Hyperarousal-state of Insomnia Disorder in Wake-resting State Quantitative Electroencephalography
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Objective: Insomnia is associated with elevated high-frequency electroencephalogram power in the waking state. Although affective symptoms (e.g., depression and anxiety) are commonly comorbid with insomnia, few reports distinguished objective sleep disturbance from affective symptoms. In this study, we investigated whether daytime electroencephalographic activity explains insomnia, even after controlling for the effects of affective symptoms. Methods: A total of 107 participants were divided into the insomnia disorder (n = 58) and healthy control (n = 49) groups using the Mini-International Neuropsychiatric Interview and diagnostic criteria for insomnia disorder. The participants underwent daytime resting-state electroencephalography sessions (64 channels, eye-closed). Results: The insomnia group showed higher levels of anxiety, depression, and insomnia than the healthy group, as well as increased beta [t(105) = −2.56, p = 0.012] and gamma [t(105) = −2.44, p = 0.016] spectra. Among all participants, insomnia symptoms positively correlated with the intensity of beta (r = 0.28, p < 0.01) and gamma (r = 0.25, p < 0.05) spectra. Through hierarchical multiple regression, the beta power showed the additional ability to predict insomnia symptoms beyond the effect of anxiety (ΔR 2 = 0.041, p = 0.018). Conclusion: Our results showed a significant relationship between beta electroencephalographic activity and insomnia symptoms, after adjusting for other clinical correlates, and serve as further evidence for the hyperarousal theory of insomnia. Moreover, resting-state quantitative electroencephalography may be a supplementary tool to assess insomnia.
Abstract
Objective: Insomnia is associated with elevated high-frequency electroencephalogram power in the waking state. Although affective symptoms (e.g., depression and anxiety) are commonly comorbid with insomnia, few reports distinguished objective sleep disturbance from affective symptoms. In this study, we investigated whether daytime electroencephalographic activity explains insomnia, even after controlling for the effects of affective symptoms. Methods: A total of 107 participants were divided into the insomnia disorder (n = 58) and healthy control (n = 49) groups using the Mini-International Neuropsychiatric Interview and diagnostic criteria for insomnia disorder. The participants underwent daytime resting-state electroencephalography sessions (64 channels, eye-closed). Results: The insomnia group showed higher levels of anxiety, depression, and insomnia than the healthy group, as well as increased beta [t(105) = −2.56, p = 0.012] and gamma [t(105) = −2.44, p = 0.016] spectra. Among all participants, insomnia symptoms positively correlated with the intensity of beta (r = 0.28, p < 0.01) and gamma (r = 0.25, p < 0.05) spectra. Through hierarchical multiple regression, the beta power showed the additional ability to predict insomnia symptoms beyond the effect of anxiety (ΔR 2 = 0.041, p = 0.018). Conclusion: Our results showed a significant relationship between beta electroencephalographic activity and insomnia symptoms, after adjusting for other clinical correlates, and serve as further evidence for the hyperarousal theory of insomnia. Moreover, resting-state quantitative electroencephalography may be a supplementary tool to assess insomnia.
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