Aerobic Fitness and the Sleeping Brain of Adolescents – A Pilot Study

Abstract Introduction Aerobic fitness facilitates brain synaptic plasticity, which influences global and local sleep expression. While it is known that sleep patterns/behavior and non-rapid eye movement (NREM) sleep slow wave activity (SWA) tracks brain maturation, little is known about how aerobic fitness and sleep interact during development in youth. The aim of this pilot was to characterize relationships among aerobic fitness, measures of global/local sleep expression, and habitual sleep patterns in children and adolescents. We hypothesized that greater aerobic fitness would be associated with better sleep quality, indicated by increased SWA. Methods 20 adolescents (mean age=14.6±2.3 years old, range 11-17, 11 females) were evaluated for AF (peak VO2 assessed by ramp-type progressive cycle ergometry in the laboratory), habitual sleep duration and efficiency (continuous 7-14 day actigraphy with sleep diary), and topographic patterns of spectral power in slow wave, theta, and sleep spindle frequency ranges in non-rapid eye movement (NREM) sleep using overnight polysomnography with high-density electroencephalography (hdEEG, 128 channels). Results Significant relationships were observed between peak VO2 and habitual bedtime (r=-0.604, p=0.013) and wake-up time (r=-0.644, p=0.007), with greater fitness associated with an earlier sleep schedule (going to bed and waking up earlier). Peak VO2 was a significant predictor of slow oscillations (0.5-1Hz, p=0.018) and theta activity (4.5-7.5Hz, p=0.002) over anterior frontal and central derivations (p<0.001 and p=0.001, respectively) after adjusting for sex and pubertal development stage. Similar associations were detected for fast sleep spindle activity (13-16Hz, p=0.006), which was greater over temporo-parietal derivations. Conclusion Greater AF was associated with earlier habitual sleep times and with enhanced expression of developmentally-relevant sleep oscillations during NREM sleep. These data suggest that AF may 1) minimize the behavioral sleep delay commonly seen during adolescence, and 2) impact topographically-specific features of sleep physiology known to mechanistically support neuroplasticity and cognitive processes which are dependent on prefrontal cortex and hippocampal function in adolescents and adults. Support (if any) NCATS grant #UL1TR001414 & PERC Systems Biology Fund

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145 Aerobic Fitness Is Associated with Advanced Circadian Rhythms in Adolescents

SLEEP ◽

10.1093/sleep/zsab072.144 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A59-A60

Author(s):

Ariel Neikrug ◽

Shlomit Radom-Aizik ◽

Ivy Chen ◽

Annamarie Stehli ◽

Kitty Lui ◽

...

Keyword(s):

Eye Movement ◽

Slow Wave ◽

Aerobic Fitness ◽

Nrem Sleep ◽

Brain Maturation ◽

Rapid Eye Movement ◽

Sleep Spindle ◽

Sleep Patterns ◽

Peak Vo2 ◽

Local Sleep

Abstract Introduction Aerobic fitness facilitates brain synaptic plasticity, which influences global and local sleep expression. While it is known that sleep patterns/behavior and non-rapid eye movement (NREM) sleep slow wave activity (SWA) tracks brain maturation, little is known about how aerobic fitness and sleep interact during development in youth. The aim of this pilot was to characterize relationships among aerobic fitness, measures of global/local sleep expression, and habitual sleep patterns in children and adolescents. We hypothesized that greater aerobic fitness would be associated with better sleep quality, indicated by increased SWA. Methods 20 adolescents (mean age=14.6±2.3 years old, range 11-17, 11 females) were evaluated for AF (peak VO2 assessed by ramp-type progressive cycle ergometry in the laboratory), habitual sleep duration and efficiency (continuous 7-14 day actigraphy with sleep diary), and topographic patterns of spectral power in slow wave, theta, and sleep spindle frequency ranges in non-rapid eye movement (NREM) sleep using overnight polysomnography with high-density electroencephalography (hdEEG, 128 channels). Results Significant relationships were observed between peak VO2 and habitual bedtime (r=-0.604, p=0.013) and wake-up time (r=-0.644, p=0.007), with greater fitness associated with an earlier sleep schedule (going to bed and waking up earlier). Peak VO2 was a significant predictor of slow oscillations (0.5-1Hz, p=0.018) and theta activity (4.5-7.5Hz, p=0.002) over anterior frontal and central derivations (p<0.001 and p=0.001, respectively) after adjusting for sex and pubertal development stage. Similar associations were detected for fast sleep spindle activity (13-16Hz, p=0.006), which was greater over temporo-parietal derivations. Conclusion Greater AF was associated with earlier habitual sleep times and with enhanced expression of developmentally-relevant sleep oscillations during NREM sleep. These data suggest that AF may 1) minimize the behavioral sleep delay commonly seen during adolescence, and 2) impact topographically-specific features of sleep physiology known to mechanistically support neuroplasticity and cognitive processes which are dependent on prefrontal cortex and hippocampal function in adolescents and adults. Support (if any) NCATS grant #UL1TR001414 & PERC Systems Biology Fund

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072 Sleep Spindle Harmonics in Insomnia

SLEEP ◽

10.1093/sleep/zsab072.071 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A29-A30

Author(s):

Michael Goldstein ◽

Monika Haack ◽

Janet Mullington

Keyword(s):

Spectral Power ◽

Nrem Sleep ◽

Sleep Time ◽

Sleep Spindle ◽

Time Frequency ◽

Insomnia Disorder ◽

Spectral Peaks ◽

Limited Application ◽

Thalamic Relay Nuclei ◽

Restorative Sleep

Abstract Introduction Prior research has reported NREM spectral EEG differences between individuals with insomnia and good-sleeper controls, including elevated high-frequency EEG power (beta/gamma bands, ~16-50Hz) and, to a lesser extent, elevations in sleep spindle parameters. However, the mechanisms driving these differences remain unclear. Harmonics have been observed in EEG data as spectral peaks at multiples of a fundamental frequency associated with an event (e.g., for a 14Hz spindle, the 2nd harmonic is expected to be a peak at 28Hz). Thus far, there has been very limited application of this idea of spectral harmonics to sleep spindles, even though these patterns can indeed be seen in some existing literature. We sought to build on this literature to apply spectral harmonic analysis to better understand differences between insomnia and good sleepers. Methods 15 individuals with insomnia disorder (DSM-5 criteria, 13 female, age 18–32 years) and 15 good-sleeper controls (matched for sex, age, and BMI) completed an overnight polysomnography recording in the laboratory and subsequent daytime testing. Insomnia diagnosis was determined by a board-certified sleep specialist, and exclusion criteria included psychiatric history within past 6 months, other sleep disorders, significant medical conditions, and medications with significant effects on inflammation, autonomic function, or other psychotropic effects. Results Consistent with prior studies, we found elevated sleep spindle density and fast sigma power (14-16Hz). Despite no difference in beta or gamma band power when averaged across NREM sleep, time-frequency analysis centered on the peaks of detected spindles revealed a phasic elevation in spectral power surrounding the 28Hz harmonic peak in the insomnia group, especially for spindles coupled with slow waves. We also observed an overall pattern of time-locked delay in the 28Hz harmonic peak, occurring approximately 40 msec after spindle peaks. Furthermore, we observed a 42Hz ‘3rd harmonic’ peak, not yet predicted by the existing modeling work, which was also elevated for insomnia. Conclusion In conjunction with existing mathematical modeling work that has linked sleep spindle harmonic peaks with thalamic relay nuclei as the primary generators of this EEG signature, these findings may enable novel insights into specific thalamocortical mechanisms of insomnia and non-restorative sleep. Support (if any) NIH 5T32HL007901-22

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The European starling (Sturnus vulgaris) shows signs of NREM sleep homeostasis but has very little REM sleep and no REM sleep homeostasis

SLEEP ◽

10.1093/sleep/zsz311 ◽

2019 ◽

Vol 43 (6) ◽

Cited By ~ 4

Author(s):

Sjoerd J van Hasselt ◽

Maria Rusche ◽

Alexei L Vyssotski ◽

Simon Verhulst ◽

Niels C Rattenborg ◽

...

Keyword(s):

Sleep Deprivation ◽

Eye Movement ◽

Rem Sleep ◽

Spectral Power ◽

Nrem Sleep ◽

Sleep Time ◽

European Starling ◽

Dark Phase ◽

Rapid Eye Movement ◽

Sleep Homeostasis

Abstract Most of our knowledge about the regulation and function of sleep is based on studies in a restricted number of mammalian species, particularly nocturnal rodents. Hence, there is still much to learn from comparative studies in other species. Birds are interesting because they appear to share key aspects of sleep with mammals, including the presence of two different forms of sleep, i.e. non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. We examined sleep architecture and sleep homeostasis in the European starling, using miniature dataloggers for electroencephalogram (EEG) recordings. Under controlled laboratory conditions with a 12:12 h light–dark cycle, the birds displayed a pronounced daily rhythm in sleep and wakefulness with most sleep occurring during the dark phase. Sleep mainly consisted of NREM sleep. In fact, the amount of REM sleep added up to only 1~2% of total sleep time. Animals were subjected to 4 or 8 h sleep deprivation to assess sleep homeostatic responses. Sleep deprivation induced changes in subsequent NREM sleep EEG spectral qualities for several hours, with increased spectral power from 1.17 Hz up to at least 25 Hz. In contrast, power below 1.17 Hz was decreased after sleep deprivation. Sleep deprivation also resulted in a small compensatory increase in NREM sleep time the next day. Changes in EEG spectral power and sleep time were largely similar after 4 and 8 h sleep deprivation. REM sleep was not noticeably compensated after sleep deprivation. In conclusion, starlings display signs of NREM sleep homeostasis but the results do not support the notion of important REM sleep functions.

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EEG microstates of dreams

Scientific Reports ◽

10.1038/s41598-020-74075-z ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 2

Author(s):

Lucie Bréchet ◽

Denis Brunet ◽

Lampros Perogamvros ◽

Giulio Tononi ◽

Christoph M. Michel

Keyword(s):

Temporal Dynamics ◽

Conscious Experience ◽

Low Frequency ◽

Nrem Sleep ◽

Occipital Cortex ◽

Perceptual Content ◽

Frequency Synchronization ◽

Dream Recall ◽

Brain States ◽

Waking Up

Abstract Why do people sometimes report that they remember dreams, while at other times they recall no experience? Despite the interest in dreams that may happen during the night, it has remained unclear which brain states determine whether these conscious experiences will occur and what prevents us from waking up during these episodes. Here we address this issue by comparing the EEG activity preceding awakenings with recalled vs. no recall of dreams using the EEG microstate approach. This approach characterizes transiently stable brain states of sub-second duration that involve neural networks with nearly synchronous dynamics. We found that two microstates (3 and 4) dominated during NREM sleep compared to resting wake. Further, within NREM sleep, microstate 3 was more expressed during periods followed by dream recall, whereas microstate 4 was less expressed. Source localization showed that microstate 3 encompassed the medial frontal lobe, whereas microstate 4 involved the occipital cortex, as well as thalamic and brainstem structures. Since NREM sleep is characterized by low-frequency synchronization, indicative of neuronal bistability, we interpret the increased presence of the “frontal” microstate 3 as a sign of deeper local deactivation, and the reduced presence of the “occipital” microstate 4 as a sign of local activation. The latter may account for the occurrence of dreaming with rich perceptual content, while the former may account for why the dreaming brain may undergo executive disconnection and remain asleep. This study demonstrates that NREM sleep consists of alternating brain states whose temporal dynamics determine whether conscious experience arises.

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Spatiotemporal Dynamics of Sleep Spindle Sources Across NREM Sleep Cycles

Frontiers in Neuroscience ◽

10.3389/fnins.2019.00727 ◽

2019 ◽

Vol 13 ◽

Cited By ~ 2

Author(s):

Valentina Alfonsi ◽

Aurora D’Atri ◽

Maurizio Gorgoni ◽

Serena Scarpelli ◽

Anastasia Mangiaruga ◽

...

Keyword(s):

Nrem Sleep ◽

Spatiotemporal Dynamics ◽

Sleep Spindle ◽

Sleep Cycles

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EEG spectral power density profiles during NREM sleep for gaboxadol and zolpidem in patients with primary insomnia

Journal of Psychopharmacology ◽

10.1177/0269881111424457 ◽

2011 ◽

Vol 26 (8) ◽

pp. 1081-1087 ◽

Cited By ~ 11

Author(s):

Jonas Lundahl ◽

Steve Deacon ◽

Damien Maurice ◽

Luc Staner

Keyword(s):

Power Density ◽

Spectral Power ◽

Spectral Power Density ◽

Nrem Sleep ◽

Primary Insomnia ◽

Density Profiles ◽

Eeg Spectral Power

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0789 Decreased Sigma Band Power During NREM Sleep in REM Sleep Behavior Disorder

SLEEP ◽

10.1093/sleep/zsaa056.785 ◽

2020 ◽

Vol 43 (Supplement_1) ◽

pp. A300-A300

Author(s):

Y Lee ◽

B Lee

Keyword(s):

Rem Sleep ◽

Spectral Power ◽

Rem Sleep Behavior Disorder ◽

Behavior Disorder ◽

Nrem Sleep ◽

Control Group ◽

Sleep Behavior ◽

Band Power ◽

Potential Biomarker ◽

Obstructive Sleep

Abstract Introduction REM sleep Behavior Disorder (RBD) is characterized by dream enacting behaviors and a loss of atonia during REM sleep. Early detection of RBD is important because it is considered premonitory symptoms neurodegenerative disorders. In this study, we investigated the slow and fast sigma band power of patients with RBD using frequency analysis. Methods Twenty patients who were diagnosed as RBD according to the ICSD-3 criteria and 20 age-matched controls who underwent polysomnography (PSG) for other sleep disorders (insomnia, snoring) and showed normal to mild obstructive sleep apnea (OSA). NREM sleep EEG data was extracted and N1 sleep data was excluded to minimize arousal artifact. Fast Fourier transform-based spectral power analysis was used to compute the power spectral densities of the EEG in the MATLAB environment. The sigma bands were divided into 2 discrete bands: slow sigma (11 to 13 Hz) and- fast sigma (13 to 15 Hz). Mann-Whitney U test by SPSS was used. Results RBD patients (61.9 ± 7.1 years old; 12 men) had a significantly lower sigma band power than the control group (61.5 ± 1.1 years old; 11 men) in central region (p = 0.028). Particularly, the slow sigma band power showed a bigger difference in all regions except O1 (F3 = 0.017, F4 = 0.027, C3 = 0.004, C4 = 0.009, O2 = 0.017). Conclusion Sigma power was lower in the RBD patients than in the control. It suggests that RBD has impaired cortical activity. Thus, decreased spindle activity during NREM sleep may be a potential biomarker of RBD. Support

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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

10.1101/2020.04.09.035113 ◽

2020 ◽

Author(s):

Róbert Bódizs ◽

Orsolya Szalárdy ◽

Csenge Horváth ◽

Péter P. Ujma ◽

Ferenc Gombos ◽

...

Keyword(s):

Sex Differences ◽

Eye Movement ◽

Power Law ◽

Activity Patterns ◽

Nrem Sleep ◽

Sleep Eeg ◽

Spectral Peak ◽

Sleep Spindle ◽

Frequency Range ◽

Electroencephalogram Eeg

AbstractA 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.

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Dual orexin and MCH neuron-ablated mice display severe sleep attacks and cataplexy

eLife ◽

10.7554/elife.54275 ◽

2020 ◽

Vol 9 ◽

Cited By ~ 2

Author(s):

Chi Jung Hung ◽

Daisuke Ono ◽

Thomas S Kilduff ◽

Akihiro Yamanaka

Keyword(s):

Transgenic Mice ◽

Spectral Power ◽

Nrem Sleep ◽

Functional Interaction ◽

Neuronal Populations ◽

Orexin Neuron ◽

Melanin Concentrating Hormone ◽

The Brain ◽

Theta Range

Orexin/hypocretin-producing and melanin-concentrating hormone-producing (MCH) neurons are co-extensive in the hypothalamus and project throughout the brain to regulate sleep/wakefulness. Ablation of orexin neurons decreases wakefulness and results in a narcolepsy-like phenotype, whereas ablation of MCH neurons increases wakefulness. Since it is unclear how orexin and MCH neurons interact to regulate sleep/wakefulness, we generated transgenic mice in which both orexin and MCH neurons could be ablated. Double-ablated mice exhibited increased wakefulness and decreased both rapid eye movement (REM) and non-REM (NREM) sleep. Double-ablated mice showed severe cataplexy compared with orexin neuron-ablated mice, suggesting that MCH neurons normally suppress cataplexy. Double-ablated mice also showed frequent sleep attacks with elevated spectral power in the delta and theta range, a unique state that we call ‘delta-theta sleep’. Together, these results indicate a functional interaction between orexin and MCH neurons in vivo that suggests the synergistic involvement of these neuronal populations in the sleep/wakefulness cycle.

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