Depression and Obstructive Sleep Apnea (OSA)

For over two decades clinical studies have been conducted which suggest the existence of a relationship between depression and Obstructive Sleep Apnea (OSA). Recently, Ohayon underscored the evidence for a link between these two disorders in the general population, showing that 800 out of 100,000 individuals had both, a breathing-related sleep disorder and a major depressive disorder, with up to 20% of the subjects presenting with one of these disorders also having the other. In some populations, depending on age, gender and other demographic and health characteristics, the prevalence of both disorders may be even higher: OSA may affect more than 50% of individuals over the age of 65, and significant depressive symptoms may be present in as many as 26% of a community-dwelling population of older adults. In clinical practice, the presence of depressive symptomatology is often considered in patients with OSA, and may be accounted for and followed-up when considering treatment approaches and response to treatment. On the other hand, sleep problems and specifically OSA are rarely assessed on a regular basis in patients with a depressive disorder. However, OSA might not only be associated with a depressive syndrome, but its presence may also be responsible for failure to respond to appropriate pharmacological treatment. Furthermore, an undiagnosed OSA might be exacerbated by adjunct treatments to antidepressant medications, such as benzodiazepines. Increased awareness of the relationship between depression and OSA might significantly improve diagnostic accuracy as well as treatment outcome for both disorders. In this review, we will summarize important findings in the current literature regarding the association between depression and OSA, and the possible mechanisms by which both disorders interact. Implications for clinical practice will be discussed

Influence of psychosocial stress on changes of hypothalamo-pituitary-adrenocortical hormones and sleep dependent on CRHR1 genotype

In dieser Studie wurde der Einfluss psychosozialen Stresses auf den Schlaf und die Hormone der Hypothalamus-Hypophysen-Nebennierenrinden-Achse in Abhängigkeit vom CHRHR1 Genotyp (rs110402) untersucht. Dabei zeigte sich, dass die Cortisol-Ausschüttung nach Stress bei den homozygoten T-Trägern signifikant höher war, als bei den homozygoten C Trägern. Im Schlaf konnte lediglich ein genetisch unabhängiger Stress-Effekt in Form einer verringerten Schlaf-Effizienz, verringerten Zeit im REM Schlaf, sowie erhöhte Latenzen für N1, N2 und N3, sowie eine längere Zeit wach festgestellt werden

Using heart rate profiles during sleep as a biomarker of depression

Background: Abnormalities in heart rate during sleep linked to impaired neuro-cardiac modulation may provide new information about physiological sleep signatures of depression. This study assessed the validity of an algorithm using patterns of heart rate changes during sleep to discriminate between individuals with depression and healthy controls. Methods: A heart rate profiling algorithm was modeled using machine-learning based on 1203 polysomnograms from individuals with depression referred to a sleep clinic for the assessment of sleep abnormalities, including insomnia, excessive daytime fatigue, and sleep-related breathing disturbances (n = 664) and mentally healthy controls (n = 529). The final algorithm was tested on a distinct sample (n = 174) to categorize each individual as depressed or not depressed. The resulting categorizations were compared to medical record diagnoses. Results: The algorithm had an overall classification accuracy of 79.9% [sensitivity: 82.8, 95% CI (0.73–0.89), specificity: 77.0, 95% CI (0.67–0.85)]. The algorithm remained highly sensitive across subgroups stratified by age, sex, depression severity, comorbid psychiatric illness, cardiovascular disease, and smoking status. Conclusions: Sleep-derived heart rate patterns could act as an objective biomarker of depression, at least when it cooccurs with sleep disturbances, and may serve as a complimentary objective diagnostic tool. These findings highlight the extent to which some autonomic functions are im

Einfluss von Psychosozialem Stress auf die Hormone der Hypothalamus-Hypophysen-Nebennierenrinden-Achse und den Schlaf in Abhängigkeit vom CRHR1 Genotyp

This study investigated the influence of psychosocial stress on sleep and hormones of the hypothalamic-pituitary-adrenal cortex axis depending on the CHRHR1 genotype (rs110402). It was found that the cortisol release after stress was significantly higher in homozygous T carriers than in homozygous C carriers. During sleep, only a genetically independent stress effect in the form of reduced sleep efficiency, reduced time in REM sleep, increased latencies for N1, N2 and N3, and a longer period of wakefulness could be observed.In dieser Studie wurde der Einfluss psychosozialen Stresses auf den Schlaf und die Hormone der Hypothalamus-Hypophysen-Nebennierenrinden-Achse in Abhängigkeit vom CHRHR1 Genotyp (rs110402) untersucht. Dabei zeigte sich, dass die Cortisol-Ausschüttung nach Stress bei den homozygoten T-Trägern signifikant höher war, als bei den homozygoten C Trägern. Im Schlaf konnte lediglich ein genetisch unabhängiger Stress-Effekt in Form einer verringerten Schlaf-Effizienz, verringerten Zeit im REM Schlaf, sowie erhöhte Latenzen für N1, N2 und N3, sowie eine längere Zeit wach festgestellt werden

Decreased Neuroautonomic Complexity in Men during an Acute Major Depressive Episode: Analysis of Heart Rate Dynamics

Major depression affects multiple physiologic systems. Therefore, analysis of signals that reflect integrated function may be useful in probing dynamical changes in this syndrome. Increasing evidence supports the conceptual framework that complex variability is a marker of healthy, adaptive control mechanisms and that dynamical complexity decreases with aging and disease. We tested the hypothesis that heart rate (HR) dynamics in non-medicated, young to middle-aged males during an acute major depressive episode would exhibit lower complexity compared with healthy counterparts. We analyzed HR time series, a neuroautonomically regulated signal, during sleep, using the multiscale entropy method. Our results show that the complexity of the HR dynamics is significantly lower for depressed than for non-depressed subjects for the entire night (P<0.02) and combined sleep stages 1 and 2 (P<0.02). These findings raise the possibility of using the complexity of physiologic signals as the basis of novel dynamical biomarkers of depression

Changes in Physiological Network Connectivity of Body System in Narcolepsy during REM Sleep: 렘 수면 중 기면 환자의 인체 시스템 내 생리학적 네트워크 연결성 변화

학위논문(석사) -- 서울대학교대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2022.2. 박광석.연구 배경: 기면증은 병리학적 증상을 수반하는 수면 질환의 하나로, 야간에 충분한 수면을 취했음에도 주간의 과도한 졸림증, 무기력증의 증상을 나타낸다. 기면증은 두 가지의 유형이 있으며 이들은 탈력발작을 동반한 1유형 기면증과 탈력발작을 동반하지 않는 2유형 기면증으로 구별된다. 1유형 기면증의 진단 생체 지표로서 히포크레틴이라는 신경 물질이 존재하지만, 그에 반하여 2유형 기면증은 적절한 생체 지표가 부재하여 2유형 기면증의 기면 증상 및 인과관계의 확인에 한계를 지니고 있다. 이에 기반하여, 본 연구는 2유형 기면증의 새로운 생체 지표의 탐색을 목표로 하며 이를 위해 인체의 시스템적 연결망의 분석을 진행하였다. 연구 방법: 본 연구는 30명의 참여자 (15명의 2유형 기면증 환자, 15명의 정상 대조군)를 대상으로, 시간 지연 안정성의 방법을 통해 시간적 정보에 기반하여 여러 생체신호들의 관계에 대한 분석을 진행하였다. 각 참여자의 야간 수면다원 검사로부터 얻은 9개 생체신호들 (뇌파, 심장, 호흡, 근육과 안구의 움직임으로 부터의 신호)의 연결성 네트워크에 대한 정량적 분석을 진행하였으며, 특히 수면 단계에 따른 네트워크 연결성의 차이가 두 그룹 사이에 어떠한 영향력을 미치며 이들이 기면증과 정상군을 구별하는 잠재적 생체 지표로서 사용될 수 있을지에 대한 분석에 중점을 두었다. 그룹 간의 차이에 대한 인과관계의 조사와 함께 생체 지표의 분류 성능의 확인을 위한 서포트 벡터 머신 기법을 적용한 조사도 함께 진행하였다. 연구 결과: 렘 수면에서, 기면 환자군은 정상 대조군에 비교하여 더 많은 네트워크의 연결을 보였다 (기면 환자 연결 수: 24.47 ± 2.87, 대조군 연결 수: 21.34 ± 3.49; p = 0.022). 이러한 차이는 여러 연결의 요소들 중 움직임과 관련된 기관과 심장 활동에서 유의미하게 나타난 것을 확인할 수 있었으며, 네트워크의 연결 개수와 유의미한 차이를 보이는 생체신호 요소의 정보를 이용한 서포트 벡터 머신 기반 분류 성능은 0.93의 민감도, 특이도, 정확도를 각각 나타내었다. 결 론: 본 연구는 시간 지연 안정성에 기반한 네트워크 연결성이 2유형 기면증을 대조군과 분류하는 데에 있어 유용한 생체지표로 이용될 수 있음을 보이며, 나아가 인체의 시스템적 네트워크에 대한 분석을 통해 차이에 대한 인과관계 분석 및 정량적 접근이 가능함을 보여준다.Background: Narcolepsy is marked by pathologic symptoms including excessive daytime drowsiness and lethargy, even with sufficient nocturnal sleep. There are two types of narcolepsy: type 1 (with cataplexy) and type 2 (without cataplexy). Unlike type 1, for which hypocretin is a biomarker, type 2 narcolepsy has no adequate biomarker to identify the causality of narcoleptic phenomenon. Therefore, we aimed to establish new biomarkers for narcolepsy using the body’s systemic networks. Method: Thirty participants (15 with type 2 narcolepsy, 15 healthy controls) were included. We used the time delay stability (TDS) method to examine temporal information and determine relationships among multiple signals. We quantified and analyzed the network connectivity of nine biosignals (brainwaves, cardiac and respiratory information, muscle and eye movements) during nocturnal sleep. In particular, we focused on the differences in network connectivity between groups according to sleep stages and investigated whether the differences could be potential biomarkers to classify both groups by using a support vector machine. Result: In rapid eye movement sleep, the narcolepsy group displayed more connections than the control group (narcolepsy connections: 24.47 ± 2.87, control connections: 21.34 ± 3.49; p = 0.022). The differences were observed in movement and cardiac activity. The performance of the classifier based on connectivity differences was a 0.93 for sensitivity, specificity and accuracy, respectively. Conclusion: Network connectivity with the TDS method may be used as a biomarker to identify differences in the systemic networks of patients with narcolepsy type 2 and healthy controls.Chapter 1. Introduction 1 1.1. Narcolepsy 1 1.2. Physiological interactions in body system 3 1.3. Connectivity with time delay stability 5 1.4. Dissertation Outline 7 Chapter 2. Material and Methods 9 2.1. Participants 9 2.2. PSG recording and data 12 2.3. Data processing 14 2.4. Time delay cross-correleation 17 2.5. TDS methods 20 2.6. Threshold tuning 22 2.7. Test-retest reproducibility 25 2.8. Brain and peripheral connections 26 2.9. Effect of brain-brain connections according to brain areas 27 2.10. Feature significance analysis 28 2.11. Network directionality with correlation 29 2.12. Verifications of network connectivity as classifier 30 2.13. Classification with support vector machine 31 Chater 3. Results and Discussion 32 3.1. Results 32 3.1.1. Network connections between narcolepsy and control groups 32 3.1.2. Test-retest analysis for reproducibility 35 3.1.3. Significant feature identification 36 3.1.4. Effect of brain-brain connections according to brain areas 39 3.1.5. Network directionality with correlation 44 3.1.6. Performance comparison between unimodal biosignal and connectivity 46 3.1.7. Classification performance with SVM 49 3.2. Discussion 51 3.2.1. Differences between patients with narcolepsy and healthy controls 51 3.2.2. Analysis of nervous system with HRV 52 3.2.3. Causalities in network connections 55 3.2.4. Effect of brain-brain connections 58 3.2.5. Network connectivity as a biomarker and prospective utility 59 Limitations 61 References 63 국문초록 72석

Rentoutuminen ennen nukkumaan menoa vähensi REM-unen katkonaisuutta : pilottitutkimus

Tavoitteet. Aiempien tutkimusten mukaan sekä hidas hengitys että musiikin kuuntelu ennen nukkumaanmenoa voivat vähentää koehenkilöiden kokemusta levottomasta ja katkonaisesta yöunesta. Onkin ehdotettu, että unettomuuspotilaille tyypillinen kokemus katkonaisista ja huonosti palauttavista yöunista voisi selittyä REM-unen aikaisilla lyhyillä heräämisillä. Kuitenkaan sitä, voidaanko unta edeltävällä rentoutumisella vaikuttaa REM-unen katkonaisuuteen, ei ole tutkittu objektiivisella mittauksella aiemmin. Tämän tutkimuksen tavoitteena oli selvittää unipolygrafia-mittauksella, voidaanko unta edeltävällä rentoutumisella parantaa REM-unen laatua sekä vähentää REM-unen katkonaisuutta. Menetelmät. Tutkimus oli satunnaistettu kontrolloitu tutkimus, jossa 20 koehenkilöä jaettiin kahteen koeryhmään. Toinen ryhmä hengitti viisi hengitystä minuutissa puolen tunnin ajan, kun taas toinen ryhmä kuunteli rauhallista musiikkia puolen tunnin ajan ennen nukkumaanmenoa. Koehenkilöiden unta mitattiin kahtena peräkkäisenä yönä heidän omissa kodeissaan. Toinen öistä toimi kontrollitilanteena. Tulokset analysoitiin lineaarisella sekamallilla. Tulokset ja johtopäätökset. Hidas hengitys vähensi 3–15 sekuntia kestävien heräämisten suhteellista osuutta REM-unesta verrattuna kontrollitilanteeseen. Musiikin kuuntelu ei vaikuttanut REM-unen katkonaisuuteen tai muihin REM-muuttujiin. Tulosten perusteella nukkumaanmenoa edeltävä hidas hengitys voi vähentää REM-unen katkonaisuutta. Kuitenkin lisätutkimukset isommalla ja monipuolisemmalla otoksella ovat tarpeen, jotta löydettyä yhteyttä ymmärrettäisiin syvällisemmin.Aims of the study. Evidence from previous studies suggests that slow breathing or listening to calming music before sleep would decrease subjects’ experience of fragmented and disturbed sleep. It has been proposed that experience of restless and non-restorative sleep could be explained by fragmented REM sleep. However, the possibility to decrease REM sleep fragmentation by increasing pre-sleep relaxation has not been investigated objectively before. The aim of this study was to investigate whether slow breathing or listening to music improve REM sleep quality and decrease REM sleep fragmentation. Methods. This study was a randomized controlled trial, where 20 participants were randomized to two intervention groups. The other group breathed five slow breaths in a minute for 30 minutes before sleep, while the other group listened to calming music for 30 minutes before sleep. Participants’ sleep was measured on two successive nights with polysomnography. The other night included the intervention, while the other night worked as a control night without treatment. The data was analyzed with a linear mixed model. Results and conclusions. Slow breathing decreased the percentage of macro-arousals (3–15 s) compared to control condition. Pre-sleep music listening did not influence REM sleep fragmentation or other REM sleep parameters. The results suggest that pre-sleep slow breathing could improve REM sleep quality by decreasing fragmentation of REM sleep. However, replications of this study with larger sample sizes and more diverse subject populations are needed to better understand the exact mechanisms underlying these associations

The sleep phenotype of Borderline Personality Disorder : a systematic review and meta-analysis

Aim: To delineate the sleep profile of Borderline Personality Disorder (BPD). Method: A meta-analysis to synthesise findings on the objective and subjective sleep characteristics of BPD. Results: We identified 32 studies published between 1980 and December 2015. Meta-analysis indicated significant differences between BPD and healthy control groups across objective sleep continuity (sleep onset latency, total sleep time, sleep efficiency) and architecture (rapid eye movement latency/density, slow wave sleep) measures, and self-reported sleep problems (nightmares, sleep quality). Findings were independent of depression (in clinical and community populations), and concomitant psychotropic medication use. There were few significant differences between BPD and clinical (majority depressed) control groups. Conclusion: BPD is associated with comparable sleep disturbances to those observed in depression. These disturbances are not solely attributable to comorbid depression. Given growing evidence that sleep disturbance may exacerbate emotional dysregulation and suicide risk, treatments for BPD should explicitly address sleep problems. Future studies should utilise prospective designs to ascertain whether (and in which circumstances) sleep problems predate or follow the onset of the disorder

Modulation of Sleep Quality and Autonomic Functioning by Symptoms of Depression in Women with Irritable Bowel Syndrome

The objective of this study was to determine how depressive symptoms affect autonomic activity during sleep, objective and subjective sleep, and gastrointestinal symptom severity in women with irritable bowel syndrome (IBS). Seventy women who met the Rome II criteria for IBS and 21 healthy volunteers participated. All participants were recruited from the surrounding community. IBS patients were stratified into two groups based on their Beck Depression Inventory II score and 44 IBS patients with depressive symptoms (IBS+DS) were compared to 26 IBS patients without depressive symptoms (IBS−DS). Autonomic activity was measured by heart rate variability (HRV) analysis. Fifteen-minute segments were selected from a baseline presleep period, stage 2, slow-wave sleep, and rapid-eye movement sleep for heart rate variability spectral analysis. Subjective sleep quality was assessed by the Pittsburg Sleep Quality Index (PSQI) and gastrointestinal symptom severity was assessed by an 18-item questionnaire. The IBS+DS group reported significantly ( P <0.01) more sleep complaints, measured by the PSQI, than the IBS−DS group and healthy controls. The IBS+DS group took significantly ( P <0.05) longer to enter their first rapid-eye movement period than healthy controls. The IBS+DS group reported significantly ( P =0.01) increased gastrointestinal symptom severity compared to the IBS−DS group. There were no significant group differences in autonomic activity during the baseline presleep period or sleep stages. The results demonstrated that IBS patients with significant depressive symptoms had increased gastrointestinal symptom severity, increased sleep complaints, and alterations in sleep architecture compared to healthy controls and IBS patients without significant depressive symptoms.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44433/1/10620_2004_Article_494230.pd