scholarly journals 0350 Characterizing Continuous Changes in Spectral Dynamics of Sleep EEG as a Function of Age

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A133-A133
Author(s):  
H Kim ◽  
M Prerau ◽  
S Redline
Keyword(s):  
Spectral Power ◽  
Population Analysis ◽  
Sampling Rate ◽  
Sleep Eeg ◽  
Sleep Stages ◽  
Apparent Difference ◽  
Sleep State ◽  
Spectral Dynamics ◽  
Time Frequency ◽  
Sleep Depth

Abstract Introduction Sleep is a continuous and dynamic physiological process. Current research practice, however, limits our ability to observe electroencephalography (EEG) oscillation dynamics by breaking sleep into discrete stages. In this study, we propose a novel quantitative framework that represents population-level changes in sleep EEG spectral dynamics as a function of age, preserving the information-rich spectral dynamics of sleep data. Rather than relying on sleep stages, our approach uses slow-oscillation power (SO-power) as an objective, continuous-valued correlate of sleep depth. Methods We analyzed the EEG signal (Fz-Cz, 256 Hz sampling rate) from a subset of the Multi-Ethnic Study of Atherosclerosis (MESA) study participants (n = 2056, 53.6% female, age: mean 69.37 ± 9.12, range 54 - 94) who underwent polysomnography. For each subject, we computed the sleep EEG multitaper spectrogram and extracted the total baseline-normalized SO-power (0.1 - 1.5 Hz). We next computed mean EEG spectral power as a function of SO-power, which we then tracked across all subjects as a function of age in sliding windows. Results The population analysis shows apparent, continuous changes in time-frequency domain features of the EEG as a function of a sleep depth along with age, that would be otherwise lost in traditional analyses. Moreover, by analyzing the directionality of the SO-power, we show that there is no apparent difference in neural activity during deepening sleep and lightening sleep; thus EEG sleep state is likely non-directional. Conclusion Our results show that state-based sleep dynamics of the EEG power spectrum can comprehensively be represented using SO-power as a surrogate of sleep depth. This representation identifies state-based activity independent of the temporal evolution of sleep architecture. As such, it is a powerful tool for analysis and phenotyping of EEG activity in large cohorts. Support The Biomedical Global Talent Nurturing Program through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea (HI19C1065) to HK, National Institute of Neurological Disorders and Stroke (NINDS, R01 NS-096177) to MP.

scholarly journals Sleep Neurophysiological Dynamics Through the Lens of Multitaper Spectral Analysis

Physiology ◽  
2017 ◽  
Vol 32 (1) ◽  
pp. 60-92 ◽  
Author(s):  
Michael J. Prerau ◽  
Ritchie E. Brown ◽  
Matt T. Bianchi ◽  
Jeffrey M. Ellenbogen ◽  
Patrick L. Purdon
Keyword(s):  
Spectral Analysis ◽  
Spectral Estimation ◽  
Sleep Eeg ◽  
Sleep Stages ◽  
Subcortical Structures ◽  
Sleep State ◽  
Time Frequency ◽  
Oscillatory Dynamics ◽  
Spectral Estimates ◽  
The Rich

During sleep, cortical and subcortical structures within the brain engage in highly structured oscillatory dynamics that can be observed in the electroencephalogram (EEG). The ability to accurately describe changes in sleep state from these oscillations has thus been a major goal of sleep medicine. While numerous studies over the past 50 years have shown sleep to be a continuous, multifocal, dynamic process, long-standing clinical practice categorizes sleep EEG into discrete stages through visual inspection of 30-s epochs. By representing sleep as a coarsely discretized progression of stages, vital neurophysiological information on the dynamic interplay between sleep and arousal is lost. However, by using principled time-frequency spectral analysis methods, the rich dynamics of the sleep EEG are immediately visible—elegantly depicted and quantified at time scales ranging from a full night down to individual microevents. In this paper, we review the neurophysiology of sleep through this lens of dynamic spectral analysis. We begin by reviewing spectral estimation techniques traditionally used in sleep EEG analysis and introduce multitaper spectral analysis, a method that makes EEG spectral estimates clearer and more accurate than traditional approaches. Through the lens of the multitaper spectrogram, we review the oscillations and mechanisms underlying the traditional sleep stages. In doing so, we will demonstrate how multitaper spectral analysis makes the oscillatory structure of traditional sleep states instantaneously visible, closely paralleling the traditional hypnogram, but with a richness of information that suggests novel insights into the neural mechanisms of sleep, as well as novel clinical and research applications.

scholarly journals Comparing two measures of sleep depth/intensity

SLEEP ◽  
2020 ◽  
Vol 43 (12) ◽  
Author(s):  
Magdy Younes ◽  
Paula K Schweitzer ◽  
Kara S Griffin ◽  
Robert Balshaw ◽  
James K Walsh
Keyword(s):  
Spectral Power ◽  
Secondary Analysis ◽  
Sleep Restriction ◽  
Sleep Stages ◽  
Characteristic Analysis ◽  
Experimental Conditions ◽  
Delta Power ◽  
Sleep Depth ◽  
Two Measures ◽  
Degree Of Association

Abstract Study Objectives To compare delta spectral power (delta) and odds ratio product (ORP) as measures of sleep depth during sleep restriction with placebo or a drug that increases delta. Methods This is a secondary analysis of data from a study of 41 healthy participants randomized to receive placebo or gaboxadol 15 mg during sleep restriction. Participants underwent in-laboratory sleep studies on two baseline, four sleep restriction (5-h), and two recovery nights. Relation between delta or ORP and sleep depth was operationally defined as the degree of association of each metric to the probability of arousal or awakening occurring during the next 30 s (arousability). Results ORP values in wake, N1, N2, N3, and REM were significantly different. Delta differed between both N2 and N3 and other sleep stages but not between wake and N1 or N1 and REM. Epoch-by-epoch and individual correlations between ORP and delta power were modest or insignificant. The relation between ORP and arousability was linear across the entire ORP range. Delta also changed with arousability but only when delta values were less than 300 μV2. Receiver-operating-characteristic analysis found the ability to predict imminent arousal to be significantly greater with ORP than with log delta power for all experimental conditions. Changes in ORP, but not log delta, across the night correlated with next-day physiologic sleep tendency. Conclusions Compared to delta power, ORP is more discriminating among sleep stages, more sensitive to sleep restriction, and more closely associated with arousability. This evidence supports ORP as a measure of sleep depth/intensity.

scholarly journals 0282 Correlation Between Sleep Depth in the Right and Left Cerebral Hemispheres Following Sleep Deprivation, Restriction or Noise Exposure

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A107-A107
Author(s):  
M Younes ◽  
S T Kuna ◽  
A I Pack ◽  
P K Schweitzer ◽  
J K Walsh ◽  
...  
Keyword(s):  
Sleep Deprivation ◽  
Noise Exposure ◽  
Traffic Noise ◽  
Sleep Loss ◽  
Sleep Stages ◽  
Depth Range ◽  
Sleep State ◽  
Sleep Depth ◽  
High Prevalence ◽  
The Right

Abstract Introduction The Odds-Ratio-Product (ORP) is a highly-validated continuous index of sleep depth (range 0=deep sleep; 2.5=full wakefulness). ORP values fluctuate within this range as sleep state changes between wake and different sleep stages. In healthy non-sleep deprived adults, intra-class correlation coefficient of concurrent right vs. left ORP values (R / L coefficient) is typically >0.80. In a recent study R / L coefficient was markedly reduced in many critically-ill patients and these patients failed to be weaned from mechanical ventilation. Given the high prevalence of sleep loss in such patients we hypothesized that reduction in R/L coefficient might result from sleep loss. This retrospective EEG analysis of data from 3 independent research studies investigated if R / L coefficient decreases in pure models of sleep deprivation, restriction or noise exposure during sleep in healthy subjects. Methods Polysomnograms were obtained from three studies: A) 200 subjects who underwent 36 hours of total sleep deprivation; B) 21 subjects who underwent 4 consecutive nights of sleep restriction (5 hrs. / night); C) 72 subjects who were exposed to intermittent traffic noise events with maximum sound pressure levels ranging from 45–65 dB(A) for 10 consecutive nights. For study A, R / L coefficient was calculated from pre- and post-deprivation sleep studies and the two values were compared. For study B, coefficient was calculated at baseline and in each restriction night. For study C, the coefficient was calculated in each of the 10 exposure nights and the slope of the change was calculated. Results In study A, the coefficient decreased from 0.82±0.12 at baseline to 0.74±0.16 after sleep deprivation (p &lt 0.0001). In study B, the coefficient decreased from 0.83±0.11 at baseline to 0.75±0.15 on the 4th restriction night (p &lt 0.01). In study C, coefficient decreased at a rate of 0.003±0.001 per exposure night (p &lt 0.001). Conclusion The correlation between sleep depth in the right and left hemispheres deteriorates following sleep deprivation, restriction or noise-induced sleep fragmentation. Support NIH P50 HL060287

scholarly journals 0150 Comparing Two Measures of Sleep Depth/ Intensity

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A59-A59
Author(s):  
M Younes ◽  
P K Schweitzer ◽  
K Griffin ◽  
J K Walsh ◽  
R Balshaw
Keyword(s):  
Spectral Power ◽  
Secondary Analysis ◽  
Sleep Restriction ◽  
Sleep Stages ◽  
Continuous Measure ◽  
Characteristic Analysis ◽  
Experimental Conditions ◽  
Delta Power ◽  
Sleep Depth ◽  
Two Measures

Abstract Introduction There is currently no well-validated method for evaluating objective sleep depth/intensity. Delta power is thought to reflect sleep depth based upon limited evidence. Odds-ratio-product (ORP) is a recently introduced continuous measure of sleep depth. We compared delta spectral power (delta) and ORP as measures of sleep depth/intensity during manipulations that altered sleep depth (sleep restriction with placebo or with a delta-promoting drug). We hypothesized that ORP will provide a more robust measure of sleep depth. Methods This is a secondary analysis of data from a study in which forty-one healthy subjects were sleep restricted and randomized to receive placebo or gaboxadol 15mg. Participants underwent consecutive in-laboratory sleep studies on two baseline, four sleep restriction (5 hours) and two recovery nights. The relation between delta or ORP during any given 30s epoch and sleep depth, operationally defined as the probability of arousal / awakening occurring during the next 30 seconds (arousability), was assessed. Results Mean ORP values differed significantly among the four sleep / wake stages, but delta power did not differentiate wake, N1 and N2. The relation between ORP and arousability was linear across the entire range of ORP whereas delta power detected differences in arousability only with delta values &lt 300 μV2. Correlations with arousability in individual subjects were stronger with ORP (p &lt 0.0001). Receiver operating characteristic analysis found the ability to predict imminent arousal to be significantly greater with ORP than with delta power for all experimental conditions (p &lt 0.0001). The increase in sleep depth with restriction alone was detected on the second day of restriction by ORP (p &lt 0.01) but not by delta. Conclusion As compared to delta power, ORP is more discriminating among sleep stages, more sensitive to sleep restriction, and more closely associated with arousability. These observations indicate ORP better reflects sleep depth/intensity. Support None

scholarly journals Neural correlates of memory recovery: Preliminary findings in children and adolescents with acquired brain injury

2021 ◽  
Vol 39 (1) ◽  
pp. 61-71
Author(s):  
Anne-Laure Mouthon ◽  
Andreas Meyer-Heim ◽  
Reto Huber ◽  
Hubertus J.A. Van Hedel
Keyword(s):  
Brain Injury ◽  
Spectral Power ◽  
Recovery Of Function ◽  
Acquired Brain Injury ◽  
Neural Correlates ◽  
Sleep Eeg ◽  
Brain Maturation ◽  
Brain Areas ◽  
Memory Recovery ◽  
Posterior Parietal

Background: After acquired brain injury (ABI), patients show various neurological impairments and outcome is difficult to predict. Identifying biomarkers of recovery could provide prognostic information about a patient’s neural potential for recovery and improve our understanding of neural reorganization. In healthy subjects, sleep slow wave activity (SWA, EEG spectral power 1–4.5 Hz) has been linked to neuroplastic processes such as learning and brain maturation. Therefore, we suggest that SWA might be a suitable measure to investigate neural reorganization underlying memory recovery. Objectives: In the present study, we used SWA to investigate neural correlates of recovery of function in ten paediatric patients with ABI (age range 7–15 years). Methods: We recorded high-density EEG (128 electrodes) during sleep at the beginning and end of rehabilitation. We used sleep EEG data of 52 typically developing children to calculate age-normalized values for individual patients. In patients, we also assessed every-day life memory impairment at the beginning and end of rehabilitation. Results: In the course of rehabilitation, memory recovery was paralleled by longitudinal changes in SWA over posterior parietal brain areas. SWA over left prefrontal and occipital brain areas at the beginning of rehabilitation predicted memory recovery. Conclusions: We show that longitudinal sleep-EEG measurements are feasible in the clinical setting. While posterior parietal and prefrontal brain areas are known to belong to the memory “core network”, occipital brain areas have never been related to memory. While we have to remain cautious in interpreting preliminary findings, we suggest that SWA is a promising measure to investigate neural reorganization.

072 Sleep Spindle Harmonics in Insomnia

SLEEP ◽  
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

scholarly journals 0710 Sleep EEG Spectral Power Characteristics According to Age and Gender in Middle-to-Late Adulthood

SLEEP ◽  
2018 ◽  
Vol 41 (suppl_1) ◽  
pp. A264-A264
Author(s):  
J Yoon ◽  
E Lee ◽  
S Lee ◽  
K Jung ◽  
S Park ◽  
...  
Keyword(s):  
Spectral Power ◽  
Sleep Eeg ◽  
Age And Gender ◽  
Late Adulthood ◽  
Power Characteristics ◽  
Eeg Spectral Power ◽  
And Gender

nCPAP Treatment of Obstructive Sleep Apnea Increases Slow Wave Sleep in Prefrontal EEG

2007 ◽  
Vol 38 (3) ◽  
pp. 148-154 ◽  
Author(s):  
Veera Eskelinen ◽  
Toomas Uibu ◽  
Sari-Leena Himanen
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Treatment Period ◽  
Slow Wave ◽  
Slow Wave Sleep ◽  
Sleep Stage ◽  
Sleep Eeg ◽  
Sleep Stages ◽  
Obstructive Sleep ◽  
Sleep Stage Scoring

According to standard sleep stage scoring, sleep EEG is studied from the central area of parietal lobes. However, slow wave sleep (SWS) has been found to be more powerful in frontal areas in healthy subjects. Obstructive sleep apnea syndrome (OSAS) patients often suffer from functional disturbances in prefrontal lobes. We studied the effects of nasal Continuous Positive Airway Pressure (nCPAP) treatment on sleep EEG, and especially on SWS, in left prefrontal and central locations in 12 mild to moderate OSAS patients. Sleep EEG was recorded by polysomnography before treatment and after a 3 month nCPAP treatment period. Recordings were classified into sleep stages. No difference was found in SWS by central sleep stage scoring after the nCPAP treatment period, but in the prefrontal lobe all night S3 sleep stage increased during treatment. Furthermore, prefrontal SWS increased in the second and decreased in the fourth NREM period. There was more SWS in prefrontal areas both before and after nCPAP treatment, and SWS increased significantly more in prefrontal than central areas during treatment. Regarding only central sleep stage scoring, nCPAP treatment did not increase SWS significantly. Frontopolar recording of sleep EEG is useful in addition to central recordings in order to better evaluate the results of nCPAP treatment.

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