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      Suvorexant alters dynamics of the sleep-electroencephalography-power spectrum and depressive-symptom trajectories during inpatient opioid withdrawal

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          Abstract

          Study Objectives

          Opioid withdrawal is an aversive experience that often exacerbates depressive symptoms and poor sleep. The aims of the present study were to examine the effects of suvorexant on oscillatory sleep-electroencephalography (EEG) band power during medically managed opioid withdrawal, and to examine their association with withdrawal severity and depressive symptoms.

          Methods

          Participants with opioid use disorder (N = 38: age-range:21–63, 87% male, 45% white) underwent an 11-day buprenorphine taper, in which they were randomly assigned to suvorexant (20 mg [n = 14] or 40 mg [n = 12]), or placebo [n = 12], while ambulatory sleep-EEG data was collected. Linear mixed-effect models were used to explore: (1) main and interactive effects of drug group, and time on sleep-EEG band power, and (2) associations between sleep-EEG band power change, depressive symptoms, and withdrawal severity.

          Results

          Oscillatory spectral power tended to be greater in the suvorexant groups. Over the course of the study, decreases in delta power were observed in all study groups (β = −189.082, d = −0.522, p = <0.005), increases in beta power (20 mg: β = 2.579, d = 0.413, p = 0.009 | 40 mg β = 5.265, d = 0.847, p < 0.001) alpha power (20 mg: β = 158.304, d = 0.397, p = 0.009 | 40 mg: β = 250.212, d = 0.601, p = 0.001) and sigma power (20 mg: β = 48.97, d = 0.410, p < 0.001 | 40 mg: β = 71.54, d = 0.568, p < 0.001) were observed in the two suvorexant groups. During the four-night taper, decreases in delta power were associated with decreases in depressive symptoms (20 mg: β = 190.90, d = 0.308, p = 0.99 | 40 mg: β = 433.33, d = 0.889 p = <0.001), and withdrawal severity (20 mg: β = 215.55, d = 0.034, p = 0.006 | 40 mg: β = 192.64, d = −0.854, p = <0.001), in both suvorexant groups and increases in sigma power were associated with decreases in withdrawal severity (20 mg: β = −357.84, d = −0.659, p = 0.004 | 40 mg: β = −906.35, d = −1.053, p = <0.001). Post-taper decreases in delta (20 mg: β = 740.58, d = 0.964 p = <0.001 | 40 mg: β = 662.23, d = 0.882, p = <0.001) and sigma power (20 mg only: β = 335.54, d = 0.560, p = 0.023) were associated with reduced depressive symptoms in the placebo group.

          Conclusions

          Results highlight a complex and nuanced relationship between sleep-EEG power and symptoms of depression and withdrawal. Changes in delta power may represent a mechanism influencing depressive symptoms and withdrawal.

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          Most cited references72

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          The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research

          Despite the prevalence of sleep complaints among psychiatric patients, few questionnaires have been specifically designed to measure sleep quality in clinical populations. The Pittsburgh Sleep Quality Index (PSQI) is a self-rated questionnaire which assesses sleep quality and disturbances over a 1-month time interval. Nineteen individual items generate seven "component" scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. The sum of scores for these seven components yields one global score. Clinical and clinimetric properties of the PSQI were assessed over an 18-month period with "good" sleepers (healthy subjects, n = 52) and "poor" sleepers (depressed patients, n = 54; sleep-disorder patients, n = 62). Acceptable measures of internal homogeneity, consistency (test-retest reliability), and validity were obtained. A global PSQI score greater than 5 yielded a diagnostic sensitivity of 89.6% and specificity of 86.5% (kappa = 0.75, p less than 0.001) in distinguishing good and poor sleepers. The clinimetric and clinical properties of the PSQI suggest its utility both in psychiatric clinical practice and research activities.
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            A RATING SCALE FOR DEPRESSION

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              Parameterizing neural power spectra into periodic and aperiodic components

              Electrophysiological signals exhibit both periodic and aperiodic properties. Periodic oscillations have been linked to numerous physiological, cognitive, behavioral and disease states. Emerging evidence demonstrates that the aperiodic component has putative physiological interpretations and that it dynamically changes with age, task demands and cognitive states. Electrophysiological neural activity is typically analyzed using canonically defined frequency bands, without consideration of the aperiodic (1/f-like) component. We show that standard analytic approaches can conflate periodic parameters (center frequency, power, bandwidth) with aperiodic ones (offset, exponent), compromising physiological interpretations. To overcome these limitations, we introduce an algorithm to parameterize neural power spectra as a combination of an aperiodic component and putative periodic oscillatory peaks. This algorithm requires no a priori specification of frequency bands. We validate this algorithm on simulated data, and demonstrate how it can be used in applications ranging from analyzing age-related changes in working memory to large-scale data exploration and analysis.
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                Author and article information

                Contributors
                (View ORCID Profile)
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                Journal
                SLEEP
                Oxford University Press (OUP)
                0161-8105
                1550-9109
                April 01 2024
                April 12 2024
                January 30 2024
                April 01 2024
                April 12 2024
                January 30 2024
                : 47
                : 4
                Article
                10.1093/sleep/zsae025
                10b39d75-e19c-45f2-b324-1bd79bc463f4
                © 2024

                https://academic.oup.com/pages/standard-publication-reuse-rights

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