The dichotomy between low frequency and delta waves in human sleep: A reappraisal

Intracranial Directed Connectivity Links Subregions of the Prefrontal Cortex to Major Depression

Understanding the neural basis of major depressive disorder (MDD) is vital to guiding neuromodulatory treatments. The available evidence supports the hypothesis that MDD is fundamentally a disease of cortical disinhibition, where breakdowns of inhibitory neural systems lead to diminished emotion regulation and intrusive ruminations. Recent research also points towards network changes in the brain, especially within the prefrontal cortex (PFC), as primary sources of MDD etiology. However, due to limitations in spatiotemporal resolution and clinical opportunities for intracranial recordings, this hypothesis has not been directly tested. We recorded intracranial EEG from the dorsolateral (dlPFC), orbitofrontal (OFC), and anterior cingulate cortices (ACC) in neurosurgical patients with MDD. We measured daily fluctuations in self-reported depression severity alongside directed connectivity between these PFC subregions. We focused primarily on delta oscillations (1-3 Hz), which have been linked to GABAergic inhibitory control and intracortical communication. Depression symptoms worsened when connectivity within the left vs. right PFC became imbalanced. In the left hemisphere, all directed connectivity towards the ACC, from the dlPFC and OFC, was positively correlated with depression severity. In the right hemisphere, directed connectivity between the OFC and dlPFC increased with depression severity as well. This is the first evidence that delta oscillations flowing between prefrontal subregions transiently increase intensity when people are experiencing more negative mood. These findings support the overarching hypothesis that MDD worsens with prefrontal disinhibition.

Alterations of neural network organization during REM sleep in women: implication for sex differences in vulnerability to mood disorders

BACKGROUND

Sleep plays an important role in vulnerability to mood disorders. However, despite the existence of sex differences in vulnerability to mood disorders, no study has yet investigated the sex effect on sleep network organization and its potential involvement in vulnerability to mood disorders. The aim of our study was to empirically investigate the sex effect on network organization during REM and slow-wave sleep using the effective connectivity measured by Granger causality.

METHODS

Polysomnographic data from 44 healthy individuals (28 men and 16 women) recruited prospectively were analysed. To obtain the 19 × 19 connectivity matrix of all possible pairwise combinations of electrodes by Granger causality method from our EEG data, we used the Toolbox MVGC multivariate Granger causality. The computation of the network measures was realized by importing these connectivity matrices into EEGNET Toolbox.

RESULTS

In men and women, all small-world coefficients obtained are compatible with a small-world network organization during REM and slow-wave sleep. However, compared to men, women present greater small-world coefficients during REM sleep as well as for all EEG bands during this sleep stage, which indicates the presence of a small-world network organization less marked during REM sleep as well as for all EEG bands during this sleep stage in women. In addition, in women, these small-world coefficients during REM sleep as well as for all EEG bands during this sleep stage are positively correlated with the presence of subclinical symptoms of depression.

CONCLUSIONS

Thus, the highlighting of these sex differences in network organization during REM sleep indicates the presence of differences in the global and local processing of information during sleep between women and men. In addition, this small-world network organization less marked during REM sleep appears to be a marker of vulnerability to mood disorders specific to women, which opens up new perspectives in understanding sex differences in the occurrence of mood disorders.

Spatio-temporal dynamics of EEG features during sleep in major depressive disorder after treatment with escitalopram: a pilot study

BACKGROUND

Previous studies have shown escitalopram is related to sleep quality. However, effects of escitalopram on dynamics of electroencephalogram (EEG) features especially during different sleep stages have not been reported. This study may help to reveal pharmacological mechanism underlying escitalopram treatment.

METHODS

The spatial and temporal responses of patients with major depressive disorder (MDD) to escitalopram treatment were analyzed in this study. Eleven MDD patients and eleven healthy control subjects who completed eight weeks' treatment of escitalopram were included in the final statistics. Six-channel sleep EEG signals were acquired during sleep. Power spectrum and nonlinear dynamics were used to analyze the spatio-temporal dynamics features of the sleep EEG after escitalopram treatment.

RESULTS

For temporal dynamics: after treatment, there was a significant increase in the relative energy (RE) of δ1 band (0.5 - 2 Hz), accompanied by a significant decrease in the RE of β2 band (20 - 30 Hz). Lempel-Ziv complexity and Co - complexity values were significantly lower. EEG changes at different sleep stages also showed the same regulation as throughout the night sleep. For spatio dynamics: after treatment, the EEG response of the left and right hemisphere showed asymmetry. Regarding band-specific EEG complexity estimations, δ1 and β2 in stage-1 and δ1 in stage-2 sleep stage in frontal cortex is found to be much more sensitive to escitalopram treatment in comparison to central and occipital cortices.

CONCLUSIONS

The sleep quality of MDD patients improved, EEG response occurred asymmetry in left and right hemispheres due to escitalopram treatment, and frontal cortex is found to be much more sensitive to escitalopram treatment. These findings may contribute to a comprehensive understanding of the pharmacological mechanism of escitalopram in the treatment of depression.

Impact of number of sleep ultradian cycles on polysomnographic parameters related to REM sleep in major depression: Implications for future sleep research in psychiatry

Given the contradictory data on REMS alterations in major depression, the aim of this study was to empirically demonstrate that based on the number of sleep ultradian cycles, it was possible to highlight different subtypes of major depression characterized by specific patterns of REMS alterations. Demographic and polysomnographic data from 211 individuals (30 healthy controls and 181 untreated major depressed individuals) recruited from the sleep laboratory database were analyzed. Major depressed individuals with sleep ultradian cycles <4 showed alterations consistent with REMS deficiency (non-shortened REM latency as well as decrease in REMS percentage, REMS duration and REMS/NREMS ratio) whereas major depressed individuals with sleep ultradian cycles >4 showed alterations consistent with REMS disinhibition (shortened REM latency as well as increase in REMS percentage, REMS duration and REMS/NREMS ratio). Regarding major depressed individuals with 4 sleep ultradian cycles, their REMS alterations were intermediate to those present in major depressed individuals with sleep ultradian cycles <4 and >4. Thus, in major depressed individuals, the highlighting of this heterogeneity of REMS alterations based on the number of sleep ultradian cycles seems to suggest the involvement of distinct pathophysiological mechanisms and could open new perspectives for future sleep research in psychiatry.

Alterations of neural network organisation during rapid eye movement sleep and slow-wave sleep in major depression: Implications for diagnosis, classification, and treatment

The aim of this study was to empirically investigate the network organisation during rapid eye movement sleep (REMS) and slow-wave sleep (SWS) using the effective connectivity measured using the Granger causality to identify new potential biomarkers for the diagnosis, classification, and potential favourable response to treatment in major depression. Polysomnographic data were analysed from 24 healthy individuals and 16 major depressed individuals recruited prospectively. To obtain the 19×19 connectivity matrix of all possible pairwise combinations of electrodes by the Granger causality method from our electroencephalographic data, we used the Toolbox MVGC multivariate Granger causality. The computation of network measures was realised by importing these connectivity matrices into the EEGNET Toolbox. Major depressed individuals (versus healthy individuals) and those with endogenous depression (versus those with neurotic depression) present alterations of small-world network organisation during REMS, whereas major depressed individuals with potential favourable response to electroconvulsive therapy (versus those with potential unfavourable response) have a less efficient small-world network organisation during SWS. Thus, alterations in network organisation during REMS could be biomarkers for the diagnosis and classification of major depressive episodes, whereas alterations of network organisation during SWS could be a biomarker to predict potential favourable response to treatment by electroconvulsive therapy.

Sleep ultradian cycling: Statistical distribution and links with other sleep variables, depression, insomnia and sleepiness-A retrospective study on 2,312 polysomnograms

The number of alternations between Non-Rapid Eye Movement (NREM) sleep and Rapid Eye Movement (REM) sleep in humans is usually considered to consist of 4-5 cycles of about 90 minutes duration per night. Previous studies by our group showed a normal distribution on 26 healthy human subjects. The present study retrospectively analyzes the polysomnograms of 2,312 unmedicated patients who were admitted for medical and/or psychiatric reasons in the Erasme University Hospital between 2003 and 2014. The normal distribution of the Number of Cycles and Mean Cycle Duration was confirmed. Q-Q plots were very close to linearity. This distribution allows the use of these variables in parametric comparisons. The Number of Cycles per night and the Mean Cycle Duration showed predominant links with REM sleep-related variables, such as the REM Latency, REM sleep duration, the REM/NREM sleep ratio. None of these variables was associated with the diagnosis of Major Depressive Disorder, nor the intensity of Depression as measured by the Beck Depression Inventory (short version). On the other hand, the diagnosis of Major Depressive Disorder was significantly associated with the Insomnia Severity Index and correlated with the intensity of depressive symptoms (Beck Depression Inventory).

Prevalence and risk factors of excessive daytime sleepiness in major depression: A study with 703 individuals referred for polysomnography

BACKGROUND

To date, few studies have investigated the prevalence and risk factors of excessive daytime sleepiness (EDS) in major depression. Thus, the aim of the present study was to examine the prevalence and risk factors of EDS in a large sample of individuals with major depression.

METHODS

Data from 703 individuals with major depression were retrospectively collected from the sleep laboratory research database of Erasme Hospital for analysis. A score of > 10 on the Epworth Sleepiness Scale was used as the cut-off for EDS. Logistic regression analyses were conducted to examine the clinical and demographic risk factors of EDS in major depression.

RESULTS

The prevalence of EDS in our sample was 50.8%. Multivariate logistic regression analysis revealed that the following were significant risk factors of EDS in major depression: non-use of short to intermediate half-life benzodiazepine receptor agonists, BMI ≥ 25 kg/m², age < 60 years, C-reactive protein > 7 mg/L, Beck Depression Inventory score ≥ 16, atypical depression, apnea-hypopnea index ≥ 15/h, and use of selective serotonin reuptake inhibitors or serotonin-norepinephrine reuptake inhibitors.

LIMITATIONS

To evaluate EDS, we used the Epworth Sleepiness Scale, which only allows for a subjective measure of daytime sleepiness.

CONCLUSION

EDS is a common symptom in individuals with major depression. In this subpopulation, interventions are possible for most risk factors of EDS, which justifies improved management of this symptom to avoid its negative consequences.

Neuroendocrine Control of Sleep

Sleep is a phenomenon in animal behavior as enigmatic as it is ubiquitous, and one deeply tied to endocrine function. Though there are still many unanswered questions about the neurochemical basis of sleep and its functions, extensive interactions have been identified between sleep and the endocrine system, in both the endocrine system's effect on sleep and sleep's effect on the endocrine system. Unfortunately, until recent years, much research on sleep behavior largely disregarded its connections with the endocrine system. Use of both clinical studies and rodent models to investigate interactions between neuroendocrine function, including biological sex, and sleep therefore presents a promising area of further exploration. Further investigation of the neurobiological and neuroendocrine basis of sleep could have wide impact on a number of clinical and basic science fields. In this review, we summarize the state of basic sleep biology and its connections to the field of neuroendocrine biology, as well as suggest key future directions for the neuroendocrine regulation of sleep that may significantly impact new therapies for sleep disorders in women and men.

The sleep network organization during slow-wave sleep is more stable with age and has small-world characteristics more marked than during REM sleep in healthy men

Sleep plays an important role in cognitive functioning. However, few studies have investigated the sleep network organization. The aim of our study was to empirically investigate the presence and the stability with age of a small-world network organization during REM and slow-wave sleep using the effective connectivity measured by the Granger causality. Polysomnographic data from 30 healthy men recruited prospectively were analysed. To obtain the 19 × 19 connectivity matrix of all possible pairwise combinations of electrodes by the Granger causality method from our EEG data, we used the Toolbox MVGC multivariate Granger causality. The computation of the network measures was realised by importing these connectivity matrices into the EEGNET Toolbox. Even if all small-world coefficients obtained are compatible with a small-world network organization during REM and slow-wave sleep, slow-wave sleep seems to have a small-world network organization more marked than REM sleep. Moreover, the sleep network organization is affected greater by age during REM sleep than during slow-wave sleep. In healthy individuals, the highlighting of a sleep network organization during slow-wave sleep more stable with age and with small-world characteristics more marked than during REM sleep may help to better understand the global and local processing of information during sleep.