The impact of chronic primary insomnia on the heart rate – EEG variability link

ObjectiveTo determine if chronic insomnia alters the relationship between heart rate variability and delta sleep determined at the EEG.MethodsAfter one night of accommodation, polysomnography was performed in 14 male patients with chronic primary insomnia matched with 14 healthy men. ECG and EEG recordings allowed the determination of High Frequency (HF) power of RR-interval and delta sleep EEG power across the first three Non Rapid Eye Movement (NREM)–REM cycles. Interaction between normalized HF RR-interval variability and normalized delta sleep EEG power was studied by coherency analysis.ResultsPatients showed increased total number of awakenings, longer sleep latency and wake durations and shorter sleep efficiency and REM duration than controls (p < .01). Heart rate variability across first three NREM–REM cycles and sleep stages (NREM, REM and awake) were similar between both groups. In each group, normalized HF variability of RR-interval decreased from NREM to both REM and awake. Patients showed decreased linear relationship between normalized HF RR-interval variability and delta EEG power, expressed by decreased coherence, in comparison to controls (p < .05). Gain and phase shift between these signals were similar between both groups.ConclusionsInteraction between changes in cardiac autonomic activity and delta power is altered in chronic primary insomniac patients, even in the absence of modifications in heart rate variability and cardiovascular diseases.SignificanceThis altered interaction could reflect the first step to cardiovascular disorders.     

Task switching in older adults with and without insomnia

BackgroundTask-switching deficits are common in older adults and in those with insomnia. Such deficits may be driven by difficulties with sleep continuity and dampened homeostatic sleep drive.ObjectiveTo identify the aspects of task switching affected by insomnia and its treatment, and to determine whether such effects are associated with sleep continuity and homeostatic sleep drive.MethodsPolysomnographic sleep and task switching were tested in healthy older adults aged 60–93 years with insomnia (n = 48) and normal sleeping controls (n = 51). Assessments were repeated in the insomnia group after eight weeks of cognitive behavioral treatment for insomnia. Sleep measures included wake after sleep onset (WASO) and quantitative indices of homeostatic sleep drive (delta power during nonrapid eye movement [NREM] sleep and the ratio of delta power during the first and second NREM periods). A cued task-switching paradigm instructed participants to perform one of two tasks with varying preparatory cue-target intervals, manipulating task alternation, task repetition, and task preparation.ResultsThe effect of preparatory cues on accuracy was diminished in the insomnia group compared with that in controls. Across the two groups, a stronger effect of preparatory cues was associated with a higher delta sleep ratio. Following insomnia treatment, task-repetition accuracy significantly improved. This improvement was associated with improvements in WASO. There were no group or treatment effects on response time or task alternation accuracy.ConclusionsEffects of insomnia diagnosis and treatment apply to conditions that depend on the maintenance of a task set, rather than a domain general effect across task switching. Such effects are associated with homeostatic sleep drive and sleep continuity.     

Effects of suvorexant on sleep architecture and power spectral profile in patients with insomnia: analysis of pooled phase 3 data

BackgroundThe orexin receptor antagonist, suvorexant, is approved for treating insomnia at a maximum dose of 20 mg. We evaluated its effects on sleep architecture.MethodsThe analyses included pooled polysomnography data from two similar randomized, double-blind, placebo-controlled, 3-month trials evaluating two age-adjusted (non-elderly/elderly) dose regimes of 20/15 mg and 40/30 mg in 1482 patients with insomnia. Polysomnography was recorded at baseline and on three nights during the treatment: Night-1, Month-1, and Month-3. Effects on non-REM sleep stages 1 (N1), 2 (N2), 3 (N3)/slow wave sleep (SWS), and REM sleep were evaluated. A power spectral analysis of non-REM sleep was also performed.ResultsSuvorexant increased the time (in minutes) spent in all sleep stages compared with placebo. When suvorexant and placebo were compared in terms of changes in percentage of total sleep time spent in each stage, there were small decreases of ≤1%, ≤2.2%, and ≤0.8% for N1, N2, and N3/SWS on average, respectively, and an average increase of ≤3.9% in REM. The largest differences from placebo were observed at Night-1 and generally diminished over time. Suvorexant reduced REM latency (number of non-REM 30-s epochs from lights-off to the first REM epoch) compared with placebo; the reduction was greater at Night-1 (~40–50 non-REM epochs) in comparison to later time points (~12–25 non-REM epochs at Month-3). The spectral analysis of non-REM showed a small decrease in power of 3–6% in the gamma and beta bands, and a small increase of 4–8% in the delta band, at Night-1 for suvorexant relative to placebo; these effects were not apparent at the later Month-1 and Month-3 time points.ConclusionOverall sleep architecture appears to be preserved in insomnia patients taking suvorexant. The power spectral profile of suvorexant is generally similar to placebo.     

Sleep disturbances in schizophrenia spectrum and bipolar disorders – a transdiagnostic perspective

BackgroundSleep disturbances are prevalent in severe mental disorders but their type and frequency across diagnostic categories has not been investigated in large scale studies.MethodsParticipants with Schizophrenia spectrum disorders (SCZ, (N = 617)), Bipolar disorders (BD, (N = 440)), and Healthy Controls (HC, (N = 173)) were included in the study. Sleep disturbances (insomnia, hypersomnia and delayed sleep phase) were identified based on items from the Inventory of Depressive Symptoms – Clinician rated scale. Clinical symptoms were assessed with the Positive and Negative Syndrome scale and level of functioning with the Global assessment of Functioning scale.ResultsThe rate of any sleep disturbance was 78% in SZ, 69% in BD and 39% in HC. Insomnia was the most frequently reported sleep disturbance across all groups. Both diagnostic groups reported significantly more of any sleep disturbances than HC (P < 0.001). Having a sleep disturbance was associated with more severe negative and depressive symptoms and with lower functioning across diagnostic groups (P < 0.001, η² = 0.0071). Hypersomnia was the only sleep disturbance associated with previous treatment history.ConclusionSleep disturbances, including insomnia, hypersomnia and delayed sleep phase, are frequent in SCZ and BD, and associated with more severe clinical symptomatology across diagnostic groups. This suggests that sleep disturbance is a clinically relevant transdiagnostic phenomenon.     

Spectral characteristics of sleep EEG in chronic insomnia

To determine whether the spectral characteristics of the sleep electroencephalogram (EEG) of insomniacs differ from that of healthy subjects, we compared in each of the first four non-rapid eye movement (NREM) and rapid eye movement (REM) episodes: (a) the time courses of absolute power, averaged over the subjects in each group, for the delta, theta, alpha, sigma and beta frequency bands; (b) the relationship between these time courses; and (c) the overnight trend of integrated power in each frequency band. The results show that NREM power, for all frequencies below the beta range, has slower rise rates and reaches lower levels in the insomniac group, whereas beta power is significantly increased. In REM, insomniacs show lower levels in the delta and theta bands, whereas power in the faster frequency bands is significantly increased. Thus, the pathophysiology of insomnia is characterized not only by the generally acknowledged slow wave deficiency, but also by an excessive hyperarousal of the central nervous system throughout the night, affecting both REM and NREM sleep. This hyperarousal is interpreted in terms of the neuronal group theory of sleep which provides a possible explanation for the discrepancies observed between subjective impressions and objective measures of sleep. Also, it is suggested that the progressive hyperpolarization of the thalamocortical neurons as sleep deepens is slower in the patient population and that this may explain the observed slow wave deficiency. The homeostatic control of slow wave activity, on the other hand, would appear to be intact in the patient population.     

Periodic limb movements both in non-REM and REM sleep: Relationships between cerebral and autonomic activities

ObjectiveTo investigate the temporal relationship between cerebral and autonomic activities before and during periodic limb movements in NREM and REM sleep (PLMS).MethodsPatterns of EEG, cardiac and muscle activities associated with PLMS were drawn from polysomnographic recordings of 14 outpatients selected for the presence of PLMS both in NREM and REM sleep. PLMS were scored during all sleep stages from tibial EMG. Data from a bipolar EEG channel were analyzed by wavelet transform. Heart rate (HR) was evaluated from the electrocardiogram. EEG, HR and EMG activations were detected as transient increase of signal parameters and examined by analysis of variance and correlation analysis independently in NREM and REM sleep. Homologous parameters in REM and NREM sleep were compared by paired t-test.ResultsThe autonomic component, expressed by HR increase, took place before the motor phenomenon both in REM and NREM sleep, but it was significantly earlier during NREM. In NREM sleep, PLM onset was heralded by a significant activation of delta-EEG, followed by a progressive increase of all the other bands. No significant activations of delta EEG were found in REM sleep. HR and EEG activations positively correlated with high frequency EEG activations and negatively (in NREM) with slow frequency ones.ConclusionsOur findings suggested a heralding role for delta band only in NREM sleep and for HR during both NREM and REM sleep. Differences in EEG and HR activation between REM and NREM sleep and correlative data suggested a different modulation of the global arousal response.SignificanceIn this study, time–frequency analysis and advanced statistical methods enabled an accurate comparison between brain and autonomic changes associated to PLM in NREM and REM sleep providing indications about interaction between autonomic and slow and fast EEG components of arousal response.     

Sleep spindle activity correlates with implicit statistical learning consolidation in untreated obstructive sleep apnea patients

Objective/BackgroundThe aim of this study was to examine the relationship between overnight consolidation of implicit statistical learning with spindle frequency EEG activity and slow frequency delta power during non-rapid eye movement (NREM) sleep in obstructive sleep apnea (OSA).Patients/MethodsForty-seven OSA participants completed the experiment. Prior to sleep, participants performed a reaction time cover task containing hidden patterns of pictures, about which participants were not informed. After the familiarisation phase, participants underwent overnight polysomnography. 24 h after the familiarisation phase, participants performed a test phase to assess their learning of the hidden patterns, expressed as a percentage of the number of correctly identified patterns. Spindle frequency activity (SFA) and delta power (0.5–4.5 Hz), were quantified from NREM electroencephalography. Associations between statistical learning and sleep EEG, and OSA severity measures were examined.ResultsSFA in NREM sleep in frontal and central brain regions was positively correlated with statistical learning scores (r = 0.41 to 0.31, p = 0.006 to 0.044). In multiple regression, greater SFA and longer sleep onset latency were significant predictors of better statistical learning performance. Delta power and OSA severity were not significantly correlated with statistical learning.ConclusionsThese findings suggest spindle activity may serve as a marker of statistical learning capability in OSA. This work provides novel insight into how altered sleep physiology relates to consolidation of implicitly learnt information in patients with moderate to severe OSA.     

Relationship between delta power and the electrocardiogram-derived cardiopulmonary spectrogram: possible implications for assessing the effectiveness of sleep

ObjectivesThe physiologic relationship between slow-wave activity (SWA) (0–4 Hz) on the electroencephalogram (EEG) and high-frequency (0.1–0.4 Hz) cardiopulmonary coupling (CPC) derived from electrocardiogram (ECG) sleep spectrograms is not known. Because high-frequency CPC appears to be a biomarker of stable sleep, we tested the hypothesis that that slow-wave EEG power would show a relatively fixed-time relationship to periods of high-frequency CPC. Furthermore, we speculated that this correlation would be independent of conventional nonrapid eye movement (NREM) sleep stages.MethodsWe analyzed selected datasets from an archived polysomnography (PSG) database, the Sleep Heart Health Study I (SHHS-I). We employed the cross-correlation technique to measure the degree of which 2 signals are correlated as a function of a time lag between them. Correlation analyses between high-frequency CPC and delta power (computed both as absolute and normalized values) from 3150 subjects with an apnea-hypopnea index (AHI) of ⩽5 events per hour of sleep were performed.ResultsThe overall correlation (r) between delta power and high-frequency coupling (HFC) power was 0.40 ± 0.18 (P = .001). Normalized delta power provided improved correlation relative to absolute delta power. Correlations were somewhat reduced in the second half relative to the first half of the night (r = 0.45 ± 0.20 vs r = 0.34 ± 0.23). Correlations were only affected by age in the eighth decade. There were no sex differences and only small racial or ethnic differences were noted.ConclusionsThese results support a tight temporal relationship between slow wave power, both within and outside conventional slow wave sleep periods, and high frequency cardiopulmonary coupling, an ECG-derived biomarker of “stable” sleep. These findings raise mechanistic questions regarding the cross-system integration of neural and cardiopulmonary control during sleep.     

A preliminary study of slow-wave EEG activity and insulin sensitivity in adolescents

ObjectiveThe objective was to evaluate the relationship between the time course of slow wave EEG activity (SWA) during NREM sleep and insulin sensitivity in adolescents.MethodsNine normal weight and nine overweight (BMI > 85th percentile) adolescents (13–18 years of age) participated. None of the participants had a history of sleep disordered breathing, confirmed by sleep study. Participants maintained a regularized sleep wake cycle for five days followed by overnight polysomnography in the lab or at home. An oral glucose tolerance test (OGTT) was administered after a 12 h fast and within two weeks of the sleep study. Whole body insulin sensitivity (WBISI) and homeostasis model assessment (HOMA-IR) determined insulin resistance. Power spectral analysis quantified slow-wave EEG activity (.05–3.9 Hz) and exponential regression evaluated SWA across successive NREM periods.ResultsThose who were insulin resistant and had low insulin sensitivity had less Stages 2, 3 and 4 of NREM sleep, more Stage 1, but did not sleep less than those with low resistance and high sensitivity. SWA power was significantly lower in the first NREM period and the decay rate of SWA across NREM sleep was significantly slower in the low insulin sensitivity group. Similar results were obtained after removing the influence of BMI and Tanner score.ConclusionsInsulin sensitivity in adolescents is related to SWA power and its time course, not total sleep time, regardless of BMI.     

Let there be no light: the effect of bedside light on sleep quality and background electroencephalographic rhythms

ObjectivesArtificial lighting has been beneficial to society, but unnecessary light exposure at night may cause various health problems. We aimed to investigate how whole-night bedside light can affect sleep quality and brain activity.Patients and methodsTen healthy sleepers underwent two polysomnography (PSG) sessions, one with the lights off and one with the lights on. PSG variables related to sleep quality were extracted and compared between lights-off and lights-on sleep. Spectral analysis was performed to rapid eye movement (REM) sleep and non-REM (NREM) sleep epochs to reveal any light-induced differences in background brain rhythms.ResultsLights-on sleep was associated with increased stage 1 sleep (N1), decreased slow-wave sleep (SWS), and increased arousal index. Spectral analysis revealed that theta power (4–8 Hz) during REM sleep and slow oscillation (0.5–1 Hz), delta (1–4 Hz), and spindle (10–16 Hz) power during NREM sleep were decreased in lights-on sleep conditions.ConclusionsSleeping with the light on not only causes shallow sleep and frequent arousals but also has a persistent effect on brain oscillations, especially those implicated in sleep depth and stability. Our study demonstrates additional hazardous effect of light pollution on health.     

Correlation between electroencephalography and heart rate variability during sleep

It is known that autonomic nervous activities change in correspondence with sleep stages. However, the characteristics of continuous fluctuations in nocturnal autonomic nerve tone have not been clarified in detail. The study aimed to determine the possible correlation between the electroencephalogram (EEG) and autonomic nervous activities, and to clarify in detail the nocturnal fluctuations in autonomic nerve activities. Overnight EEGs and electrocardiograms of seven healthy males were obtained. These EEGs were analyzed by fast Fourier transformation algorithm to extract delta, sigma and beta power. Heart rate and heart rate variability (HRV) were calculated in consecutive 5-min epochs. The HRV indices of low frequency (LF), high frequency (HF) and LF/HF ratio were calculated from the spectral analysis of R-R intervals. The sleep stages were manually scored according to Rechtschaffen and Kales' criteria. Low frequency and LF/HF were significantly lower during non-rapid eye movement (NREM) than REM, and were lower in stages 3 and 4 than in stages 1 and 2. Furthermore, delta EEG showed inverse correlations with LF (r = - 0.44, P < 0.001) and LF/HF (r = - 0.41, P < 0.001). In contrast, HF differed neither between REM and NREM nor among NREM sleep stages. Detailed analysis revealed that correlation was evident from the first to third NREM, but not in the fourth and fifth NREM. Delta EEG power showed negative correlations with LF and LF/HF, suggesting that sympathetic nervous activities continuously fluctuate in accordance with sleep deepening and lightening.     

Spectral analysis of the sleep onset period in primary insomnia

ObjectiveTo compare the EEG power spectra characteristics of the sleep onset period (SOP) in patients with sleep onset insomnia (SOI), sleep maintenance insomnia (SMI) and good sleepers (GS).MethodsThe time course of EEG power density (1–40 Hz) during the SOP was examined in thirty subjects (SOI patients: N = 10, SMI patients: N = 10, GS: N = 10).ResultsThe EEG power of the beta2 frequency band (18–29.75 Hz) was significantly lower in SOI than in SMI in the period preceding sleep onset. The alpha power was significantly higher for the SMI group compared to GS before sleep onset. Despite the lack of statistical significance, several differences in EEG dynamics were observed in SOI compared to two other groups: delta power increased slower after sleep onset; beta2 and 3 (18–29.75 and 30–39.75 Hz) power decrease less abruptly before sleep onset; beta1 (15–17.75 Hz) power increase through the whole SOP.ConclusionsThe lower level of beta2 frequency band in SOI and the differences in dynamics in delta and beta bands may suggest that a mechanism other than hyperarousal participates in etiology of SOI.SignificanceSOI and SMI patients have different spectral characteristics in SOP, thus future studies should avoid the inclusion of mixed insomnia samples.     

不同病程原发性失眠症患者非快速眼运动睡眠δ波对比分析

目的 对比分析不同病程原发性失眠症患者非快速眼运动（Non Rapid Eye Movement,NREM）睡眠δ波分布特点.方法 按照美国精神病学会制定的《精神障碍诊断和统计手册（DSM-Ⅳ）》关于失眠病程划分标准,收集急性原发性失眠（API）组82例,亚急性原发性失眠（SPI）组80例,慢性原发性失眠（CPI）组85例,正常睡眠组（NS）30例.并检测4组受试者正常作息条件及睡眠限制条件下非快速眼运动睡眠δ波分布情况.结果 正常作息条件下,API组、SPI组及CPI组患者与NS组比较均存在非快速眼运动睡眠δ波减少（P＜0.05）,API组、SPI组及CPI组患者之间δ波对比差异无统计学意义（P＞0.05）.在睡眠限制条件下,API组、SPI组非快速眼运动睡眠δ波与NS组比较差异无统计学意义（P＞0.05）,CPI组δ波小于NS组（P＜0.05）.结论 API组、SPI组及CPI组患者均存在睡眠平衡压力不足情况,睡眠限制能提高API组及SPI组睡眠平衡压力,但无法提高CPI组患者睡眠平衡压力.     

Identifying the best sleep measure to screen clinical insomnia in a psychiatric population

BackgroundInsomnia symptoms are highly prevalent among patients with psychiatric disorders, and this mandates the need to identify the best self-administered sleep measure to screen for clinical insomnia among them.MethodsA total of 400 psychiatric outpatients completed the Pittsburgh Sleep Quality Index, Insomnia Severity Index (ISI), Epworth Sleepiness Scale, Flinders Fatigue Scale, Functional Outcomes of Sleep Questionnaire, and Dysfunctional Beliefs and Attitudes about Sleep Scale in a cross-sectional study. The sensitivity, specificity, positive, and negative predictive values of these six sleep scales were assessed and compared in relation to both ICD-10 and DSM-5 insomnia disorder status established using the interviewer-administered Brief Insomnia Questionnaire.ResultsReceiver operator characteristic curves with the area under the curve (AUC) revealed the ISI to be the most accurate measure to discriminate cases and non-cases on both ICD-10 (AUC = 0.88, 95% CI = 0.84–0.92) and DSM-5 (AUC = 0.82, 95% CI = 0.78–0.86) criteria with “good” accuracy. The cut-off scores of ≥14 and ≥ 11 for the ISI provided optimal sensitivity and specificity for the detection of ICD-10 and DSM-5 insomnia, respectively.DiscussionWith the new calling from DSM-5 to treat sleep symptoms in the presence of a co-existing mental condition, early detection of psychiatric patients with clinically significantly insomnia using a simple but accurate self-report sleep measure becomes important. Our study suggests that the ISI could be used as a potential screening tool for comorbid insomnia disorder in patients with mental disorders.     

Sleep spindles may predict response to cognitive-behavioral therapy for chronic insomnia

BackgroundWhile cognitive-behavioral therapy for insomnia constitutes the first-line treatment for chronic insomnia, only few reports have investigated how sleep architecture relates to response to this treatment. In this pilot study, we aimed to determine whether pre-treatment sleep spindle density predicts treatment response to cognitive-behavioral therapy for insomnia.MethodsTwenty-four participants with chronic primary insomnia participated in a 6-week cognitive-behavioral therapy for insomnia performed in groups of 4–6 participants. Treatment response was assessed using the Pittsburgh Sleep Quality Index and the Insomnia Severity Index measured at pre- and post-treatment, and at 3- and 12-months' follow-up assessments. Secondary outcome measures were extracted from sleep diaries over 7 days and overnight polysomnography, obtained at pre- and post-treatment. Spindle density during stage N2–N3 sleep was extracted from polysomnography at pre-treatment. Hierarchical linear modeling analysis assessed whether sleep spindle density predicted response to cognitive-behavioral therapy.ResultsAfter adjusting for age, sex, and education level, lower spindle density at pre-treatment predicted poorer response over the 12-month follow-up, as reflected by a smaller reduction in Pittsburgh Sleep Quality Index over time. Reduced spindle density also predicted lower improvements in sleep diary sleep efficiency and wake after sleep onset immediately after treatment. There were no significant associations between spindle density and changes in the Insomnia Severity Index or polysomnography variables over time.ConclusionThese preliminary results suggest that inter-individual differences in sleep spindle density in insomnia may represent an endogenous biomarker predicting responsiveness to cognitive-behavioral therapy. Insomnia with altered spindle activity might constitute an insomnia subtype characterized by a neurophysiological vulnerability to sleep disruption associated with impaired responsiveness to cognitive-behavioral therapy.     

Smart Phone/Ecological Momentary Assessment of Sleep and Daytime Symptoms Among Older Adults With Insomnia

ObjectivesTo employ smart phone/ecological momentary assessment (EMA) methods to evaluate the impact of insomnia on daytime symptoms among older adults.DesignProspective cohort studySettingAcademic medical centerParticipantsTwenty-nine older adults with insomnia (M age = 67.5 ± 6.6 years, 69% women) and 34 healthy sleepers (M age = 70.4 ± 5.6 years, 65% women).MeasurementsParticipants wore an actigraph, completed daily sleep diaries, and completed the Daytime Insomnia Symptoms Scale (DISS) via smart phone 4x/day for 2 weeks (i.e., 56 survey administrations across 14 days).ResultsRelative to healthy sleepers, older adults with insomnia demonstrated more severe insomnia symptoms in all DISS domains (alert cognition, positive mood, negative mood, and fatigue/sleepiness). A series of mixed model analyses were performed using the Benjamini-Hochberg procedure for correcting false discovery rate (BH-FDR) and an adjusted p-value <0.05. Among older adults with insomnia, all five prior-night sleep diary variables (sleep onset latency, wake after sleep onset, sleep efficiency, total sleep time, and sleep quality) were significantly associated with next-day insomnia symptoms (i.e., all four DISS domains). The median, first and third quintiles of the effect sizes (R2) of the association analyses were 0.031 (95% confidence interval (CI: [0.011,0.432]), 0.042(CI: [0.014,0.270]), 0.091 (CI:[0.014,0.324]).ConclusionResults support the utility of smart phone/EMA assessment among older adults with insomnia. Clinical trials incorporating smart phone/EMA methods, including EMA as an outcome measure, are warranted.     

Comparison of impact of insomnia on depression and quality of life in restless legs syndrome/Willis–Ekbom disease and primary insomnia patients

BackgroundAlthough insomnia is common among people with restless legs syndrome (RLS), its impact on the daily suffering of those with RLS remains unclear. This study aimed to compare the differential impact of clinical insomnia on depression and quality of life (QoL) among people with RLS, primary insomnia, and healthy controls.MethodsA total of 148 people with RLS, 115 with primary insomnia, and 117 healthy controls were enrolled into this cross-sectional study. Participants completed sleep, depression, and QoL questionnaires. Clinical insomnia was defined as Korean version of the Insomnia Severity Index (K-ISI) ≥ 15. Correlation coefficients between sleep measures and both depression and QoL were calculated. Multivariate regression was used to identify the clinical factors that were most closely associated with depression and QoL among people with RLS and primary insomnia.ResultsParticipants with RLS had insomnia and sleep quality at intermediate levels between the healthy controls and primary insomnia subjects, but those with clinical insomnia had equivalent depression and QoL scores regardless of RLS diagnosis. Insomnia severity correlated with depression and QoL in RLS and primary insomnia. Multivariate regression, however, revealed that RLS severity was the most overall predictive factor for depression and QoL among those with RLS. Insomnia severity was the strongest predictor in primary insomnia.ConclusionInsomnia was more closely associated with depression and QoL among people with primary insomnia than those with RLS, but clinical insomnia may have a significant impact in RLS as well. Future RLS studies should account for sleep quality in addition to RLS symptom severity when investigating mood and QoL.     

Longitudinal assessment of NREM sleep EEG in typically developing and medication-free ADHD adolescents: first year results

ObjectiveClinical observation and structural MRI studies suggest that delayed brain maturation is a major cause of attention deficit hyperactivity disorder (ADHD). Sleep electroencephalogram (EEG) which exhibits major changes across adolescence provides an opportunity to investigate brain electrophysiology evidence for maturational delay. We present data from an ongoing longitudinal study of sleep EEG in medication-free ADHD and typically developing adolescents to investigate brain electrophysiological evidence for this maturational delay.MethodsNine adolescents diagnosed with ADHD (combined presentation, DSM-V criteria, mean age 12.39 ± 0.61 years, 2 females), and nine typically developing controls (12.08 ± 0.35 years, 4 females) were recruited. Subjects underwent an adaptation night and all night polysomnography twice yearly at the Laboratory.ResultsBasic sleep structure did not differ between the ADHD and control groups. In addition, we found no group differences on delta power (p = 0.77), but found a possible trend toward higher theta power (p = 0.057) for the ADHD group. The decline of standardized delta power across the 4 non-rapid eye movement (NREM) periods differed by group (p < 0.05) with the percent delta power in the first NREM period being lower in the ADHD group.ConclusionsOur data support the preponderant evidence that basic sleep structure is unaltered with ADHD. Our data do suggest altered sleep homeostatic recuperative processes in ADHD. The theta findings from the first two recordings are suggestive of a maturational delay associated with ADHD, but follow-up data-points are needed.     

Sleep and risk for high blood pressure and hypertension in midlife women: the SWAN (Study of Women’s Health Across the Nation) Sleep Study

ObjectiveInadequate self-reported sleep is related to high blood pressure (BP). Our study investigated cross-sectional and longitudinal relationships between poor sleep measured by in-home polysomnography (PSG) and BP.MethodsMidlife participants (132 black, 164 white, and 59 Chinese) were from the SWAN (Study of Women’s Health Across the Nation) ancillary sleep study. In-home PSG measured sleep apnea, duration, efficiency, and electroencephalogram (EEG) total delta and beta power during nonrapid eye movement (NREM) sleep. Women subsequently were followed annually for 4.5 (1–7) years for BP and hypertensive status (>140/90 mmHg or use of antihypertensive medication). Covariates were age, race, site, and educational attainment, with time-covariates of BP medications, body mass index, diabetes mellitus (DM), cigarette smoking, and menopausal status.ResultsSleep duration and efficiency were unrelated to BP cross-sectionally or longitudinally in multivariate models. Women with higher total beta power were more likely to be hypertensive at the time of the sleep study; women with lower total delta power were more likely to show increases in diastolic BP (DBP) and to be at risk for incident hypertension across follow-up.ConclusionsLow NREM delta power may be a risk factor for future hypertension. Quantitative EEG measures are worthy of future investigations of hypertension risk.