Sleep and circadian rhythms in mood disorders.

The impact of chronic hepatitis C infection on the circadian clock and sleep

Univ Maryland, Dept Pharmacol & Expt Therapeut, Sch Med, Baltimore, MD 21201 USA

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Background: Young people living with 22q11.2 Deletion Syndrome (22q11.2DS) are at increased risk of schizophrenia, intellectual disability, attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). In common with these conditions, 22q11.2DS is also associated with sleep problems. We investigated whether abnormal sleep or sleep-dependent network activity in 22q11.2DS reflects convergent, early signatures of neural circuit disruption also evident in associated neurodevelopmental conditions. Methods: In a cross-sectional design, we recorded high-density sleep EEG in young people (6–20 years) with 22q11.2DS (n=28) and their unaffected siblings (n=17), quantifying associations between sleep architecture, EEG oscillations (spindles and slow waves) and psychiatric symptoms. We also measured performance on a memory task before and after sleep. Results: 22q11.2DS was associated with significant alterations in sleep architecture, including a greater proportion of N3 sleep and lower proportions of N1 and REM sleep than in siblings. During sleep, deletion carriers showed broadband increases in EEG power with increased slow-wave and spindle amplitudes, increased spindle frequency and density, and stronger coupling between spindles and slow-waves. Spindle and slow-wave amplitudes correlated positively with overnight memory in controls, but negatively in 22q11.2DS. Mediation analyses indicated that genotype effects on anxiety, ADHD and ASD were partially mediated by sleep EEG measures. Conclusions: This study provides a detailed description of sleep neurophysiology in 22q11.2DS, highlighting alterations in EEG signatures of sleep which have been previously linked to neurodevelopment, some of which were associated with psychiatric symptoms. Sleep EEG features may therefore reflect delayed or compromised neurodevelopmental processes in 22q11.2DS, which could inform our understanding of the neurobiology of this condition and be biomarkers for neuropsychiatric disorders. Funding: This research was funded by a Lilly Innovation Fellowship Award (UB), the National Institute of Mental Health (NIMH 5UO1MH101724; MvdB), a Wellcome Trust Institutional Strategic Support Fund (ISSF) award (MvdB), the Waterloo Foundation (918-1234; MvdB), the Baily Thomas Charitable Fund (2315/1; MvdB), MRC grant Intellectual Disability and Mental Health: Assessing Genomic Impact on Neurodevelopment (IMAGINE) (MR/L011166/1; JH, MvdB and MO), MRC grant Intellectual Disability and Mental Health: Assessing Genomic Impact on Neurodevelopment 2 (IMAGINE-2) (MR/T033045/1; MvdB, JH and MO); Wellcome Trust Strategic Award 'Defining Endophenotypes From Integrated Neurosciences' Wellcome Trust (100202/Z/12/Z MO, JH). NAD was supported by a National Institute for Health Research Academic Clinical Fellowship in Mental Health and MWJ by a Wellcome Trust Senior Research Fellowship in Basic Biomedical Science (202810/Z/16/Z). CE and HAM were supported by Medical Research Council Doctoral Training Grants (C.B.E. 1644194, H.A.M MR/K501347/1). HMM and UB were employed by Eli Lilly & Co during the study; HMM is currently an employee of Boehringer Ingelheim Pharma GmbH & Co KG. The views and opinions expressed are those of the author(s), and not necessarily those of the NHS, the NIHR or the Department of Health funders. Editor's evaluation The authors quantified sleep oscillations and their coordination in young people with 22q11.2 Deletion Syndrome and their siblings. This was done to identify potential biomarkers of later neurodevelopmental diagnoses in 22q11.2 Deletion Syndrome. The core findings based on solid data demonstrate that sleep rhythms in 22q11.2DS are altered in comparison to the control group, as is their relationship with the behavioral expressions of memory consolidation. These are important findings as they directly provide a link between genes and sleep rhythms and memory consolidation. https://doi.org/10.7554/eLife.75482.sa0 Decision letter Reviews on Sciety eLife's review process Introduction 22q11.2 microdeletion syndrome (22q11.2DS) is caused by a deletion spanning a~2.6 megabase region on the long arm of chromosome 22. It occurs in ~1:3000–4000 births and is associated with increased risk of neuropsychiatric conditions including intellectual disability, autism spectrum disorder (ASD), attention-deficit hyperactivity disorder (ADHD), and epileptic seizures. (Cunningham et al., 2018; Eaton et al., 2019; Moulding et al., 2020; Niarchou et al., 2014). 22q11.2DS is also considered to be one of the largest biological risk factors for schizophrenia, with up to 41% of adults with 22q11.2DS having psychotic disorders (Karayiorgou et al., 1995; Monks et al., 2014; Schneider et al., 2014). However, the neurobiological mechanisms underlying psychiatric symptoms in 22q11.2DS remain unclear. Deep phenotyping of young people with 22q11.2DS may allow their elucidation and therefore enable early detection and/or intervention. The electroencephalogram (EEG) recorded during non-rapid eye movement (NREM) sleep features spindle and slow-wave (SW) oscillations: highly conserved and non-invasively measurable signatures of neuronal network activity generated by corticothalamic circuits (Adamantidis et al., 2019). The properties and co-ordination of these oscillations are candidate biomarkers of brain dysfunction in neuropsychiatric disorders (Ferrarelli and Tononi, 2017; Gardner et al., 2014; Manoach et al., 2016). Sleep EEG features are altered across many neurodevelopmental disorders, including schizophrenia, including first episode psychosis, as well as first degree relatives (Chouinard et al., 2004; Cohrs, 2008; Ferrarelli et al., 2007; Ferrarelli et al., 2010; Göder et al., 2014; Bartsch et al., 2019; Demanuele et al., 2017; Wamsley et al., 2012; Manoach and Stickgold, 2019; Castelnovo et al., 2018; Keshavan et al., 1998); ADHD (Cortese et al., 2009; Gorgoni et al., 2020; Lunsford-Avery et al., 2016), ASD although findings have been inconsistent (Gorgoni et al., 2020; Lehoux et al., 2019) and a range of rare genetic conditions, including Down syndrome, Fragile-X syndrome and Angelman syndrome (Angriman et al., 2015). We have recently shown that the majority of young people with 22q11.2DS have sleep problems, particularly insomnia and sleep fragmentation, that associate with psychopathology (Moulding et al., 2020). However, this analysis was based on parental report; the neurophysiological properties of sleep in this condition remain unexplored. Furthermore, it has been demonstrated that neuroanatomical features associated with psychopathology in 22q11.2DS significantly converge with those in idiopathic psychiatric disorders (Ching et al., 2020). Therefore, studying the sleep EEG in 22q11.2DS may produce insights that can be generalized to broader populations, affording a unique opportunity to clarify the relationship between sleep EEG and psychiatric risk. We hypothesized that 22q11.2DS would be associated with alterations in sleep EEG features relative to controls, including altered spindle and SW events, and aberrant spindle-SW coupling. We investigated these hypotheses in a cross-sectional study of young people with 22q11.2DS and unaffected sibling controls, combining detailed neuropsychiatric assessments with overnight high-density EEG recordings and a sleep-dependent memory task. Results Psychopathology and sleep architecture in 22q11.2 DS Young people living with 22q11.2DS (n=28) and healthy control siblings (n=17) completed semi-structured research diagnostic interviews to quantify Full Spectrum Intelligence Quotient (FSIQ), neuropsychiatric symptoms and self- and carer-reported sleep behavioral problems (Table 1 and Figure 1—figure supplement 1). Participants with 22q11.2DS had a lower mean FSIQ (reported as Odds Ratio (OR) or group difference (GD) with [95% confidence interval]): FSIQ, GD = − 28.70 [- 40.48, – 16.92], p<(0.001), and higher incidence of anxiety (OR = 3.10 [1.93, 4.99], p<0.001), ADHD (OR = 9.46 [5.12 – 17.48], p<0.001) and ASD symptoms (Odds Ratio [OR]=7.46 [4.76, 11.70], p<0.001), but did not show significantly more psychotic experiences than controls (OR = 4.05 [0.67, 43.67], p = 0.096). Details of the specific psychotic symptoms reported are shown in Table 2. Table 1 Psychiatric characteristics and sleep architecture. VariableGroupTypeStatistic (95% CI)p-value22q11.2DS,n=28 aSiblingControl,n=17 aAge @ EEG14.6 (3.4)13.7 (3.4)Group Difference (22q - Sib) b0.897 [-1.219, 3.013]0.397SexChi-Squared c01Female14 (50%)9 (53%)Male14 (50%)8 (47%)Sleep Problem1.32 (1.70)0.24 (0.56)Odds Ratio d6.269 [2.118, 18.556]0.001FSIQ76 (13)105 (27)Group Difference (22q - Sib) e–28.696 [-40.478,–16.915]<0.001missing01Anxiety Symptoms5.0 (7.8)1.4 (2.8)Odds Ratio d3.101 [1.929, 4.986]<0.001ADHD Symptoms6.0 (6.0)0.7 (2.1)Odds Ratio d9.456 [5.117, 17.475]<0.001ASD Symptoms11 (6)1 (2)Odds Ratio d7.463 [4.762, 11.697]<0.001missing11Psychotic ExperiencesOdds Ratio f4.047 [0.698, 43.668]0.096No PE18 (64%)15 (88%)PE10 (36%)2 (12%)N1 (%)10.4 (4.7)13.6 (4.3)Group Difference (22q - Sib) e–2.707 [-5.05,–0.363]0.044N2 (%)26.2 (8.2)27.1 (5.9)Group Difference (22q - Sib) e–1.089 [-5.146, 2.967]0.620N3 (%)30 (7)25 (6)Group Difference (22q - Sib) e5.473 [1.984, 8.962]0.009REM (%)14.4 (4.6)18.2 (5.6)Group Difference (22q - Sib) e–4.198 [-7.1,–1.296]0.012N1 Latency (Minutes)23 (18)21 (9)Group Difference (22q - Sib) e3.486 [-5.538, 12.509]0.470REM Latency (Minutes)143 (69)140 (49)Group Difference (22q - Sib) e9.368 [-19.312, 38.048]0.549Sleep Efficiency (%)88 (8)89 (9)Group Difference (22q - Sib) e–1.845 [-5.826, 2.136]0.398Total Sleep Time (Minutes)456 (122)485 (79)Group Difference (22q - Sib) e–27.206 [-88.489, 34.077]0.413Awakenings (n)42 (52)42 (40)Group Difference (22q - Sib) e3.097 [-19.732, 25.925]0.802a Mean (SD); n (%)b Linear Modelc Pearson's Chi Squared Testd Generalised Linear Mixed Modele Linear Mixed Modef Fisher's Exact Test Table 2 Psychotic experiences details. Frequency of specific psychotic experiencesType of PE22q11.2DSSiblingUnusual thought content/Delusional ideas81Suspiciousness/Persecutory ideas50Grandiose Ideas32Perceptual Abnormalities/Hallucinations82Disorganised communication40Count of total distinct types of psychotic experienceNumber of PE22q11.2DSSibling01815120221321440 Details of psychotic experiences reported by participants with 22q11.2DS and unaffected sibling controls in the CAPA interview. Participants with 22q11.2DS also experienced more sleep problems (OR = 6.27 [2.12, 18.56], p=0.001); more sleep problems were associated with younger age, 22q11.2DS genotype and anxiety symptoms but not with gender, family income, psychotic experiences, ADHD, or ASD symptoms (Table 3). Table 3 CAPA sleep problem adjusted model. TermOdds ratiop-valueGenotypeSiblingReference22q11.2DS7.867 [1.71, 36.186]0.008GenderFemaleReferenceMale1.557 [0.486, 4.986]0.456Age @ EEG0.757 [0.622, 0.921]0.005Family income (£PA)<19,999Reference20,000–39,9990.38 [0.068, 2.13]0.27140,000–59,9990.227 [0.034, 1.505]0.124>60,0000.297 [0.043, 2.058]0.219Anxiety symptomsa1.117 [1.031, 1.21]0.007ADHD symptomsa1.025 [0.945, 1.112]0.546ASD symptomsa0.964 [0.869, 1.07]0.488Psychotic experiences (PEs)No PEsReferencePEs1.369 [0.646, 2.9]0.413aContinuous variables (no reference category) Associations between CAPA sleep problem count and group, demographic, family and psychiatric covariates, modeled with a generalized linear mixed model, with a poisson distribution and family identity as a random (varying) intercept. Data shown are odds ratios and the 95% confidence interval. Participants were asked to perform a delayed recall 2D object location task (Figure 1A) to test sleep-dependent memory consolidation. Of 42 participants who engaged in the task, those with 22q11.2DS needed more training cycles to reach a 30% performance criterion (Hazard Ratio [95% CI]=0.328 [0.151, 0.714], p=0.005, Figure 1B, Table 4) and made fewer correct responses in the morning test session (OR = 0.631 [0.45, 0.885], p=0.008, Figure 1C, Table 4). However, there was no difference between groups in overnight change in correct responses between the evening learning session and the morning test session (Figure 1D, Table 4). Additionally, there was no association between task performance or accuracy in the morning test session and any psychiatric measure, or FSIQ (Table 4). Figure 1 with 1 supplement see all Download asset Open asset Memory task performance and sleep architecture features of 22q11.2DS. (A): Schematic of the 2D object location task. The evening before sleep EEG recordings, participants first were sequentially presented with pairs of images on a 5 x 6 grid. In a subsequent test cycle, they were presented with one image of the pair, and were required to select the grid location of the other half of the pair. If the participant did not achieve > 30% accuracy, they would have another learning cycle. In the morning a single test cycle was undertaken. (B): Plot of performance in acquiring the 2D object location task, showing the proportion of participants in each group reaching the 30% performance criterion after each learning cycle. Shaded areas represent the 95% confidence interval. Black dots show when participants were right-censored due to stopping the task prior to reaching the 30% criterion. (C): Box plots of performance in the morning test session, where participants had one cycle of the memory task. Number of correct responses is out of a possible 15. Asterix indicate the group difference is statistically significant, generalised linear mixed model, p<0.05 (see Table 2 for full statistics). (D): Plots of change in performance between the final evening learning session and the morning test session. Each participant is represented as a point, with a line connecting their evening and morning performance. Points have been slightly jittered to illustrate where multiple participants had the same score. (E): Box and whisker plots showing sleep architecture features: Total sleep time (TST) in minutes, Sleep efficiency (SE) as a percentage, Latency to N1 sleep (minutes), Latency to first REM sleep (minutes), Number of awakenings after sleep onset (n), Percentage of hypnogram in N1 sleep, Percentage of hypnogram in N2 sleep, Percentage of hypnogram in N3 sleep, and Percentage of hypnogram in REM sleep. Asterixes indicate the group difference is statistically significant, linear mixed model, P<0.05 (see Table 1 for full statistics). Boxes represent the median and IQR, with the whiskers representing 1.5 x the IQR. Individual participant data are shown as individual points. Points have been slightly jittered in the x direction only to illustrate where multiple participants had similar results. Table 4 Memory task acquisition and test session performance. Cycles to Criterion Cox ModelTermHazard ratiop-valueGroupControlReference22q11.2DS0.328 [0.151, 0.714]0.005GenderFemaleReferenceMale1.389 [0.642, 3.005]0.400Age @ EEG1.029 [0.91, 1.164]0.650Cycles to Criterion Cox Model – Adjusted for Psychiatric Measures - 22q11.2DS OnlyTermHazard Ratiop-valueGenderFemaleReferenceMale2.314 [0.542, 9.882]0.257Psychotic experiencesNo PEsReferencePEs0.203 [0.041, 1.012]0.052Age @ EEG1.139 [0.933, 1.390]0.200FSIQ1.026 [0.972, 1.082]0.355Anxiety symptoms0.992 [0.879, 1.120]0.900ADHD symptoms0.915 [0.760, 1.102]0.349ASD symptoms1.027 [0.926, 1.139]0.616Morning Accuracy Binomial ModelTermORp-valueGroupControlReference22q11.2DS0.631 [0.45, 0.885]0.008GenderFemaleReferenceMale1.083 [0.762, 1.538]0.657Age @ EEG0.997 [0.945, 1.051]0.900Morning Accuracy Binomial Model - Adjusted for Psychiatric Measures - 22q11.2DS OnlyTermORp-valueGenderFemaleReferenceMale1.623 [0.807, 3.268]0.174Psychotic experiencesNo PEsReferencePEs0.556 [0.296, 1.032]0.065Age @ EEG1.012 [0.924, 1.108]0.803FSIQ1.004 [0.982, 1.027]0.716Anxiety symptoms1.028 [0.969, 1.091]0.353ADHD symptoms0.973 [0.924, 1.023]0.288ASD symptoms1.018 [0.973, 1.066]0.441Evening – Morning DifferenceTermGroup Differencep-valueGroupControlReference22q11.2DS–0.424 [-1.923, 1.074]0.565GenderFemaleReferenceMale–0.5 [-2.036, 1.035]0.512Age @ EEG–0.023 [-0.256, 0.21]0.839 Associations between genotype group, sex, age and psychiatric symptoms and performance in the 2D object location task. All participants completed one night of full polysomnography with 64-channel high density EEG recorded at their home. After expert sleep scoring, we compared sleep architecture between 22q11.2DS and controls (Figure 1E and Table 1). There was no difference in gross measures of sleep such as Total Sleep Time and Sleep Efficiency, suggesting that our EEG recordings did not disrupt sleep differently between groups. However, 22q11.2DS was associated with a reduced percentage of N1 (GD = −2.71 [-5.05,–0.36], p = 0.044) and REM sleep (GD = −4.20 [-7.10,–1.30], p = 0.012) while the percentage of N3 sleep was increased (GD = 5.47 [1.98, 8.96], p = 0.009). There were no significant relationships between sleep architecture metrics and psychiatric measures or FSIQ in 22q11.2DS (Table 5). Table 5 Regression of sleep architecture features in 22q11.2DS. MeasureVariableBeta (95% CI)Adjusted P-value (BH)N1 (%)Sex–0.059 [-5.21, 5.092]0.981Age @ EEG0.101 [-0.808, 1.01]0.963CAPA sleep problems0.003 [-1.632, 1.639]0.963FSIQ0.135 [-0.044, 0.313]0.963Anxiety symptoms0.158 [-0.38, 0.696]0.963ADHD symptoms–0.288 [-0.682, 0.105]0.963ASD symptoms0.33 [-0.006, 0.666]0.963Psychotic experiences–2.758 [-6.921, 1.404]0.963N2 (%)Sex2.183 [-8.465, 12.831]0.963Age @ EEG0.097 [-1.782, 1.976]0.915CAPA sleep problems–0.407 [-3.787, 2.974]0.915FSIQ0.195 [-0.174, 0.564]0.915Anxiety symptoms0.254 [-0.859, 1.366]0.915ADHD symptoms–0.691 [-1.505, 0.123]0.915ASD symptoms0.28 [-0.414, 0.974]0.915Psychotic experiences–3.603 [-12.208, 5.002]0.915N3 (%)Sex–0.849 [-10.545, 8.847]0.915Age @ EEG0.675 [-1.037, 2.386]0.915CAPA sleep problems1.399 [-1.68, 4.477]0.997FSIQ–0.062 [-0.398, 0.273]0.997Anxiety symptoms–0.43 [-1.442, 0.583]0.816ADHD symptoms0.359 [-0.382, 1.1]0.816ASD symptoms–0.05 [-0.682, 0.582]0.997Psychotic experiences3.852 [-3.984, 11.688]0.816REM (%)Sex2.516 [-2.744, 7.775]0.816Age @ EEG–0.682 [-1.61, 0.246]0.816CAPA sleep problems–1.168 [-2.837, 0.502]0.816FSIQ0.138 [-0.044, 0.32]0.235Anxiety symptoms0.732 [0.182, 1.281]0.421ADHD symptoms–0.295 [-0.697, 0.107]0.788ASD symptoms–0.054 [-0.397, 0.288]0.235Psychotic experiences–0.404 [-4.655, 3.847]0.719N1 Latency (Minutes)Sex–8.061 [-32.213, 16.092]0.235Age @ EEG–1.225 [-5.487, 3.037]0.235CAPA sleep problems0.484 [-7.184, 8.152]0.947FSIQ–0.237 [-1.073, 0.6]0.235Anxiety symptoms–0.62 [-3.143, 1.902]0.638ADHD symptoms0.143 [-1.703, 1.989]0.638ASD symptoms–0.194 [-1.769, 1.38]0.638Psychotic experiences1.894 [-17.625, 21.414]0.107REM Latency (Minutes)Sex–30.174 [-110.781, 50.433]0.638Age @ EEG1.517 [-12.707, 15.741]0.638CAPA sleep problems10.761 [-14.83, 36.353]0.638FSIQ–2.491 [-5.283, 0.301]0.638Anxiety symptoms–2.763 [-11.183, 5.656]0.638ADHD symptoms3.909 [-2.251, 10.069]0.254ASD symptoms–2.116 [-7.371, 3.139]0.254Psychotic experiences–14.904 [-80.048, 50.24]0.363Sleep Efficiency (%)Sex1.813 [-8.177, 11.802]0.254Age @ EEG–0.099 [-1.862, 1.663]0.872CAPA sleep problems–0.979 [-4.151, 2.192]0.256FSIQ0.286 Sleep Time @ sleep @ sleep Associations between sleep architecture measures of N3 and REM sleep, to N1 and REM sleep, Sleep Efficiency, Total Sleep Time and total sex, age and psychiatric and covariates, in participants with 22q11.2DS. Regression were with linear mixed with family identity as a random (varying) intercept. Data presented are with 95% confidence properties of the sleep EEG in 22q11.2DS the for an altered distribution of sleep in 22q11.2DS, we analyses to quantify sleep EEG oscillations in our all EEG we power density across to for controls and in 22q11.2DS for as spindle and slow oscillations can be at this location (Figure We that power in lower to be increased in 22q11.2DS across N2 and N3 as well as across a range of during REM sleep Figure 2 with 2 see all Download asset Open asset power and Frequency in 22q11.2DS. in of the on across and REM sleep. show group mean power = 22q11.2DS, = with 95% confidence of the show of significant difference between groups = 22q11.2DS = 22q11.2DS with 22q11.2DS associated with increased power at lower of EEG on as in with 22q11.2DS associated with lower power in the frequency of EEG the on as power across a frequency range in 22q11.2DS. of the EEG the on as of group difference generalized to the full for the measures mean power and 1 and 1 recorded in N2 sleep. represent group indicate 22q11.2DS group indicate 22q11.2DS were where the of direction for group difference was are in for N3 sleep. in for REM sleep. as REM sleep we have not for or measures in REM as these would not be potential in specific of the EEG at the and frequency we first EEG recordings in the time to broadband power between recordings, and compared the (Figure This analysis reduced relative power in the frequency in 22q11.2DS in N2 and N3 sleep we analysis et al., 2020; and to the and of the This analysis demonstrated that the power of the of the was increased in 22q11.2DS across a range of in and REM sleep (Figure However, in the of the EEG we that power in the to be reduced in 22q11.2DS (Figure but to have a higher participant had a distinct in activity in the frequency (Figure supplement We on a of measures N2 and N3 power in the slow and and Additionally, we the and the of a 1 line to the of the to allow comparison of activity between for and REM We these measures across all EEG and generalized mixed to the data all for each Table 6 all EEG measures detailed analysis that 22q11.2DS showed lower but higher frequency during N2 and N3 sleep in and higher total as by the 1 across and REM sleep, particularly in (Figure In there were no in slow power or 1 between groups. Table 6 EEG – – – – Mean – – Mean All EEG by the individual spindle slow individual slow and measures spindle – slow coupling. Individual data and group for this of measures at are shown in Figure supplement 1B, and plots of measures with age are shown in Figure supplement 1C, relationships between age and and relationships between age and the power and as previously demonstrated et al., 2020). for all measures are shown in Figure supplement 2 and slow in 22q11.2DS the oscillations underlying alterations in power and we quantified individual spindle and slow (SW) detection spindle for each participant and each we the frequency for spindle the frequency our Figure and show for a of one control (Figure one with 22q11.2DS (Figure with spindle and SW these plots indicate the of spindle and SW during sleep. Figure 3 with 3 see all Download asset Open asset and slow in 22q11.2DS. of a night EEG for an The associated hypnogram is the in spindle and slow are in The of spindle with of N2 sleep, and of SW and N3 sleep can be of a night EEG for an participant with 22q11.2DS, sibling of the participant in spindle on for siblings and 22q11.2DS each individual the spindle at was these were for all siblings or all participants with 22q11.2DS. Shaded areas the 95% confidence of the SW on same as of group in spindle density, and across all with significant group are indicate of the of are in that the of is greater in siblings of group in SW density, and as in The of spindle and SW on are shown in Figure and Figure group in spindle and SW participants with 22q11.2DS showed increased spindle across with increases in spindle density and frequency across (Figure SW was also increased in and but there were no

Neurobiologic Mechanisms of Sleep and Wakefulness

Understanding Emotion Regulation in Borderline Personality Disorder: Contributions of Neuroimaging

Contingent negative variation (CNV), ambulatory polysomnography (APSG), and clinical rating scales as evaluation of depressive illness

24-h activity rhythm and sleep in depressed outpatients

P.4.a.001 Sleep disorders and obsessive-compulsive disorder

Background:Young people living with 22q11.2 Deletion Syndrome (22q11.2DS) are at increased risk of schizophrenia, intellectual disability, attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). In common with these conditions, 22q11.2DS is also associated with sleep problems. We investigated whether abnormal sleep or sleep-dependent network activity in 22q11.2DS reflects convergent, early signatures of neural circuit disruption also evident in associated neurodevelopmental conditions.Methods:In a cross-sectional design, we recorded high-density sleep EEG in young people (6–20 years) with 22q11.2DS (n=28) and their unaffected siblings (n=17), quantifying associations between sleep architecture, EEG oscillations (spindles and slow waves) and psychiatric symptoms. We also measured performance on a memory task before and after sleep.Results:22q11.2DS was associated with significant alterations in sleep architecture, including a greater proportion of N3 sleep and lower proportions of N1 and REM sleep than in siblings. During sleep, deletion carriers showed broadband increases in EEG power with increased slow-wave and spindle amplitudes, increased spindle frequency and density, and stronger coupling between spindles and slow-waves. Spindle and slow-wave amplitudes correlated positively with overnight memory in controls, but negatively in 22q11.2DS. Mediation analyses indicated that genotype effects on anxiety, ADHD and ASD were partially mediated by sleep EEG measures.Conclusions:This study provides a detailed description of sleep neurophysiology in 22q11.2DS, highlighting alterations in EEG signatures of sleep which have been previously linked to neurodevelopment, some of which were associated with psychiatric symptoms. Sleep EEG features may therefore reflect delayed or compromised neurodevelopmental processes in 22q11.2DS, which could inform our understanding of the neurobiology of this condition and be biomarkers for neuropsychiatric disorders.Funding:This research was funded by a Lilly Innovation Fellowship Award (UB), the National Institute of Mental Health (NIMH 5UO1MH101724; MvdB), a Wellcome Trust Institutional Strategic Support Fund (ISSF) award (MvdB), the Waterloo Foundation (918-1234; MvdB), the Baily Thomas Charitable Fund (2315/1; MvdB), MRC grant Intellectual Disability and Mental Health: Assessing Genomic Impact on Neurodevelopment (IMAGINE) (MR/L011166/1; JH, MvdB and MO), MRC grant Intellectual Disability and Mental Health: Assessing Genomic Impact on Neurodevelopment 2 (IMAGINE-2) (MR/T033045/1; MvdB, JH and MO); Wellcome Trust Strategic Award ‘Defining Endophenotypes From Integrated Neurosciences’ Wellcome Trust (100202/Z/12/Z MO, JH). NAD was supported by a National Institute for Health Research Academic Clinical Fellowship in Mental Health and MWJ by a Wellcome Trust Senior Research Fellowship in Basic Biomedical Science (202810/Z/16/Z). CE and HAM were supported by Medical Research Council Doctoral Training Grants (C.B.E. 1644194, H.A.M MR/K501347/1). HMM and UB were employed by Eli Lilly & Co during the study; HMM is currently an employee of Boehringer Ingelheim Pharma GmbH & Co KG. The views and opinions expressed are those of the author(s), and not necessarily those of the NHS, the NIHR or the Department of Health funders.

The aim of this article is to review progress in understanding the mechanisms that underlie circadian and sleep rhythms, and their role in the pathogenesis and treatment of depression. Literature was selected principally by Medline searches, and additional reports were identified based on ongoing research activities in the authors' laboratory. Many physiological processes show circadian rhythms of activity. Sleep and waking are the most obvious circadian rhythms in mammals. There is considerable evidence that circadian and sleep disturbances are important in the pathophysiology of mood disorders. Depressed patients often show altered circadian rhythms, sleep disturbances, and diurnal mood variation. Chronotherapies, including bright light exposure, sleep deprivation, and social rhythm therapies, may be useful adjuncts in non-seasonal and seasonal depression. Antidepressant drugs have marked effects on circadian processes and sleep. Recent progress in understanding chronobiological and sleep regulation mechanisms may provide novel insights and avenues into the development of new pharmacological and behavioral treatment strategies for mood disorders.

Disrupted circadian rhythm commonly reported in cancer survivors is closely associated with cancer related fatigue, sleep disturbances and compromised quality of life. As more cancer survivors request non-pharmacological treatment strategies for the management of their chronic sleep-related symptoms, there is a need for meta-analyses of various interventions such as exercise on sleep and circadian rhythm disturbances. A search for RCT's was conducted in April 2020 and updated in July 2023 using relevant keywords for cancer, sleep, circadian rhythm and exercise interventions on PubMed, Scopus, Web of Science, PEDro and CINAHL. Thirty-six studies were included for qualitative analysis and 26, for meta-analysis. Thirty-five studies analyzed sleep outcomes, while five analyzed circadian rhythm. RCT's studying the effect of aerobic exercise, resistance exercise, combined aerobic and resistance exercise, physical activity, yoga, or tai chi were included. Meta-analysis results showed significant exercise-related improvements on sleep quality assessed by Pittsburgh Sleep Quality index (PSQI) (SMD = -0.50 [-0.87, -0.13], p=0.008), wake after sleep onset (WASO) (SMD = -0.29 [-0.53, -0.05], p=0.02) and circadian rhythm, assessed by salivary cortisol levels (MD = -0.09 (95% CI [-0.13 to -0.06]) mg/dL, p<0.001). Results of the meta-analysis indicated that exercise had no significant effect on sleep efficiency, sleep onset latency, total sleep time and circadian rhythm assessed by accelerometry values. While some sleep and circadian rhythm outcomes (PSQI, WASO and salivary cortisol) exhibited significant improvements, it is still somewhat unclear what exercise prescriptions would optimize different sleep and circadian rhythm outcomes across a variety of groups of cancer survivors. As exercise does not exacerbate cancer-related circadian rhythm and sleep disturbances, and may actually produce some significant benefits, this meta-analysis provides further evidence for cancer survivors to perform regular exercise.

The authors compared the effects of bupropion, fluoxetine, and cognitive behavior therapy on EEG sleep in depressed subjects. All-night sleep EEG studies were performed before treatment and after partial or full remission on 18 men with depression diagnosed according to Research Diagnostic Criteria and randomly assigned to treatment with either bupropion (N = 7) or fluoxetine (N = 11). Response to these drugs was measured by changes in Hamilton Depression Rating Scale scores. Pre- and posttreatment EEG sleep study results before and after treatment with cognitive behavior therapy were also available for 18 men matched in age and severity of Hamilton depression scale score, and one-time EEG sleep measures were available for 36 men who were not depressed. REM latency was reduced and REM sleep percent and REM time increased after treatment in the depressed men given bupropion. These effects contrasted with the effects of fluoxetine and cognitive behavior therapy. This study represents the first report of an antidepressant medication that shortens REM latency and increases REM sleep. If confirmed, this finding may require a revision of our current understanding of the relation among depression, REM sleep, and anti-depressant mechanisms.

Metabotropic Glutamate Receptors of Subtype 5 (mGluR5) and Sleep Homeostasis: Effects of Gene Knock-out and of Selective Negative Allosteric Modulation on EEG, Behavioral and Molecular Variables in Mice

Psychiatric Disorders Associated with Disturbed Sleep and Circadian Rhythms

FRI0139 The correlation of self-reported behavioral co-morbidities and disease activity in early inflammatory arthritis patients: A prospective study