Insomnia and depression

doi:10.1016/S1389-9457(08)70010-8

Sleep Medicine

Volume 9, Supplement 1, September 2008, Pages S3-S9

https://doi.org/10.1016/S1389-9457(08)70010-8 Get rights and content

Abstract

It is clear that insomnia and depression are intimately related, which may suggest an overlapping neurobiology. Although much progress has been made toward understanding how these disorders relate to each other, the exact neurobiological mechanisms that link them remain elusive. Sleep changes in depression may be associated with abnormal neurotransmission, genetic polymorphisms, HPA overactivity, impaired plasticity, or most likely a combination of factors. It is therefore crucial that sleep assessments go beyond traditional polysomnography to include a more expanded set of objective measures in the hope that these will uncover the common neurobiology that is thought to underlie insomnia and depression.

References (55)

N Breslau et al.
Sleep disturbance and psychiatric disorders: a longitudinal epidemiological study of young adults
Biol Psychiatry
(1996)
D Riemann et al.
Sleep and depression–results from psychobiological studies: an overview
Biol Psychol
(2001)
D Riemann
Insomnia and comorbid psychiatric disorders
Sleep Med
(2007)
ML Perlis et al.
Which depressive symptoms are related to which sleep electroencephalographic variables?
Biol Psychiatry
(1997)
MM Ohayon
Epidemiology of insomnia: what we know and what we still need to learn
Sleep Med Rev
(2002)
MJ Peterson et al.
Sleep in mood disorders
Psychiatr Clin North Am
(2006)
MM Ohayon et al.
Prevalence of major depressive disorder in the general population of South Korea
J Psychiatr Res
(2006)
MM Ohayon et al.
Place of chronic insomnia in the course of depressive and anxiety disorders
J Psychiatr Res
(2003)
AM Bao et al.
The stress system in depression and neurodegeneration: focus on the human hypothalamus
Brain Res Rev
(2008)
EJ Nestler et al.
Neurobiology of depression
Neuron
(2002)

A Steiger

Neurochemical regulation of sleep

J Psychiatr Res

(2007)

HK Manji et al.

Enhancing neuronal plasticity and cellular resilience to develop novel, improved therapeutics for difficult-to-treat depression

Biol Psychiatry

(2003)

CA Zarate et al.

Cellular plasticity cascades: targets for the development of novel therapeutics for bipolar disorder

Biol Psychiatry

(2006)

G Tononi et al.

Sleep function and synaptic homeostasis

Sleep Med Rev

(2006)

SV Argyropoulos et al.

Correlation of subjective and objective sleep measurements at different stages of the treatment of depression

Psychiatry Res

(2003)

U Hemmeter et al.

Serial partial sleep deprivation as adjuvant treatment of depressive insomnia

Prog Neuropsychopharmacol Biol Psychiatry

(1995)

J Buckelmuller et al.

Traitlike individual differences in the human sleep electroencephalogram

Neuroscience

(2006)

AA Borbely et al.

All-night spectral analysis of the sleep EEG in untreated depressives and normal controls

Psychiatry Res

(1984)

DJ Kupfer et al.

Electroencephalographic sleep studies in depressed patients during long-term recovery

Psychiatry Res

(1993)

R Luthringer et al.

All-night EEG spectral analysis as a tool for the prediction of clinical response to antidepressant treatment

Biol Psychiatry

(1995)

RD Jindal et al.

Electroencephalographic sleep profiles in single-episode and recurrent unipolar forms of major depression: II. Comparison during remission

Biol Psychiatry

(2002)

R Armitage et al.

Digital period analysis of EEG in depression: periodicity, coherence, and interhemispheric relationships during sleep

Prog Neuropsychopharmacol Biol Psychiatry

(1993)

R Armitage et al.

Slow-wave activity in NREM sleep: sex and age effects in depressed outpatients and healthy controls

Psychiatry Res

(2000)

MK Fulton et al.

Sleep electroencephalographic coherence abnormalities in individuals at high risk for depression: a pilot study

Biol Psychiatry

(2000)

RL Morehouse et al.

Temporal coherence in ultradian sleep EEG rhythms in a never-depressed, high-risk cohort of female adolescents

Biol Psychiatry

(2002)

LA Finelli et al.

Individual ‘fingerprints’ in human sleep EEG topography

Neuropsychopharmacology

(2001)

DE Ford et al.

Epidemiologic study of sleep disturbances and psychiatric disorders. An opportunity for prevention?

J Am Med Assoc

(1989)

Cited by (182)

Sleep rebound leads to marked recovery of prolonged sleep deprivation-induced adversities in the stress response and hippocampal neuroplasticity of male rats
2024, Journal of Affective Disorders
Sleep disturbances are not only frequent symptoms, but also risk factors for major depressive disorder. We previously reported that depressed patients who experienced “Hypersomnia” showed a higher and more rapid response rate under paroxetine treatment, but the underlying mechanism remains unclear. The present study was conducted to clarify the beneficial effects of sleep rebound through an experimental “Hypersomnia” rat model on glucocorticoid and hippocampal neuroplasticity associated with antidepressive potency.
Thirty-four male Sprague-Dawley rats were subjected to sham treatment, 72-h sleep deprivation, or sleep deprivation and subsequent follow-up for one week. Approximately half of the animals were sacrificed to evaluate adrenal weight, plasma corticosterone level, hippocampal content of mRNA isoforms, and protein of the brain-derived neurotrophic factor (Bdnf) gene. In the other half of the rats, Ki-67- and doublecortin (DCX)-positive cells in the hippocampus were counted via immunostaining to quantify adult neurogenesis.
Prolonged sleep deprivation led to adrenal hypertrophy and an increase in the plasma corticosterone level, which had returned to normal after one week follow-up. Of note, sleep deprivation-induced decreases in hippocampal Bdnf transcripts containing exons II, IV, VI, and IX and BDNF protein levels, Ki-67-(+)-proliferating cells, and DCX-(+)-newly-born neurons were not merely reversed, but overshot their normal levels with sleep rebound.
The present study did not record electroencephalogram or assess behavioral changes of the sleep-deprived rats.
The present study demonstrated that prolonged sleep deprivation-induced adversities are reversed or recovered by sleep rebound, which supports “Hypersomnia” in depressed patients as having a beneficial pharmacological effect.
Directional associations among real-time activity, sleep, mood, and daytime symptoms in major depressive disorder using actigraphy and ecological momentary assessment
2024, Behaviour Research and Therapy
Previous research has suggested that individuals with major depressive disorder (MDD) experienced alterations in sleep and activity levels. However, the temporal associations among sleep, activity levels, mood, and daytime symptoms in MDD have not been fully investigated. The present study aimed to fill this gap by utilizing real-time data collected across time points and days. 75 individuals with MDD and 75 age- and gender-matched healthy controls were recruited. Ecological momentary assessments (EMA) were adopted to assess real-time mood status for 7 days, and actigraphy was employed to measure day-to-day sleep-activity patterns. Multilevel modeling analyses were performed. Results revealed a bidirectional association between mood/daytime symptoms and activity levels across EMA intervals. Increased activity levels were predictive of higher alert cognition and positive mood, while an increase in positive mood also predicted more increase in activity levels in depressed individuals. A bidirectional association between sleep and daytime symptoms was also found. Alert cognition was found to be predictive of better sleep in the subsequent night. Contrariwise, higher sleep efficiency predicted improved alert cognition and sleepiness/fatigue the next day. A unidirectional association between sleep and activity levels suggested that higher daytime activity levels predicted a larger increase in sleep efficiency among depressed individuals. This study indicated how mood, activity levels, and sleep were temporally and intricately linked to each other in depressed individuals using actigraphy and EMA. It could pave the way for novel and efficacious treatments for depression that target not just mood but sleep and activity levels.
Sleep stage classification using Light Gradient Boost Machine: Exploring feature impact in depressive and healthy participants
2024, Biomedical Signal Processing and Control
This study compared Support Vector Machine (SVM), Random Forest, and Light Gradient Boosting Machine (LightGBM) performance in sleep stage classification for patients with depressive disorder and healthy participants.
Participants were divided into Depressive Disorder (DD) and Healthy Control (HC) groups. Overnight polysomnography recordings were obtained, and features were extracted from physiological signals, demographics, and Hamilton Depression Rating Scale (HDRS). The performance of LightGBM, SVM, and Random Forest was conducted, by using HC group (HC/HC case), DD group (DD/DD case), and HC and DD groups (HC/DD case), with 10-fold cross-validation.
The classification accuracy of LightGBM for the DD/DD and HC/HC cases (74.78% and 82.12%), respectively, was superior to those of SVM (73.7% and 80.8%) and Random Forest (71.91% and 79.01%). The classification accuracy of LightGBM obtained through HC/DD case (72.25%) was worse than that obtained through DD/DD case (74.78%) to predict sleep stage for subjects with depression. By including demographic features and HDRS score in the analysis, the improvement in the classification accuracy for the DD/DD case (from 74.78% to 76.6%) was greater than that for the HC/HC case (from 82.12% to 82.71%). Feature ranking results revealed that demographic features and HDRS score were the most crucial features for sleep stage classification for the DD/DD case.
The classification accuracy of LightGBM for DD group was lower than that for HC group. HDRS score could improve the classification accuracy for DD group. The findings provide insights for managing depressive disorder patients, particularly with sleep-related conditions.
Identifying the insomnia-related psychological issues associated with hyperarousal: A network perspective
2024, International Journal of Psychophysiology
Hyperarousal, recognized as a fundamental characteristic of insomnia for decades, has yielded limited evidence concerning its direct psychological associations. This study aimed to explore the psychological factors linked to hyperarousal within the framework of interrelated variables. Two independent samples, comprising n = 917 and n = 652 young adults, were included in the study. Employing the first dataset as a discovery sample and the second dataset as a replication sample, network analyses were conducted using 26 variables derived from 17 scales. The objective was to estimate the direct and indirect associations between psychological issues, including hyperarousal and insomnia. Additionally, linear regression analysis was employed to assess the convergence of findings obtained from the network analysis. Network analyses in both samples converged to reveal direct associations between insomnia severity and several psychological factors, including negative sleep beliefs, physical fatigue, insomnia response to stress, hyperarousal, self-reported depression, and mental fatigue. Notably, the nodes with relative importance within the network include trait anxiety, depressive rumination, hyperarousal, perfectionism sub-dimension of concern over mistakes, and private self-consciousness. Hyperarousal is one of the key factors linking insomnia with a variety of psychological issues, including emotion-related factors (rumination, perveived stress), sleep-related factors (dysfunctional sleep beliefs and attitudes, insomnia response to stress, fatigue, chronotype), and self-related factors (self-consciousness, perfectionism). The results suggest that forthcoming strategies for enhancing the treatment efficacy of insomnia could consider supplementary interventions that specifically address hyperarousal, other factors directly linked to insomnia, or the hub nodes within the network.
Resting-state EEG as a potential indicator to predict sleep quality in depressive patients
2023, International Journal of Psychophysiology
To investigate the changes in sleep quality, heart rate variability (HRV) and resting-state electroencephalogram (rsEEG) in patients with major depressive disorder (MDD), and to explore whether HRV and rsEEG may be served as more convenient tools to assess sleep quality in MDD patients.
We included a total of 91 subjects (46 healthy controls and 45 MDD patients) and compared their sleep quality, HRV and power spectra of rsEEG. Correlation analyses were conducted to discuss the relationship between HRV and seven factors of PSQI. Multiple linear regression model was used to examine whether absolute band power could predict sleep quality in MDD patients.
We found higher PSQI scores and lower levels of HRV in depressive individuals compared with healthy controls. In MDD patients, sleep latency was negatively correlated with RMSSD and HF. Delta, theta, and alpha band power of rsEEG were higher in MDD patients. Regression analyses showed delta band power of TP8, as well as theta, alpha band power of AF3 predicts PSQI score of MDD patients.
The findings of our study show that some aspects of sleep problems had negative correlations with parasympathetic activity and the regression model supports that the band power of rsEEG may be used as a potential indicator to evaluate the sleep quality in MDD patients.
Cortical hyperarousal may be one of the reasons leading to poor sleep quality of MDD patients. And resting-state EEG can be used as a potential indicator for clinical assessment of MDD patients' sleep quality.
The relationship between childhood trauma and insomnia among college students with major depressive disorder: Mediation by the role of negative life events and dysfunctional attitudes
2023, Comprehensive Psychiatry
Insomnia is a common problem among patients with major depressive disorder (MDD). According to previous studies, the development and severity of Insomnia are influenced by childhood trauma experience. Furthermore, negative life events and dysfunctional attitudes may also mediate the impact. So, this study aimed to examine the association between childhood trauma, negative life events, dysfunctional attitudes and insomnia and investigate how negative life events and dysfunctional attitudes mediate the relationship between childhood trauma and insomnia in MDD.
This cross-sectional study recruited 621 college students with MDD. The Childhood Trauma Questionnaire (CTQ), Life Event Scale (LES), Dysfunctional Attitude Scale (DAS), Insomnia Severity Index (ISI), and Hamilton Depression Scale-17 (HAMD-17) were used to assess participants' psychosocial factors. Descriptive analysis, Chi-square test, t-test, Pearson correlations, and serial mediation analyses were used in data analysis. In order to eliminate the influence of the severity of depression symptoms, severity of depression symptoms was used as a control variable in this study.
There were 166 (26.7%) participants having clinical insomnia (ISI score > 14). After controlling for the effect of severity of depression symptoms, results of serial mediation analyses determined that childhood trauma has a direct (Estimate = 0.109, 95%CI: 0.023,0.190) and indirect (Estimate = 0.090, 95%CI: 0.054,0.137) impact to insomnia. The indirect impact of childhood trauma on insomnia through the pathways of negative life events alone (Estimate = 0.050, 95%CI: 0.024,0.093), dysfunctional attitudes alone (Estimate = 0.027, 95%CI: 0.008,0.050), and negative life events to dysfunctional attitudes (Estimate = 0.013, 95%CI: 0.006,0.024) were significant.
This study demonstrates that screening for childhood trauma should be considered when treating insomnia in college students with MDD. Managing negative life events and dysfunctional attitudes may mitigate the negative impact of childhood trauma on insomnia in college students with MDD.

View all citing articles on Scopus

View full text

Original articleInsomnia and depression

Abstract

Biol Psychiatry

Biol Psychol

Sleep Med

Biol Psychiatry

Sleep Med Rev

Psychiatr Clin North Am

J Psychiatr Res

J Psychiatr Res

Brain Res Rev

Neuron

J Psychiatr Res

Biol Psychiatry

Biol Psychiatry

Sleep Med Rev

Psychiatry Res

Prog Neuropsychopharmacol Biol Psychiatry

Neuroscience

Psychiatry Res

Psychiatry Res

Biol Psychiatry

Biol Psychiatry

Prog Neuropsychopharmacol Biol Psychiatry

Psychiatry Res

Biol Psychiatry

Biol Psychiatry

Neuropsychopharmacology

Epidemiologic study of sleep disturbances and psychiatric disorders. An opportunity for prevention?

J Am Med Assoc

Original article
Insomnia and depression