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Published January 14, 2025 | Version v1
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Neurophysiological correlates of ketamine-induced dissociative state in bipolar disorder: insights from real-world clinical settings

Description

Ketamine, a dissociative compound, shows promise in treating mood disorders, including treatment-resistant depression (TRD) and bipolar disorder (BD). Despite its therapeutic potential, the neurophysiological mechanisms underlying ketamine’s effects are not fully understood. This study explored acute neurophysiological changes induced by subanesthetic doses of ketamine in BD patients with depression using electroencephalography (EEG) biomarkers. A cohort of 30 BD (F = 12) inpatients with TRD undergoing ketamine treatment was included in the study. EEG recordings were performed during one of the ketamine infusions with doses ranging from 0.5 to 1 mg/kg, and subjective effects were evaluated using the Clinician-Administered Dissociative States Scale (CADSS). Both rhythmic and arrhythmic features were extrapolated from the EEG signal. Patients who exhibited a clinical response to ketamine treatment within one week were classified as early responders (ER), whereas those who responded later were categorized as late responders (LR). Ketamine reduced low-frequency spectral power density while increasing gamma oscillatory power. Additionally, ketamine flattened the slope of the power spectra, indicating altered scale-free dynamics. Ketamine also increased brain signal entropy, particularly in high-frequency bands. Notably, LR exhibited greater EEG changes compared to ER, suggesting endophenotypic differences in treatment sensitivity. These findings provide valuable insights into the neurophysiological effects of ketamine in BD depression, highlighting the utility of EEG biomarkers for assessing ketamine’s
therapeutic mechanisms in real-world clinical settings. Understanding the neural correlates of ketamine response may contribute to personalized treatment approaches and improved management of mood disorders.

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DOI
10.1038/s41380-025-02889-2

Related works

Is derived from
Journal article: 10.3390/brainsci14070658 (DOI)

Funding

European Commission
OPADE - Optimise and predict antidepressant efficacy for patient with major depressive disorders using multi-omics analysis and AI-predictive tool 101095436

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