P023 Quantitative EEG analysis of polysomnography in a case of Fatal Familial Insomnia

Abstract Purpose To report on quantitative electroencephalograph (EEG) activity during polysomnography (PSG) in a rare case of confirmed Fatal Familial Insomnia (FFI). Methods Sleep/wake characteristics of a 32-year-old male patient were quantitatively analysed using central EEG recordings during two PSGs (FFI-1 and FFI-2) first, for investigation of insomnia and PLMS but with no suspicion of FFI and second, 120 days later with suspected but unconfirmed FFI at the time; 89 days prior to death. PSG metrics; absolute EEG power in specified frequency bands; EEG slowing ratio of slow-to-fast frequencies ((delta + theta)/ (alpha + sigma + beta)); and sleep spindle density were calculated. Results were compared with gender and age-matched insomnia and healthy controls (two of each). Results FFI-1 and FFI-2 PSGs revealed total time in bed of 413.5 and 392 minutes, total sleep times of 208.5 and 7.5 minute, including NREM 153.0 and 2.5 minutes, and REM 55.5 and 5.0 minutes, respectively. FFI-1 had approximately 1.5 times lower slow wave activity (SWA, 0.5–4.5Hz) during N3 than insomnia and controls. FFI-1 had 2 times and 1.8 times higher slowing ratio during REM than insomnia and controls, respectively. Spindle density (per minute of NREM sleep) for FFI-1 was 0.9, compared to pair-averages of 1.2 for insomnia disorder and 4.7 for healthy controls. Conclusions PSG in FFI revealed poor sleep efficiency that severely deteriorated with disease progression. Quantitative analysis of EEG revealed lower spindle density, lower SWA in N3, and higher slowing ratio in REM, when compared to insomnia patients and healthy sleepers.

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0845 The Difference in Sleep Characteristics of Chronic Insomnia Disorder According to Gender and Age

SLEEP ◽

10.1093/sleep/zsaa056.841 ◽

2020 ◽

Vol 43 (Supplement_1) ◽

pp. A322-A322

Author(s):

J Hong ◽

H Lee ◽

I Yoon

Keyword(s):

Sleep Quality ◽

The Elderly ◽

Sleep Time ◽

Chronic Insomnia ◽

Middle Aged ◽

Subjective Sleep ◽

Time In Bed ◽

Insomnia Disorder ◽

Gender And Age ◽

Sleep Parameters

Abstract Introduction Impacts of age and gender on sleep have been reported in normal population, but rarely in chronic insomnia disorder (CID). This study aimed to investigate difference in sleep characteristics of CID according to gender and age. Methods The participants with drug-naïve CID and aged between 40 and 79 years were recruited. We compared subjective and objective sleep parameters between the middle-aged (40-64 years, N=86) and the elderly (65-79 years, N=50), and between men (N=45) and women (N=91). The subjective sleep quality and habitual sleep time were measured by Pittsburgh Sleep Quality Index (PSQI). The participants were asked to wear an actigraph for 4 days to obtain objective sleep parameters. Results In the PSQI, the elderly reported earlier bedtime and wake-up time (p=0.018; p=0.026), reduced total sleep time (TST) and sleep efficiency (p=0.003; p=0.011), and low sleep quality (p=0.034) compared to the middle-aged. However, according to the actigraphy, differences were observed only in the bedtime (p=0.016) and the wake-up time (p=0.002) between the two age groups. Between genders, the actigraphy showed that the male patients woke up earlier than the female group (p=0.015); except for this finding, there was no significant gender effect. Meanwhile, regarding gender and age interactions, the elderly women with CID showed longer time in bed (TIB) with increase in both TST and wake after sleep onset (WASO) compared to the middle-aged women. The elderly men showed decreased TIB and TST, and slightly decreased WASO than the middle-aged men. Conclusion The elderly with CID show more subjective sleep complaints than the middle-aged CID despite little difference in objective sleep characteristics, which suggests that the elderly CID may seek medical help more than the middle aged. As women with CID get older, they increase time spent in bed to maintain sleep time, but with resultant increase in wake. Support None

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O033 Predicting subjective sleep quality using multi-day actigraphy data: A machine learning approach

SLEEP Advances ◽

10.1093/sleepadvances/zpab014.032 ◽

2021 ◽

Vol 2 (Supplement_1) ◽

pp. A14-A15

Author(s):

C Kao ◽

A D’Rozario ◽

N Lovato ◽

D Bartlett ◽

S Postnova ◽

...

Keyword(s):

Machine Learning ◽

Sleep Quality ◽

Machine Learning Algorithms ◽

Series Data ◽

Poor Sleep ◽

Healthy Controls ◽

Subjective Sleep Quality ◽

Subjective Sleep ◽

Quality Ratings ◽

Insomnia Disorder

Abstract Objectives Insomnia is diagnosed using clinical interview but actigraphy is often used as a consecutive multi-day measurement of activity-rest cycles to quantify sleep-wake periods. However, discrepancies between subjective complaints of insomnia and objective actigraphy measurement exist. The aims of the current study were to (i) predict subjective sleep quality using actigraphic data and, (ii) identify features of actigraphy that are associated with poor subjective sleep quality. Methods Actigraphy data were collected for 14-consecutive days with corresponding subjective sleep quality ratings from participants with Insomnia Disorder and healthy controls. We fitted multiple machine learning algorithms to determine the best performing method with the highest accuracy of predicting subjective quality rating using actigraphic data. Results We analysed a total of 1278 days of actigraphy and corresponding subjective sleep quality ratings from 86 insomnia disorder patients and 20 healthy controls. The k-neighbors classifier provided the best performance in predicting subjective sleep quality with an overall accuracy, sensitivity and specificity of 83%, 74% and 87% respectively, and an average AUC-ROC of 0.88. We also found that activity recorded in the early morning (04:00-08:00) and overnight periods (00:00-04:00) had the greatest influence on sleep quality scores, with poor sleep quality related to these periods.. Conclusions A machine learning model based on actigraphy time-series data successfully predicted self-reported sleep quality. This approach could facilitate clinician’s diagnostic capabilities and provide an objective marker of subjective sleep disturbance.

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Quantitative EEG analysis of background rhythms in patients with complex partial seizures treated with Carbamazepine

Electroencephalography and Clinical Neurophysiology ◽

10.1016/s0013-4694(97)88608-0 ◽

1997 ◽

Vol 103 (1) ◽

pp. 135

Author(s):

M Pashu

Keyword(s):

Quantitative Eeg ◽

Eeg Analysis ◽

Partial Seizures ◽

Complex Partial Seizures ◽

Quantitative Eeg Analysis

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Influence of chronotype on daily mood fluctuations: pilot study in patients with depression

BJPsych Open ◽

10.1192/bjo.2019.103 ◽

2020 ◽

Vol 6 (2) ◽

Author(s):

Konstantin F. Brückmann ◽

Jürgen Hennig ◽

Matthias J. Müller ◽

Stanislava Fockenberg ◽

Anne-Marthe Schmidt ◽

...

Keyword(s):

Clinical Trials ◽

Pilot Study ◽

Negative Affect ◽

Clinical Setting ◽

Early Morning ◽

Healthy Controls ◽

Patient Subgroup ◽

Depression Risk ◽

Gender And Age ◽

The Relationship

Summary Depression risk is associated with a late chronotype pattern often described as an ‘evening chronotype’. Fluctuations in mood over consecutive days have not yet been measured according to chronotype in in-patients with depression. A total of 30 in-patients with depression and 32 healthy controls matched for gender and age completed a chronotype questionnaire and twice-daily ratings on mood for 10 consecutive days (registered in the German Clinical Trials Register: DRKS00010215). The in-patients had Saturdays and Sundays as hospital-leave days. The relationship between chronotype and daily mood was mediated by the weekday–weekend schedule with higher levels of negative affect in the evening-chronotype patient subgroup at weekends. Results are discussed with respect to a probably advantageous standardised clinical setting with early morning routines, especially for patients with evening chronotypes.

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765 Anxiety and sleep in Generalized Anxiety Disorder with and without Insomnia Disorder

SLEEP ◽

10.1093/sleep/zsab072.762 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A298-A298

Author(s):

Augustus Kram Mendelsohn ◽

Carolina Daffre ◽

Katelyn Oliver ◽

Jeehye Seo ◽

Natasha Lasko ◽

...

Keyword(s):

Anxiety Disorder ◽

Generalized Anxiety Disorder ◽

Anxiety Sensitivity ◽

Slow Wave Sleep ◽

Stress Test ◽

Generalized Anxiety ◽

Poor Sleep ◽

Sensitivity Index ◽

Somatic Anxiety ◽

Insomnia Disorder

Abstract Introduction Insomnia Disorder (ID) elevates risk of incident anxiety disorders and vice versa. We examined whether ID and poor sleep are associated with greater self-reported anxiety in persons with Generalized Anxiety Disorder (GAD). Methods Twenty-one participants with GAD and ID (GAD+/ID+) having Insomnia Severity Index (ISI) scores ≥ 13 (mean 17.8, SD 3.6) and 14 with GAD but not ID (GAD+/ID-) having ISI scores ≤ 12 (mean 6.4, SD 3.4) completed 14 days of actigraphy and sleep diaries as well as a night of ambulatory polysomnography (PSG) following an acclimation night. Participants completed the Pittsburgh Sleep Quality Index (PSQI), the State-Trait Inventory for Cognitive and Somatic Anxiety (STICSA-T/C, -T/S), the Ford Insomnia Response to Stress Test (FIRST), the Penn State Worry Questionnaire (PSWQ), and the Anxiety Sensitivity Index (ASI). Differences in self-reported anxiety (STICSA, ASI, PSWQ) between GAD+/ID+ and GAD+/ID- were analyzed using t-tests. Relationships of anxiety with retrospective (PSQI, FIRST, ISI), longitudinal (actigraphy, diaries) and physiological (PSG) sleep variables were analyzed using simple regression. Results GAD+/ID+ versus GAD+/ID- participants showed trends toward higher anxiety on the PSWQ (p=0.075), ASI (p=0.072) and STICSA-T/S (p=0.078). PSQI scores were positively associated with STICSA-T/S, (R=0.417, p=0.018, N=32). Greater insomnia reactivity (FIRST) was associated with increased worry on the PSWQ (R=0.352, p=0.044, N=33). STICSA-T/C was negatively associated with mean diary (R= -0.440, p=0.015, N=30) and actigraph (R= -0.517, p=0.01, N=24) total sleep time (TST). Actigraph mean TST trended toward lower PSWQ (R= -0.376, p=0.058, N=26) while actigraph mean sleep efficiency (SE) trended toward lesser STICSA-T/C (R= -0.397, p=0.058). Greater REM% was associated with greater STICSA-T/C (R=0.613, p=0.0005, N=28) and STICSA-T/S (R=0.516, p=0.005), a relationship also seen in GAD+/ID+ alone (p=0.03 and 0.015 respectively, N=16). Slow Wave Sleep% (SWS%) was not associated with lesser STICSA-T/S across both groups (p=0.14) but was so in GAD+/ID+ (R= -0.539, p=0.031, N=16). Conclusion GAD+/ID+ versus GAD+/ID-, show greater worry, anxiety sensitivity and somatic anxiety. In GAD, shorter and poorer quality sleep measured retrospectively or averaged longitudinally, as well as greater REM%, are associated with greater somatic and cognitive anxiety. Among those with ID, greater SWS% is associated with less somatic anxiety. Support (if any) R21MH115279, R01MH109638

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Endogenous memory reactivation during sleep in humans is clocked by slow oscillation-spindle complexes

Nature Communications ◽

10.1038/s41467-021-23520-2 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Thomas Schreiner ◽

Marit Petzka ◽

Tobias Staudigl ◽

Bernhard P. Staresina

Keyword(s):

Memory Function ◽

Nrem Sleep ◽

Stimulus Category ◽

Learning Material ◽

Memory Reactivation ◽

Eeg Recordings ◽

Surface Eeg ◽

Function Of Sleep ◽

Prior Experiences

AbstractSleep is thought to support memory consolidation via reactivation of prior experiences, with particular electrophysiological sleep signatures (slow oscillations (SOs) and sleep spindles) gating the information flow between relevant brain areas. However, empirical evidence for a role of endogenous memory reactivation (i.e., without experimentally delivered memory cues) for consolidation in humans is lacking. Here, we devised a paradigm in which participants acquired associative memories before taking a nap. Multivariate decoding was then used to capture endogenous memory reactivation during non-rapid eye movement (NREM) sleep in surface EEG recordings. Our results reveal reactivation of learning material during SO-spindle complexes, with the precision of SO-spindle coupling predicting reactivation strength. Critically, reactivation strength (i.e. classifier evidence in favor of the previously studied stimulus category) in turn predicts the level of consolidation across participants. These results elucidate the memory function of sleep in humans and emphasize the importance of SOs and spindles in clocking endogenous consolidation processes.

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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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324 Naturalistic Characterization of Sleep in Chronic Insomnia Using a Non-Contact Sleep Measurement Device

SLEEP ◽

10.1093/sleep/zsab072.323 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A130-A130

Author(s):

Devon Hansen ◽

Mary Peterson ◽

Roy Raymann ◽

Hans Van Dongen ◽

Nathaniel Watson

Keyword(s):

Sleep Onset ◽

Contact Device ◽

Chronic Insomnia ◽

Poor Sleep ◽

Measurement Device ◽

Home Setting ◽

Time In Bed ◽

Sleep Measurement ◽

Measurement Devices

Abstract Introduction Individuals with insomnia report poor sleep quality and non-restorative sleep, and often exhibit irregular sleep patterns over days and weeks. First night effects and logistical challenges make it difficult to measure these sleep characteristics in the laboratory. Also, sensitivity to sleep disruption from obtrusive measurement devices confounds sleep measurements in people with insomnia in their naturalistic setting. Non-contact sleep measurement devices have the potential to address these issues and enable ecologically valid, longitudinal characterization of sleep in individuals with insomnia. Here we use a non-contact device – the SleepScore Max (SleepScore Labs) – to assess the sleep of individuals with chronic insomnia, compared to healthy sleeper controls, in their home setting. Methods As part of a larger study, 13 individuals with chronic insomnia (ages 25-60y, 7 males) and 8 healthy sleeper controls (ages 21-46y, 6 females) participated in an at-home sleep monitoring study. Enrollment criteria included an age range of 18-65y and, for the insomnia group, ICSD-3 criteria for chronic insomnia with no other clinically relevant illness. Participants used the non-contact sleep measurement device to record their sleep periods each night for 8 weeks. Sleep measurements were analyzed for group differences in both means (characterizing sleep overall) and within-subject standard deviations (characterizing sleep variability across nights), using mixed-effects regression controlling for systematic between-subject differences. Results Based on the non-contact sleep measurements, individuals with chronic insomnia exhibited greater variability in bedtime, time in bed, total sleep time, sleep latency, total wake time across time in bed, wakefulness after sleep onset, sleep interruptions, and estimated light sleep, compared to healthy sleeper controls (all F>5.7, P<0.05). No significant differences were found for group averages and for variability in estimated deep and REM sleep. Conclusion In this group of individuals with chronic insomnia, a non-contact device used to characterize sleep naturalistically captured enhanced variability across nights in multiple aspects of sleep stereotypical of sleep disturbances in chronic insomnia, differentiating the sample statistically significantly from healthy sleeper controls. Support (if any) NIH grant KL2TR002317; research devices provided by SleepScore Labs.

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