between light exposure and mental health.

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 adolescent pattern of sleep spindle development revealed by HD-EEG polisomnography

Sleep spindles are developmentally relevant cortical oscillatory patterns; however, they have mostly been studied by considering the entire spindle frequency range (11 to 15 Hz) without a distinction between the functionally and topographically different slow and fast spindles, using relatively few electrodes and analysing wide age-ranges. Here, we employ HD-EEG polysomnography in three age-groups between 12 to 20 years of age, with an equal distribution between the two genders, and analyse the adolescent developmental pattern of the four major parameters of slow and fast sleep spindles. Most of our findings corroborate those very few previous studies that also make a distinction between slow and fast spindles in their developmental analysis. We find spindle frequency increasing with age, although spindle density change is not obvious in our study. We confirm the declining tendencies for amplitude and duration, although within narrower, more specific age-windows than previously. Spindle frequency seems to be higher in females in the oldest age-group. Based on the pattern of our findings, we suggest that HD-EEG, specifically targeting slow and fast spindle ranges and relatively narrow age-ranges would advance the understanding of both adolescent development and the functional relevance of sleep spindles in general.

neuroscience↗

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

Thalamic activity during scalp slow waves in humans

Slow waves are major pacemakers of NREM sleep oscillations. While slow waves themselves are mainly generated by cortical neurons, it is not clear what role thalamic activity plays in the generation of some oscillations grouped by slow waves, and to what extent thalamic activity during slow waves is itself driven by corticothalamic inputs. To address this question, we simultaneously recorded both scalp EEG and local field potentials from six thalamic nuclei (bilateral anterior, mediodorsal and ventral anterior) in fifteen epileptic patients (age-range: 17-64 years, 7 females) undergoing Deep Brain Stimulation Protocol and assessed the temporal evolution of thalamic activity relative to scalp slow waves using time-frequency analysis. We found that thalamic activity in all six nuclei during scalp slow waves is highly similar to what is observed on the scalp itself. Slow wave downstates are characterized by delta, theta and alpha activity and followed by beta, high sigma and low sigma activity during subsequent upstates. Gamma activity in the thalamus is not significantly grouped by slow waves. Theta and alpha activity appeared first on the scalp, but sigma activity appeared first in the thalamus. These effects were largely independent from the scalp region in which SWs were detected and the precise identity of thalamic nuclei. Our results indicate that while small thalamocortical neuron assemblies may initiate cortical oscillations, especially in the sleep spindle range, the large-scale neuronal activity in the thalamus which is detected by field potentials is principally driven by global cortical activity, and thus it is highly similar to what is observed on the scalp.

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↗