Antidepressant effects of selective slow wave sleep deprivation in major depression: A high-density EEG investigation
Introduction
Sleep disturbances are an integral part of the diagnostic criteria for major depression (American Psychiatric Association, 2000, Peterson and Benca, 2006), and sleep may provide biomarkers for treatment response to antidepressant medication (Steiger and Kimura, 2010). Since the initial investigation of a therapeutic benefit of sleep deprivation (SD) in depression (Pflug and Tölle, 1971), many studies have attempted to understand the link between sleep and depression. SD interventions, including total sleep deprivation (TSD), partial sleep deprivation and selective REM sleep deprivation (REMD) can acutely reverse depressive symptoms in approximately 50–60% of patients with major depression (Gillin, 1983, Wu and Bunney, 1990, Kuhs and Tölle, 1991, Wirz-Justice and Van den Hoofdakker, 1999, Hemmeter et al., 2010). Decreased REM sleep latency and increased REM density are hallmark features of depression (Kupfer and Foster, 1972, Benca et al., 1992). Given these features, and the observation of suppressed REM sleep with antidepressant medication (Riemann et al., 1990, Jobert et al., 1999), it has been postulated that selectively suppressing REM sleep may yield an antidepressant response (Vogel et al., 1975).
Slow wave sleep abnormalities, however, are also prominent in depression (Borbély and Wirz-Justice, 1982, Benca et al., 1992) and likely play a role in the modulation of depressive symptomatology. For instance, Nissen et al. (2001) found that a high delta sleep ratio (quotient of slow wave activity [SWA] in the first to the second NREM sleep cycle) on the night prior to SD predicted the antidepressant response. In addition, Duncan et al. (1980) showed that the participants who responded to SD treatment exhibited a greater rebound of slow wave sleep and total sleep time upon recovery sleep compared to their baseline. Finally, Borbély (1987) proposed that sleep in depression is characterized by abnormal slow wave homeostasis, which may be renormalized by SD therapy.
The current study directly investigated the role of slow wave homeostasis in the antidepressant action of SD by using a selective slow wave deprivation (SWD) technique. As the first study to assess the antidepressant effects of SWD in depression, this exploratory investigation had two primary aims. First, we sought to determine the efficacy of the SWD technique in suppressing SWA in depressed participants, to measure the overnight change in depressive symptomatology, and to examine the extent to which these two variables are correlated. Second, we aimed with this initial investigation, using a simple, randomized cross-over design, to provide the foundation for larger, more controlled, comparison-based, double-blinded studies to rigorously assess the usefulness of SWD in treating major depression.
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Participants
17 right-handed individuals (9 female; mean age 23.94 ± 2.31 years, M ± S.E.M.) participated in the study (approved by the Institutional Review Board of the University of Wisconsin–Madison). Participants were medication-free for ≥ 6 months prior to enrollment, diagnosed with Major Depressive Disorder via the Structural Clinical Interview for DSM-IV Axis 1 disorders (SCID) (First et al., 1995) and initially evaluated with the researcher-administered 17-item Hamilton Rating Scale for Depression
Impact of SWD on sleep variables
Measures of sleep quality and quantity for the BSL, SWD, and RCV conditions are summarized in Table 1A. A repeated-measures ANOVA indicated a treatment effect for sleep stage N3 (% of total sleep time [TST]) and SWA. Post-hoc t-tests revealed a 54% and 37% reduction from BSL to SWD for N3% and SWA, respectively. Other sleep measures, including N1%, N2%, REM%, REM latency (REML), and spindle power (12–15 Hz range) were not significantly affected. However, there was also a treatment effect for
Discussion
Extending previous literature on the benefits of SD in major depression, the current investigation found that the SWD intervention selectively suppressed SWA, while leading to a small, but significant reduction in depressive symptomatology.
Conclusion
This initial investigation provides further evidence for the known role of slow waves in depression and suggests that SWD may help acutely reverse depressive symptomatology, although its effect may be less robust compared to the response demonstrated with total sleep deprivation treatments. Future studies will benefit from SWD and/or hdEEG as useful tools in understanding the underlying pathophysiology of depression and the response to sleep deprivation therapy.
Contributors
Eric Landsness contributed to designing the study, performing the experiments and data analysis, and writing the article. Michael Goldstein contributed by conducting the experiments and data analysis, and writing the article. Michael Peterson contributed to the study design, psychiatric evaluations of the participants, and writing the article. Giulio Tononi contributed to the study design and writing the article. Ruth Benca contributed by being the principal investigator, designing the study,
Role of the funding source
This research was funded by the National Institute of Mental Health (5P20MH077967 to GT and RB, and F30MH082601 to EL) and the National Alliance for Research on Schizophrenia and Depression Young Investigator Award to MP. The NIMH and NARSAD had no further role in the study design, data collection, analysis and interpretation of the data, and the decision to submit the paper for publication. Statistical consultation during the data analysis was supported by grant 1UL1RR025011 from the Clinical
Conflict of interest statement
Dr. Peterson has received unrelated research support from Sanofi-Aventis. Dr. Tononi has consulted for Sanofi-Aventis and Takeda, and he is currently the David P. White Chair in Sleep Medicine at the University of Wisconsin–Madison, endowed by Phillips Respironics. Dr. Tononi has also received unrelated research support from Phillips Respironics. Dr. Benca has consulted for Merck and Sanofi-Aventis. The other authors have indicated no financial conflicts of interest.
Acknowledgments
The authors would like to acknowledge Jennifer Noe and Kate Sprecher for their logistical contributions, Meredith Rumble and Elizabeth Frei for conducting the SCID’s, the research assistants for their role in data collection, the polysomnographic technologists from Wisconsin Sleep for working with participants overnight and scoring the sleep recordings, and Tim Wanger for his assistance in processing the hdEEG sleep data.
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