Microstructure of sleep in depressed patients according to the cyclic alternating pattern

The daily reciprocal associations between electroencephalography measured sleep and affect

Self-report studies show that sleep and positive and negative affect are closely and bidirectionally linked. However, studies assessing sleep objectively yield more inconsistent results. This study assessed the reciprocal, daily relationship between sleep as measured with electroencephalography (EEG) and affect (measured in the evening) in a natural setting. We assessed sleep both on the macrolevel (i.e., rapid eye movement [REM] sleep and slow-wave sleep [SWS] duration) and on the microlevel (i.e., REM sleep fragmentation). In this study, 33 participants (i.e., healthy college students, mean [standard deviation] age 21.55 [3.73] years, 67% female) were followed for 2 weeks. Each participant wore an EEG headband for 15 nights and had polysomnography during 3 of the 15 nights providing 72 analysable nights of polysomnography and 271 analysable nights with the EEG headband. Every evening participants reported their momentary negative and positive affect. We examined the relationship between pre-sleep affect and the sleep variables, as well as the reverse relationship, with sleep variables predicting evening affect the next day. We detected that higher negative affect in the evening was related to more fragmented REM sleep. However, this result was only found with polysomnography and not with the EEG headband. No significant associations were found between affect and time spent in REM sleep and SWS. Overall, no support was found for the reciprocal association between negative and positive affect and EEG measured sleep. Only limited support was found for an association in one direction (i.e., evening negative affect was associated with more REM sleep fragmentation at night).

Sleep apnea pathophysiology

OBJECTIVE

The purpose of this study is to examine the pathophysiology underlying sleep apnea (SA).

BACKGROUND

We consider several critical features of SA including the roles played by the ascending reticular activating system (ARAS) that controls vegetative functions and electroencephalographic findings associated with both SA and normal sleep. We evaluate this knowledge together with our current understanding of the anatomy, histology, and physiology of the mesencephalic trigeminal nucleus (MTN) and mechanisms that contribute directly to normal and disordered sleep. MTN neurons express γ-aminobutyric acid (GABA) receptors which activate them (make chlorine come out of the cells) and that can be activated by GABA released from the hypothalamic preoptic area.

METHOD

We reviewed the published literature focused on sleep apnea (SA) reported in Google Scholar, Scopus, and PubMed databases.

RESULTS

The MTN neurons respond to the hypothalamic GABA release by releasing glutamate that activates neurons in the ARAS. Based on these findings, we conclude that a dysfunctional MTN may be incapable of activating neurons in the ARAS, notably those in the parabrachial nucleus, and that this will ultimately lead to SA. Despite its name, obstructive sleep apnea (OSA) is not caused by an airway obstruction that prevents breathing.

CONCLUSIONS

While obstruction may contribute to the overall pathology, the primary factor involved in this scenario is the lack of neurotransmitters.

Investigating the relationship between objective measures of sleep and self-report sleep quality in healthy adults: a review

STUDY OBJECTIVES

Sleep is one of the most common factors related to health, yet a standard definition of sleep quality has not been identified. Polysomnography provides important information about objective sleep variables. However, the relationship between objective sleep variables and perception of sleep quality remains unclear. The purpose of this review was to (1) summarize the current methods of measuring objective sleep macrostructure and microstructure, including electroencephalography arousals, spectral frequency, cyclic alternating pattern, and self-report sleep quality, and (2) investigate the relationship between objective measures of sleep physiology and self-report sleep quality in healthy adults.

METHODS

A literature search was conducted using Medline, PubMed, and PsycInfo databases and cited reference searches. Eligible studies included a comparison between self-report sleep quality and polysomnography sleep measures in healthy adults.

RESULTS

Thirteen studies were identified. Measurement of self-report sleep quality varied widely across studies. Total sleep time and sleep efficiency were most consistently related to sleep quality, while other objective sleep variables, including electroencephalography spectral analysis, were not reliably predictive of self-report sleep quality in healthy adults. There is preliminary support that microstructural sleep analysis with cyclic alternating pattern may be related to self-reported sleep quality.

CONCLUSIONS

Further research is needed to define and standardize self-report measures of sleep quality and investigate the microstructure of sleep. Objective measures of sleep and experiences of "quality" sleep are not as closely related as one may expect in healthy individuals, and understanding this relationship further is necessary to improve the clinical utility of sleep physiology.

CITATION

Cudney LE, Frey BN, McCabe RE, Green SM. Investigating the relationship between objective measures of sleep and self-report sleep quality in healthy adults: a review. J Clin Sleep Med. 2022;18(3):927-936.

Automatic Cyclic Alternating Pattern (CAP) analysis: Local and multi-trace approaches

The Cyclic Alternating Pattern (CAP) is composed of cycles of two different electroencephalographic features: an activation A-phase followed by a B-phase representing the background activity. CAP is considered a physiological marker of sleep instability. Despite its informative nature, the clinical applications remain limited as CAP analysis is a time-consuming activity. In order to overcome this limit, several automatic detection methods were recently developed. In this paper, two new dimensions were investigated in the attempt to optimize novel, efficient and automatic detection algorithms: 1) many electroencephalographic leads were compared to identify the best local performance, and 2) the global contribution of the concurrent detection across several derivations to CAP identification. The developed algorithms were tested on 41 polysomnographic recordings from normal (n = 8) and pathological (n = 33) subjects. In comparison with the visual CAP analysis as the gold standard, the performance of each algorithm was evaluated. Locally, the detection on the F4-C4 derivation showed the best performance in comparison with all other leads, providing practical suggestions of electrode montage when a lean and minimally invasive approach is preferable. A further improvement in the detection was achieved by a multi-trace method, the Global Analysis-Common Events, to be applied when several recording derivations are available. Moreover, CAP time and CAP rate obtained with these algorithms positively correlated with the ones identified by the scorer. These preliminary findings support efficient automated ways for the evaluation of the sleep instability, generalizable to both normal and pathological subjects affected by different sleep disorders.

Automated Characterization of Cyclic Alternating Pattern Using Wavelet-Based Features and Ensemble Learning Techniques with EEG Signals

Sleep is highly essential for maintaining metabolism of the body and mental balance for increased productivity and concentration. Often, sleep is analyzed using macrostructure sleep stages which alone cannot provide information about the functional structure and stability of sleep. The cyclic alternating pattern (CAP) is a physiological recurring electroencephalogram (EEG) activity occurring in the brain during sleep and captures microstructure of the sleep and can be used to identify sleep instability. The CAP can also be associated with various sleep-related pathologies, and can be useful in identifying various sleep disorders. Conventionally, sleep is analyzed using polysomnogram (PSG) in various sleep laboratories by trained physicians and medical practitioners. However, PSG-based manual sleep analysis by trained medical practitioners is onerous, tedious and unfavourable for patients. Hence, a computerized, simple and patient convenient system is highly desirable for monitoring and analysis of sleep. In this study, we have proposed a system for automated identification of CAP phase-A and phase-B. To accomplish the task, we have utilized the openly accessible CAP sleep database. The study is performed using two single-channel EEG modalities and their combination. The model is developed using EEG signals of healthy subjects as well as patients suffering from six different sleep disorders namely nocturnal frontal lobe epilepsy (NFLE), sleep-disordered breathing (SDB), narcolepsy, periodic leg movement disorder (PLM), insomnia and rapid eye movement behavior disorder (RBD) subjects. An optimal orthogonal wavelet filter bank is used to perform the wavelet decomposition and subsequently, entropy and Hjorth parameters are extracted from the decomposed coefficients. The extracted features have been applied to different machine learning algorithms. The best performance is obtained using ensemble of bagged tress (EBagT) classifier. The proposed method has obtained the average classification accuracy of 84%, 83%, 81%, 78%, 77%, 76% and 72% for NFLE, healthy, SDB, narcolepsy, PLM, insomnia and RBD subjects, respectively in discriminating phases A and B using a balanced database. Our developed model yielded an average accuracy of 78% when all 77 subjects including healthy and sleep disordered patients are considered. Our proposed system can assist the sleep specialists in an automated and efficient analysis of sleep using sleep microstructure.

A Systematic Review of Sleep Measurement in Critically Ill Patients

Background: Clinical trialists and clinicians have used a number of sleep quality measures to determine the outcomes of interventions to improve sleep and ameliorate the neurobehavioral consequences of sleep deprivation in critically ill patients, but findings have not always been consistent. To elucidate the source of these consistencies, an important consideration is responsiveness of existing sleep measures. The purpose of an evaluative measure is to describe a construct of interest in a specific population, and to measure the extent of change in the construct over time. This systematic literature review identified measures of sleep quality in critically ill adults hospitalized in the Intensive Care Unit (ICU), and assessed their measurement properties, strengths and weaknesses, clinical usefulness, and responsiveness. We also recommended modifications, including new technology, that may improve clinical usefulness and responsiveness of the measures in research and practice.

Methods: CINAHAL, PubMed/Medline, and Cochrane Library were searched from January 1, 2000 to February 1, 2020 to identify studies that evaluated sleep quality in critically ill patients.

Results: Sixty-two studies using polysomnography (PSG) and other electroencephalogram-based methods, actigraphy, clinician observation, or patient perception using questionnaires were identified and evaluated. Key recommendations are: standard criteria are needed for scoring PSG in ICU patients who often have atypical brain waves; studies are too few, samples sizes too small, and study duration too short for recommendations on electroencephalogram-based measures and actigraphy; use the Sleep Observation Tool for clinician observation of sleep; and use the Richards Campbell Sleep Questionnaire to measure patient perception of sleep.

Conclusions: Measuring the impact of interventions to prevent sleep deprivation requires reliable and valid sleep measures, and investigators have made good progress developing, testing, and applying these measures in the ICU. We recommend future large, multi-site intervention studies that measure multiple dimensions of sleep, and provide additional evidence on instrument reliability, validity, feasibility and responsiveness. We also encourage testing new technologies to augment existing measures to improve their feasibility and accuracy.

Sleep and mental disorders: A meta-analysis of polysomnographic research

Investigating sleep in mental disorders has the potential to reveal both disorder-specific and transdiagnostic psychophysiological mechanisms. This meta-analysis aimed at determining the polysomnographic (PSG) characteristics of several mental disorders. Relevant studies were searched through standard strategies. Controlled PSG studies evaluating sleep in affective, anxiety, eating, pervasive developmental, borderline and antisocial personality disorders, attention-deficit-hyperactivity disorder (ADHD), and schizophrenia were included. PSG variables of sleep continuity, depth, and architecture, as well as rapid-eye movement (REM) sleep were considered. Calculations were performed with the "Comprehensive Meta-Analysis" and "R" software. Using random effects modeling, for each disorder and each variable, a separate meta-analysis was conducted if at least 3 studies were available for calculation of effect sizes as standardized means (Hedges' g). Sources of variability, that is, sex, age, and mental disorders comorbidity, were evaluated in subgroup analyses. Sleep alterations were evidenced in all disorders, with the exception of ADHD and seasonal affective disorders. Sleep continuity problems were observed in most mental disorders. Sleep depth and REM pressure alterations were associated with affective, anxiety, autism and schizophrenia disorders. Comorbidity was associated with enhanced REM sleep pressure and more inhibition of sleep depth. No sleep parameter was exclusively altered in 1 condition; however, no 2 conditions shared the same PSG profile. Sleep continuity disturbances imply a transdiagnostic imbalance in the arousal system likely representing a basic dimension of mental health. Sleep depth and REM variables might play a key role in psychiatric comorbidity processes. Constellations of sleep alterations may define distinct disorders better than alterations in 1 single variable. (PsycINFO Database Record

Bupropion response on sleep quality in patients with depression: implications for increased cardiovascular disease risk

Depression could be an independent risk factor for cardiovascular disease. We assessed bupropion response in depressed patients by polysomnography (PSG) and cardiopulmonary coupling (CPC) variables. Nineteen subjects participated in a two-session, two consecutive night PSG protocol. Participants received either placebo or bupropion-SR 150 mg, orally, in a randomized, double-blind cross-over fashion on night two. Outcome variables were: sleep stages, REM latency, stable, unstable sleep and very low frequency coupling (VLFC). CPC analysis uses heart rate variability and the electrocardiogram's R-wave amplitude fluctuations associated with respiration to generate frequency maps. Bupropion increased REM latency (p=0.043) but did not impact PSG sleep continuity, architecture and CPC variables. A trend (p=0.092) was observed towards increasing VLFC duration. Bupropion increased the number of stable-unstable sleep transitions (p=0.036). Moderate to strong correlations between PSG and CPC variables were found on placebo and bupropion nights. Limitations include a small sample size, limited power to detect CPC changes and lack of normal controls for comparison. Increased stable-unstable sleep transitions and VLFC duration may indicate vulnerability to cardiovascular disease due to their association with low heart rate variability that has been associated with increased mortality raising the question whether the beneficial effects of the antidepressant medication outweighs the impact on cardiopulmonary dynamics.

An electrocardiogram-based analysis evaluating sleep quality in patients with obstructive sleep apnea

OBJECTIVE

The study compares polysomnography (PSG) and cardiopulmonary coupling (CPC) sleep quality variables in patients with (1) obstructive sleep apnea (OSA) and (2) successful and unsuccessful continuous positive airway pressure (CPAP) response.

PATIENTS/METHODS

PSGs from 50 subjects (32 F/18 M; mean age 48.4 ± 12.29 years; BMI 34.28 ± 9.33) were evaluated. OSA patients were grouped by no (n = 16), mild (n = 13), and moderate to severe (n = 20) OSA (apnea-hypopnea index (AHI) ≤ 5, >5-15, >15 events/h, respectively). Outcome sleep quality variables were sleep stages in non-rapid eye movement, rapid eye movement sleep, and high (HFC), low (LFC), very low-frequency coupling (VLFC), and elevated LFC broad band (e-LFCBB). An AHI ≤ 5 events/h and HFC ≥ 50 % indicated a successful CPAP response. CPC analysis extracts heart rate variability and QRS amplitude change that corresponds to respiration. CPC-generated spectrograms represent sleep dynamics from calculated coherence product and cross-power of both time series datasets.

RESULTS

T tests differentiated no and moderate to severe OSA groups by REM % (p = 0.003), HFC (p = 0.007), VLFC (p = 0.007), and LFC/HFC ratio (p = 0.038) variables. The successful CPAP therapy group (n = 16) had more HFC (p = 0.003), less LFC (p = 0.003), and e-LFCBB (p = 0.029) compared to the unsuccessful CPAP therapy group (n = 8). PSG sleep quality measures, except the higher arousal index (p = 0.038) in the unsuccessful CPAP group, did not differ between the successful and unsuccessful CPAP groups. HFC ≥ 50 % showed high sensitivity (77.8 %) and specificity (88.9 %) in identifying successful CPAP therapy.

CONCLUSIONS

PSG and CPC measures differentiated no from moderate to severe OSA groups and HFC ≥ 50 % discriminated successful from unsuccessful CPAP therapy. The HFC ≥ 50 % cutoff showed clinical value in identifying sleep quality disturbance among CPAP users.

A meta-analysis of electroencephalographic sleep in depression: evidence for genetic biomarkers

BACKGROUND

Research on whether any electroencephalographic (EEG) sleep abnormalities observed among individuals with major depressive disorder (MDD) represent genetic biomarkers remains inconclusive. We aimed to identify EEG-based biomarkers of MDD through a review of studies from three populations: individuals with MDD, individuals with MDD under remission, and never depressed high-risk probands (HRPs) of individuals with MDD.

METHODS

We searched databases such as MEDLINE and PsycINFO for EEG studies published since 1970. Of the 886 records, our selection criteria identified 56 studies that employed standardized EEG scoring procedures and addressed confounds such as participant reactivity and drug effects. We then used fixed-effects models to calculate average weighted mean differences in EEG parameters between clinical groups across these studies.

RESULTS

Individuals with MDD differed significantly from control subjects on several EEG variables. However, remitted individuals showed normalization of all affected EEG parameters except rapid eye movement (REM) density and slow-wave sleep (SWS). Surprisingly, proportion of SWS was significantly shorter during remission than depression. Never-depressed HRPs also exhibited significantly elevated REM density and reduced SWS. Finally, these parameters constituted the only two EEG variables that were not moderated by depression severity.

CONCLUSIONS

Individuals experiencing MDD and those in remission exhibit increased REM density and shortened SWS, as do HRPs with no history of MDD. Thus, this combination of EEG features may represent a genetic biomarker of MDD. Further, SWS appears to be shorter during remission than depression, suggesting its role as both a genetic marker as well as a biological scar of the disorder.

Cyclic alternating pattern (CAP): the marker of sleep instability

Cyclic alternating pattern CAP is the EEG marker of unstable sleep, a concept which is poorly appreciated among the metrics of sleep physiology. Besides, duration, depth and continuity, sleep restorative properties depend on the capacity of the brain to create periods of sustained stable sleep. This issue is not confined only to the EEG activities but reverberates upon the ongoing autonomic activity and behavioral functions, which are mutually entrained in a synchronized oscillation. CAP can be identified both in adult and children sleep and therefore represents a sensitive tool for the investigation of sleep disorders across the lifespan. The present review illustrates the story of CAP in the last 25 years, the standardized scoring criteria, the basic physiological properties and how the dimension of sleep instability has provided new insight into pathophysiolology and management of sleep disorders.

Sleep state instabilities in major depressive disorder: Detection and quantification with electrocardiogram-based cardiopulmonary coupling analysis

Sleep disruption is an important aspect of major depressive disorder but lacks an objective and inexpensive means of assessment. We evaluated the utility of electrocardiogram (ECG)-based cardiopulmonary coupling analysis to quantify physiologic sleep stability in patients with major depression. Relative to controls, unmedicated depressed patients had a reduction in high-frequency coupling, an index of stable sleep, an increase in low-frequency coupling, an index of unstable sleep, and an increase in very-low-frequency coupling, an index of wakefulness/REM sleep. The medicated depressed group showed a restoration of stable sleep to a level comparable with that of the control group. ECG-based cardiopulmonary coupling analysis may provide a simple, cost-efficient point-of-care method to quantify sleep quality/stability and to objectively evaluate the severity of insomnia in patients with major depression.

Impact of the novel antidepressant agomelatine on disturbed sleep-wake cycles in depressed patients

BACKGROUND

Disturbance of sleep-wake cycles is common in major depressive disorder (MDD), usually as insomnia, but also as hypersomnia or reduced daytime alertness. Agomelatine, an MT(1) and MT(2) receptor agonist and 5-HT(2C) receptor antagonist, represents a novel approach in MDD, with proven antidepressant efficacy and a positive impact on the sleep-wake cycle. We review the effects of agomelatine 25/50 mg/day on objective and subjective measures of the sleep-wake cycle in MDD.

SUBJECTIVE MEASURES

Agomelatine improved all aspects of the sleep-wake cycle from as early as 1 week in randomized trials versus selective serotonin reuptake inhibitors and venlafaxine, particularly getting off to sleep and quality of sleep, with an improvement in daytime alertness.

OBJECTIVE MEASURES

Agomelatine's effect on sleep architecture in MDD has been measured by polysomnography (PSG). There were significant improvements in sleep efficiency, slow-wave sleep (SWS), and the distribution of delta activity throughout the night, but no change in amount or latency of rapid eye movement (REM) sleep. Furthermore, the slow-wave sleep was resynchronized to the first sleep cycle of the night.

CONCLUSION

Agomelatine, a novel antidepressant, improves disturbed sleep-wake cycles in MDD. The improvement of both nighttime sleep and daytime functioning with agomelatine are promising features of this antidepressant regarding the management of MDD.

Insomnia in patients with depression: some pathophysiological and treatment considerations

The almost ubiquitous sleep disturbances in patients with depression commonly, but not always, subside with the remission of depression. Evidence linking insomnia with the risk of relapses in recurrent depression, as well as suicide, makes optimization of the treatment of insomnia associated with depression a priority. However, most antidepressant agents do not adequately address the sleep complaints in depression: their effects on sleep range from sizeable improvement to equally significant worsening. One approach to the management of insomnia associated with depression is to choose a sedating antidepressant agent such as trazodone, mirtazapine or agomelatine. A second approach is to start with a non-sedating antidepressant (e.g. the selective serotonin reuptake inhibitors, bupropion, venlafaxine or duloxetine); those with a persistent or treatment-emergent insomnia can be switched to a more sedating antidepressant, or offered a hypnotic or cognitive-behavioural therapy as adjunctive treatment. The review discusses the advantages and disadvantages of all treatment options, pharmacological and otherwise.

Enhancement of sleep stability with Tai Chi exercise in chronic heart failure: preliminary findings using an ECG-based spectrogram method

OBJECTIVE

To assess the effects of a 12-week Tai Chi exercise program on sleep using the sleep spectrogram, a method based on a single channel electrocardiogram (ECG)-derived estimation of cardiopulmonary coupling, previously shown to identify stable and unstable sleep states.

METHODS

We retrospectively analyzed 24-h continuous ECG data obtained in a clinical trial of Tai Chi exercise in patients with heart failure. Eighteen patients with chronic stable heart failure, left ventricular ejection fraction

RESULTS

At 12 weeks, those who participated in Tai Chi showed a significant increase in HFC (+0.05+/-0.10 vs. -0.06+/-0.09 % ETST, p=0.04) and significant reduction in LFC (-0.09+/-0.09 vs. +0.13+/-0.13 % ETST, p<0.01), compared to patients in the control group. Correlations were seen between improved sleep stability and better disease-specific quality of life.

CONCLUSIONS

Tai Chi exercise may enhance sleep stability in patients with chronic heart failure. This sleep effect may have a beneficial impact on blood pressure, arrhythmogenesis and quality of life.

Collapse

Key Words

• exercise
• heart failure
• heart rate variability
• mind-body
• sleep stability

Collapse

MESH Headings

• Adult
• Aged
• Chronic Disease
• Combined Modality Therapy
• Electrocardiography, Ambulatory
• Exercise Test
• Female
• Fourier Analysis
• Heart Failure/psychology
• Heart Failure/rehabilitation
• Heart Rate
• Humans
• Male
• Middle Aged
• Polysomnography
• Quality of Life/psychology
• Signal Processing, Computer-Assisted
• Sleep Deprivation/psychology
• Sleep Deprivation/rehabilitation
• Sleep Initiation and Maintenance Disorders/psychology
• Sleep Initiation and Maintenance Disorders/rehabilitation
• Tai Ji

Collapse

Grants

• P41-RR13622 NCRR NIH HHS
• T32 AT000051 NCCIH NIH HHS
• K24 AT00589 NCCIH NIH HHS
• P41 RR013622 NCRR NIH HHS
• P60 AG008812 NIA NIH HHS
• T32 AT00051 NCCIH NIH HHS
• P30 AG028717 NIA NIH HHS
• K23 AT002624 NCCIH NIH HHS
• P60-AG08812 NIA NIH HHS
• K24 AT000589 NCCIH NIH HHS
• T32 AG023480 NIA NIH HHS

Collapse

Affiliation(s)

Gloria Y Yeh Division for Research and Education in Complementary and Integrative Medical Therapies, Harvard Medical School, Boston, MA 02215, USA.
Joseph E Mietus
Chung-Kang Peng
Russell S Phillips
Roger B Davis
Peter M Wayne
Ary L Goldberger
Robert J Thomas

Collapse

Collaborators Collapse

Non-REM sleep instability in patients with major depressive disorder: Subjective improvement and improvement of non-REM sleep instability with treatment (Agomelatine)

OBJECTIVE

To assess the importance of non-rapid eye movement (NREM) sleep disturbance in major depressive disorder (MDD) patients using cyclic alternating pattern (CAP) analysis, and to determine the usefulness of CAP analysis in evaluating treatment effect.

METHODS

Baseline sleep-staging data and CAP analysis of NREM sleep was compared in 15 MDD patients (Hamilton depression scale score>20) and normal controls. Longitudinal evaluation of sleep changes using similar analysis during a treatment trial was also performed.

ANALYSIS

A single-blinded researcher scored and analyzed the sleep of MDD and age-matched normal controls at baseline and during a treatment trial using the international scoring system as well as CAP analysis.

RESULTS

MDD patients had evidence of disturbed sleep with both analyses, but CAP analysis revealed more important changes in NREM sleep of MDD patients at baseline than did conventional sleep staging. There was a significant decrease in CAP rate, time, and cycle and disturbances of phase A subtype of CAP. NREM abnormalities, observed by CAP analysis, during the treatment trial paralleled subjective responses. Analysis of subtype A phase of CAP demonstrated better sleep improvement.

CONCLUSION

CAP analysis demonstrated the presence of more important NREM sleep disturbances in MDD patients than did conventional sleep staging, suggesting the involvement of slow wave sleep (SWS) in the sleep impairment of MDD patients. Improvement of NREM sleep paralleled subjective mood improvement and preceded REM sleep improvement. CAP analysis allowed objective investigation of the effect of treatment on sleep disturbances.

A final common pathway for depression? Progress toward a general conceptual framework

Functional neuroimaging studies of depressed patients have converged with functional brain mapping studies of depressed animals in showing that depression is accompanied by a hypoactivity of brain regions involved in positively motivated behavior together with a hyperactivity in regions involved in stress responses. Both sets of changes are reversed by diverse antidepressant treatments. It has been proposed that this neural pattern underlies the symptoms common to most forms of the depression, which are the loss of positively motivated behavior and increased stress. The paper discusses how this framework can organize diverse findings ranging from effects of monoamine neurotransmitters, cytokines, corticosteroids and neurotrophins on depression. The hypothesis leads to new insights concerning the relationship between the prolonged inactivity of the positive motivational network during a depressive episode and the loss of neurotrophic support, the potential antidepressant action of corticosteroid treatment, and to the key question of whether antidepressants act by inhibiting the activity of the stress network or by enhancing the activity of the positive motivational system.

Characterization of the sleep EEG in acutely depressed men using detrended fluctuation analysis

OBJECTIVE

The aim of the present paper is to study the fluctuations of the sleep EEG over various time scales during a specific pathological condition: major depressive episode. Focus is made on scaling behaviour, which is the signature of the absence of characteristic time scale, and the presence of long-range correlations associated to physiological constancy preservation, variability reduction and mostly adaptability.

METHODS

Whole night sleep electroencephalogram signals were recorded in 24 men: 10 untreated patients with a major depressive episode (41.70+/-8.11 years) and 14 healthy subjects (42.43+/-5.67 years). Scaling in these time series was investigated with detrended fluctuation analysis (time range: 0.16-2.00s). Scaling exponents (alpha) were determined in stage 2, slow wave sleep (stages 3 and 4) and during REM sleep. Forty-five epochs of 20s were chosen randomly in each of these stages.

RESULTS

The median values of alpha were lower in patients during stage 2 and SWS.

CONCLUSIONS

Major depressive episodes are characterized by a modification in the correlation structure of the sleep EEG time series. The finding which shows decreasing rate of the temporal correlations being different within the two groups in stage 2 and SWS provides an electrophysiologic argument that the underlying neuronal dynamics are modified during acute depression.

SIGNIFICANCE

The observed modifications in scaling behaviour in acutely depressed patients could be an explanation of the sleep fragmentation and instability found during major depressive episode.

Alteration of sleep microstructure in psychiatric disorders

Macrostructure describes the temporal organization of sleep based on successive epochs of conventional length, while microstructure, which is analyzed on the basis of the scoring of phasic events, provides additional important dynamic characteristics in the evaluation of both normal and pathological sleep processes. Relationships between sleep, sleep disorders, and psychiatric disorders are quite complex, and it clearly appears that both the macrostructure and the microstructure of sleep are valuable physiologically and clinically. Psychiatric patients often complain about their sleep, and they may show sleep abnormalities that increase with the severity of their illness. Changes in the occurrence and frequency of phasic events during sleep may be associated with specific psychiatric disorders, and may provide valuable information for both diagnosis and prognosis of these disorders.

Sleep microarchitecture in childhood and adolescent depression: temporal coherence

Previous work has indicated that low temporal coherence of ultradian sleep EEG rhythms is characteristic of depressed patients and women in particular. It may also be evident in depressed children and adolescents, although most published studies are limited in sample size. The present study evaluated temporal coherence of sleep EEG rhythms in 173 children and adolescents 8-17 years of age, including 97 who met criteria for major depressive disorder (MDD) and were symptomatic but unmedicated at the time of study and 76 healthy controls. Temporal coherence of all-night sleep EEG rhythms was evaluated on the second of two nights in the laboratory. Data were coded for diagnostic group, gender and age and subjected to MANOVAs. Temporal coherence was significantly lower in adolescents with MDD, compared to healthy controls. Findings were most robust for coherence between left and right beta and between delta and beta in both hemispheres. Both gender and age strongly influenced between-group differences, with the lowest temporal coherence among MDD girls, even in those under 13 years of age. In conclusion, early onset depression is associated with a reduction in synchronization of sleep EEG rhythms that shows a differential maturational course in boys and girls.

All-night EEG power spectral analysis of the cyclic alternating pattern components in young adult subjects

OBJECTIVE

To analyze in detail the frequency content of the different EEG components of the Cyclic Alternating Pattern (CAP), taking into account the ongoing EEG background and the nonCAP (NCAP) periods in the whole night polysomnographic recordings of normal young adults.

METHODS

Sixteen normal healthy subjects were included in this study. Each subject underwent one polysomnographic night recording; sleep stages were scored following standard criteria. Subsequently, each CAP A phase was detected in all recordings, during NREM sleep, and classified into 3 subtypes (A1, A2, and A3). The same channel used for the detection of CAP A phases (C3/A2 or C4/A1) was subdivided into 2-s mini-epochs. For each mini-epoch, the corresponding CAP condition was determined and power spectra calculated in the frequency range 0.5-25 Hz. Average spectra were obtained for each CAP condition, separately in sleep stage 2 and SWS, for each subject. Finally, the first 6h of sleep were subdivided into 4 periods of 90 min each and the same spectral analysis was performed for each period.

RESULTS

During sleep stage 2, CAP A subtypes differed from NCAP periods for all frequency bins between 0.5 and 25 Hz; this difference was most evident for the lowest frequencies. The B phase following A1 subtypes had a power spectrum significantly higher than that of NCAP, for frequencies between 1 and 11 Hz. The B phase after A2 only differed from NCAP for a small but significant reduction in the sigma band power; this was evident also after A3 subtypes. During SWS, we found similar results. The comparison between the different CAP subtypes also disclosed significant differences related to the stage in which they occurred. Finally, a significant effect of the different sleep periods was found on the different CAP subtypes during sleep stage 2 and on NCAP in both sleep stage 2 and SWS.

CONCLUSIONS

CAP subtypes are characterized by clearly different spectra and also the same subtype shows a different power spectrum, during sleep stage 2 or SWS. This finding underlines a probable different functional meaning of the same CAP subtype during different sleep stages. We also found 3 clear peaks of difference between CAP subtypes and NCAP in the delta, alpha, and beta frequency ranges which might indicate the presence of 3 frequency components characterizing CAP subtypes, in different proportion in each of them. The B component of CAP differs from NCAP because of a decrease in power in the sigma frequency range.

SIGNIFICANCE

This study shows that A components of CAP might correspond to periods in which the very-slow delta activity of sleep groups a range of different EEG activities, including the sigma and beta bands, while the B phase of CAP might correspond to a period in which this activity is quiescent or inhibited.

Inter-rater reliability of sleep cyclic alternating pattern (CAP) scoring and validation of a new computer-assisted CAP scoring method

OBJECTIVE

To assess inter-rater reliability between different scorers, from different qualified sleep research groups, in scoring visually the Cyclic Alternating Pattern (CAP), to evaluate the performances of a new tool for the computer-assisted detection of CAP, and to compare its output with the data from the different scorers.

METHODS

CAP was scored in 11 normal sleep recordings by four different raters, coming from three sleep laboratories. CAP was also scored in the same recordings by means of a new computer-assisted method, implemented in the Hypnolab 1.2 (SWS Soft, Italy) software. Data analysis was performed according to the following steps: (a) the inter-rater reliability of CAP parameters between the four different scorers was carried out by means of the Kendall W coefficient of concordance; (b) the analysis of the agreement between the results of the visual and computer-assisted analysis of CAP parameters was also carried out by means of the Kendall W coefficient; (c) a 'consensus' scoring was obtained, for each recording, from the four scorings provided by the different raters, based on the score of the majority of scorers; (d) the degree of agreement between each scorer and the consensus score and between the computer-assisted analysis and the consensus score was quantified by means of the Cohen's k coefficient; (e) the differences between the number of false positive and false negative detections obtained in the visual and in the computer-assisted analysis were also evaluated by means of the non-parametric Wilcoxon test.

RESULTS

The inter-rater reliability of CAP parameters quantified by the Kendall W coefficient of concordance between the four different scorers was high for all the parameters considered and showed values above 0.9 for total CAP time, CAP time in sleep stage 2 and percentage of A phases in sequence; also CAP rate showed a high value (0.829). The most important global parameters of CAP, including total CAP rate and CAP time, scored by the computer-assisted analysis showed a significant concordance with those obtained by the raters. The agreement between the computer-assisted analysis and the consensus scoring for the assignment of the CAP A phase subtype was not distinguishable from that expected from a human scorer. However, the computer-assisted analysis provided a number of false positives and false negatives significantly higher than that of the visual scoring of CAP.

CONCLUSIONS

CAP scoring shows good inter-rater reliability and might be compared in different laboratories the results of which might also be pooled together; however, caution should always be taken because of the variability which can be expected in the classical sleep staging. The computer-assisted detection of CAP can be used with some supervision and correction in large studies when only general parameters such as CAP rate are considered; more editing is necessary for the correct use of the other results.

SIGNIFICANCE

This article describes the first attempt in the literature to evaluate in a detailed way the inter-rater reliability in scoring CAP parameters of normal sleep and the performances of a human-supervised computerized automatic detection system.

Is insomnia a neurophysiological disorder? The role of sleep EEG microstructure

Unlike other sleep disorders, such as sleep-related breathing disorders and periodic limb movement (PLM), the nature and severity of which are quantified by specific respiratory and motor indexes, no apparent organ dysfunction underlies several cases of insomnia (in particular primary insomnia), which can be objectively diagnosed only through the structural alterations of sleep. Polysomnography (PSG) investigation indicates that insomnia is the outcome of a neurophysiological disturbance that impairs the regulatory mechanisms of sleep control, including sleep duration, intensity, continuity and stability. In particular, analysis of sleep microstructure has permitted to establish that etiologic factors of different nature (including depressive disorders) exert a common destabilizing action on sleep, which is reflected in an increase of cyclic alternating pattern (CAP) rate. These premises allow us to attribute a more objective identity to insomnia, which risks otherwise to be considered as an unexplainable mental complaint. In conclusion, PSG remains the "gold standard" for measuring sleep, and especially insomnia.

Microstructure of sleep in eating disorders: preliminary results

Attempts to analyse the sleep structure of patients with eating disorders have so far led to conflicting results. Polygraphic findings suggest that patients with bulimia nervosa are not easily distinguishable from age-matched controls, whereas anorexic patients show some abnormalities in sleep efficiency and sleep architecture. Nevertheless, both bulimic and anorexic patients complain of poor quality sleep. The aim of this study was to evaluate the microstructure of sleep in anorexia and bulimia by analysing arousal (following the rules of the American Sleep Disorders Association) and the cyclic alternating pattern (CAP). The results confirmed the presence of sleep disturbances in eating disordered patients: an increase in arousal length and the CAP rate. They also seem to confirm the findings of previous studies suggesting that altered sleep in eating disordered patients may be related to their body mass index (BMI) and psychopathological status.