Péter Simor

Long-range alpha and beta and short-range gamma EEG synchronization distinguishes phasic and tonic REM periods

Rapid eye movement (REM) sleep is characterized by the alternation of two markedly different microstates, phasic and tonic REM. These periods differ in awakening and arousal thresholds, sensory processing, and spontaneous cortical oscillations. Previous studies indicate that although in phasic REM, cortical activity is independent of the external environment, attentional functions and sensory processing are partially maintained during tonic periods. Large-scale synchronization of oscillatory activity, especially in the α- and β-frequency ranges, can accurately distinguish different states of vigilance and cognitive processes of enhanced alertness and attention. Therefore, we examined long-range inter- and intrahemispheric as well as short-range electroencephalographic synchronization during phasic and tonic REM periods quantified by the weighted phase lag index. Based on the nocturnal polysomnographic data of 19 healthy adult participants, we showed that long-range inter- and intrahemispheric α and β synchrony was enhanced in tonic REM states in contrast to phasic ones, and resembled α and β synchronization of resting wakefulness. On the other hand, short-range synchronization within the γ-frequency range was higher in phasic compared with tonic periods. Increased short-range synchrony might reflect local and inwardly driven sensorimotor activity during phasic REM periods, whereas enhanced long-range synchrony might index frontoparietal activity that reinstates environmental alertness after phasic REM periods.

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 relationship 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 early off-line changes in Sequence and Statistical Learning as well as further improvements after extended practice. Performance in Sequence Learning increased in a gradual manner 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. Sequence Learning further improved during extended practice, while Statistical Learning did not. Although, on a behavioral level, Sequence and Statistical Learning were similar across groups after the off-line period, cortical oscillations were associated with individual differences in performance changes within the sleep group only. Moreover, 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.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 relationship 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 early off-line changes in Sequence and Statistical Learning as well as further improvements after extended practice. Performance in Sequence Learning increased in a gradual manner 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. Sequence Learning further improved during extended practice, while Statistical Learning did not. Although, on a behavioral level, Sequence and Statistical Learning were similar across groups after the off-line period, cortical oscillations were associated with individual differences in performance changes within the sleep group only. Moreover, 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.

Individual slow wave morphology is a marker of ageing

Slow wave activity is a hallmark of deep NREM sleep. Scalp slow wave morphology is stereotypical, it is highly correlated with the synchronized onset and cessation of cortical neuronal firing measured from the surface or depth of the cortex, strongly affected by ageing, and these changes are causally associated with age-related cognitive decline. We investigated how normal ageing affects the individual morphology of the slow wave, and whether these changes are captured by the summary slow wave parameters generally used in the literature. We recorded full-night polysomnography in 159 subjects (age 17-69 years) and automatically detected slow waves using six different detection methods to ensure methodological robustness. We established individual slow morphologies at 501 data points for each subject and also calculated the individual average slow wave amplitude, average ascending and descending slope steepness and the total number of slow waves (gross parameters). Using LASSO penalized regression we found that fine-grained slow wave morphology is associated with age beyond gross parameters, with young subjects having faster slow wave polarity reversals, suggesting a more efficient initiation and termination of slow wave down- and upstates. Our results demonstrate the superiority of the high-resolution slow wave morphology as a biomarker of ageing, and highlights state transitions as promising targets of restorative stimulation-based interventions.

Lateralized rhythmic acoustic stimulation during daytime NREM sleep enhances slow waves

Slow wave sleep (SWS) is characterized by the predominance of delta waves and slow oscillations, reflecting the synchronized activity of large cortical neuronal populations. Amongst other functions, SWS plays a crucial role in the restorative capacity of sleep. Rhythmic acoustic stimulation (RAS) during SWS has been shown a cost-effective method to enhance slow wave activity. Slow wave activity can be expressed in a region-specific manner as a function of previous waking activity. However, it is unclear whether slow waves can be enhanced in a region-specific manner using RAS. We investigated the effects of unilaterally presented rhythmic acoustic sound patterns on sleep electroencephalographic (EEG) oscillations. Thirty-five participants received during SWS 12-second long rhythmic bursts of pink noise (at a rate of 1 Hz) that alternated with non-stimulated, silent periods, unilaterally delivered into one of the ears of the participants. As expected, RAS enhanced delta power, especially in its low-frequency components between 0.75 and 2.25 Hz. However, increased slow oscillatory activity was apparent in both hemispheres regardless of the side of the stimulation. The most robust increases in slow oscillatory activity appeared during the first 3-4 seconds of the stimulation period. Furthermore, a short-lasting increase in theta and sigma power was evidenced immediately after the first pulse of the stimulation sequences. Our findings indicate that lateralized RAS has a strong potential to globally enhance slow waves during daytime naps. The lack of localized effects suggests that slow waves are triggered by the ascending reticular system and not directly by specific auditory pathways.

Insomnia and intellect mask the positive link between schizotypal traits and creativity

Background Schizotypy is a set of personality traits that resemble the signs and symptoms of schizophrenia in the general population, and it is associated with various subclinical mental health problems, including sleep disturbances. Additionally, dimensions of schizotypy show specific but weak associations with creativity. Given that creativity demands cognitive control and mental health, and that sleep disturbances negatively impact cognitive control, we predicted that positive, impulsive and disorganised schizotypy will demonstrate stronger associations with indicators of creativity, if the effect of mental health, insomnia, and intellect are statistically controlled. Methods University students (N = 182) took part in the study. Schizotypy was assessed with the shortened Oxford-Liverpool Inventory of Feelings and Experiences (sO-LIFE). Creative achievements were measured with the Creative Achievement Questionnaire (CAQ), divergent thinking was assessed with the ‘Just suppose’ task, and remote association problem solving was tested with Compound Remote Associate (CRA) problems. Mental health was assessed with the 12-item version of the General Health Questionnaire (GHQ-12), and insomnia was examined with the Athens Insomnia Scale (AIS). Verbal short term memory was measured with the forward digit span task, and intellect was assessed with the Rational-Experiential Inventory (REI). Multiple linear regressions were performed to examine the relationship between creativity and schizotypy. Indicators of creativity were the dependent variables. In the first block, dimensions of schizotypy, age, gender and smoking were entered, and in the second block, the models were extended with mental health, insomnia, verbal short term memory, and intellect. Results Positive schizotypy positively predicted real-life creative achievements, independently from the positive effect of intellect. Follow-up analyses revealed that positive schizotypy predicted creative achievements in art, while higher disorganised schizotypy was associated with creative achievements in science (when intellect was controlled for). Furthermore, disorganised schizotypy positively predicted remote association problem solving performance, if insomnia and verbal short term memory were statistically controlled. No dimension of schizotypy was significantly associated with divergent thinking. Discussion In line with previous findings, positive schizotypy predicted real-life creative achievements. The positive effects of disorganised schizotypy might be explained in terms of the simultaneous involvement of enhanced semantic priming and cognitive control in problem solving. We speculate that the lack of associations between divergent thinking and schizotypy might be related to instruction effects. Our study underscores the relevance of sleep impairment to the psychosis-spectrum, and refines our knowledge about the adaptive aspects of schizotypy in the general population.

Increased cortical involvement and synchronization during CAP A1 slow waves

Slow waves recorded with EEG in NREM sleep are indicative of the strength and spatial extent of synchronized firing in neuronal assemblies of the cerebral cortex. Slow waves often appear in the A1 part of the cyclic alternating patterns (CAP), which correlate with a number of behavioral and biological parameters, but their physiological significance is not adequately known. We automatically detected slow waves from the scalp recordings of 37 healthy patients, visually identified CAP A1 events and compared slow waves during CAP A1 with those during NCAP. For each slow wave, we computed the amplitude, slopes, frequency, synchronization (synchronization likelihood) between specific cortical areas, as well as the location of origin and scalp propagation of individual waves. CAP A1 slow waves were characterized by greater spatial extent and amplitude, steeper slopes and greater cortical synchronization, but a similar prominence in frontal areas and similar propagation patterns to other areas on the scalp. Our results indicate that CAP A1 represents a period of highly synchronous neuronal firing over large areas of the cortical mantle. This feature may contribute to the role CAP A1 plays in both normal synaptic homeostasis and in the generation of epileptiform phenomena in epileptic patients.

S170 REM – Dreaming – Emotions: The neurophysiology of nightmare disorder

Objectives Nightmares are intense and highly unpleasant mental experiences that occur usually – but not exclusively – during late-night Rapid Eye Movement (REM) sleep and often provoke abrupt awakenings.The underlying mechanism of nightmare disorder was only scarcely investigated and remains poorly understood. The most influential model of nightmare disorder that provided a common framework fortrauma-related and non-traumatic (idiopathic) nightmares was introduced by Levin and Nielsen (2007). One of the main assumptions of the model is that nightmares reflect unsuccessful fear-extinction processes during (REM) sleep due to a dysfunctional brain network comprising fronto-limbic structures. Consequently, amygdalar over-activation and inefficient (prefrontal) inhibition lead to the emotional intensification of dreaming and hyperarousal during sleep. Nevertheless, sleep quality and emotional regulation in nightmare sufferers was mainly examined by questionnaire-based studies. Methods We have performed a series of quantitative EEG analyses based on the polysomnographic data of young (non-ptsd) nightmare sufferers and matched controls. Moreover, in a subsequent study we investigated emotional habituation to arousing images after a night of polysomnographically monitored sleep in a group of nightmare sufferers and matched controls. Emotional reactivity was quantified by subjective evaluations and psychophysiological measures (HRV, SCR). Results In line with questionnaire-based findings, we found that nightmare sufferers are characterized by fragmented sleep, reduced Slow Wave Sleep (SWS), enhanced microarousals during NREM sleep and heightened wake-like oscillations during REM sleep. Furthermore, arousal-related cortical activity coupled with attenuated parasympathetic regulation were evidenced in nightmare sufferers specifically during NREM to REM transitions. Discussion Given that the neural network underlying emotional memory processing is highly active during REM periods, and REM sleep seems to be intimately related to fear memory processing, our findings might indirectly support Levin and Nielsen’s assumption of disrupted REM-dependent fear-extinction in nightmare disorder. Nevertheless, the association between impaired emotional regulation and altered sleep physiology remains to be investigated. Conclusions In our experiments we aimed to directly test Levin and Nielsen’s fear-extintion model of nightmare disorder and to relate impaired emotional regulation to specific sleep EEG features. We conclude that examining nightmare disorder by such integrative approach would shed more light on the enigma of REM sleep and emotional memory processing. Significance Our research could shed more light on the mechanism of nightmare formation, propagate the professional knowledge of nightmare disorder, and facilitate the development of effective, evidence-based treatment procedures.