Biology subjects

Zavecz, Z.

Publications and source records attributed to Zavecz, Z..

5 recordsLinked to original sources

bioRxiv2024

Electrophysiological correlates of lucid dreaming

Lucid dreaming (LD) is a state of conscious awareness of the current dream state, predominantly associated with REM sleep. Research progress in uncovering the neurobiological basis of LD has been hindered by low sample sizes, diverse EEG setups, and specific artifact issues like saccadic eye movements and signal non-stationarity. To address these matters, we developed a multi-stage preprocessing pipeline that integrates standardized early-stage preprocessing, artifact subspace reconstruction, and signal-space projection. This approach enhanced data quality by precisely removing saccadic potential effects even in setups with minimal channels. To robustly identify the electrophysiological correlates of LD, we applied this methodology to LD data collected across laboratories (pooled N = 44) and explored sensor-and source-level markers hypothesized to underlie LD. Compared to non-lucid REM sleep, we observed few robust differences on the EEG sensor level, which is in line with recent findings. In contrast, on the source level, gamma1 power (30-36 Hz) showed increases during LD in left-hemispheric temporal areas, which might reflect verbal insight processes. Gamma1 power also increased around the onset of LD eye signaling in right temporo-occipital regions including the right precuneus, in line with its involvement in self-referential thinking. Reductions in beta power (12-30 Hz) during LD in right central and parietal areas including the temporo-parietal junction are potentially associated with a conscious reassessment of the veridicality of the currently perceived reality. Notably, functional connectivity in alpha band (8-12 Hz) increased during LD, in contrast to the reductions typically seen in psychedelic states, highlighting enhanced self-awareness. Taken together, these findings illuminate the electrophysiological correlates of LD state, and may serve as a basis to uncover neural mechanisms at the time point of lucid dream insight.

neuroscience↗

bioRxiv2023

Sustained polyphasic sleep restriction abolishes human growth hormone release

Voluntary sleep restriction is a common phenomenon in industrialized societies aiming to increase time spent awake and thus productivity. We explored how restricting sleep to a radically polyphasic schedule affects neural, cognitive, and endocrine characteristics. Ten young healthy participants were restricted to one 30-min nap opportunity at the end of every 4 hours (i.e., 6 sleep episodes per 24 hours) without any extended core sleep window, which resulted in a cumulative sleep amount of just 2 hours per day (i.e., [~]20 min per bout). All but one participant terminated this schedule during the first three weeks. The remaining participant (a 25-year-old male) succeeded to adhere to a polyphasic schedule for 5 weeks with no apparent impairments in cognitive and psychiatric measures except for psychomotor vigilance. While in-blood cortisol or melatonin release pattern and amounts were unaltered by the polyphasic as compared to monophasic sleep, growth hormone seemed almost entirely abolished (>95% decrease), with the residual release showing a considerably changed polyphasic secretional pattern. While coarse sleep structure appeared intact during polyphasic sleep, REM sleep showed decreased oscillatory and increased aperiodic EEG activity compared to monophasic sleep. Considering the decreased vigilance, abolished growth hormone release, and neurophysiological changes observed, it is doubtful that radically polyphasic sleep schedules can subserve the different functions of sleep to a sufficient degree.

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↗

bioRxiv2018

Does subjective sleep quality predict cognitive performance? - Evidence from three empirical studies

The role of sleep in cognitive performance has gained increasing attention in neuroscience and sleep research in the recent decades, however, the relationship between subjective (self-reported) sleep quality and cognitive performance has not yet been comprehensively characterized. In this paper, our aim was to test the relationship between subjective sleep quality and a wide range of cognitive functions in a healthy young adult sample combined across three studies. Sleep quality was assessed by Pittsburgh Sleep Quality Index, Athens Insomnia Scale, and a sleep diary to capture general subjective sleep quality, and Groningen Sleep Quality Scale to capture prior nights sleep quality. Within cognitive functions, we tested working memory, executive functions, and several sub-processes of procedural learning. To provide more reliable results, we included robust frequentist and Bayesian statistical analyses as well. Unequivocally across all analyses, we showed that there is no association between subjective sleep quality and cognitive performance in the domain of working memory, executive functions and procedural learning in healthy young adults. Our paper can contribute to a deeper understanding of subjective sleep quality and its measures, and we discuss various factors that may affect whether associations can be observed between subjective sleep quality and cognitive performance.

neuroscience↗

bioRxiv2017

Deconstructing Procedural Memory: Different Learning Trajectories and Consolidation of Sequence and Statistical Learning

Procedural learning is a fundamental cognitive function that facilitates efficient processing of and automatic responses to complex environmental stimuli. Here, we examined training-dependent and off-line changes of two sub-processes of procedural learning: namely, sequence learning and statistical learning. Whereas sequence learning requires the acquisition of order-based relationships between the elements of a sequence, statistical learning is based on the acquisition of probabilistic associations between elements. Seventy-eight healthy young adults (58 females and 20 males) completed the modified version of the Alternating Serial Reaction Time task that was designed to measure Sequence and Statistical Learning simultaneously. After training, participants were randomly assigned to one of three conditions: active wakefulness, quiet rest, or daytime sleep. We examined off-line changes in Sequence and Statistical Learning as well as further improvements after extended practice. Performance in Sequence Learning increased during training, while Statistical Learning plateaued relatively rapidly. After the off-line period, both the acquired sequence and statistical knowledge was preserved, irrespective of the vigilance state (awake, quiet rest or sleep). Sequence Learning further improved during extended practice, while Statistical Learning did not. Moreover, within the sleep group, cortical oscillations and sleep spindle parameters showed differential associations with Sequence and Statistical Learning. Our findings can contribute to a deeper understanding of the dynamic changes of multiple parallel learning and consolidation processes that occur during procedural memory formation.

neuroscience↗