Dataset Information

EEG-vigilance regulation is associated with and predicts ketamine response in major depressive disorder.

ABSTRACT: Ketamine offers promising new therapeutic options for difficult-to-treat depression. The efficacy of treatment response, including ketamine, has been intricately linked to EEG measures of vigilance. This research investigated the interplay between intravenous ketamine and alterations in brain arousal, quantified through EEG vigilance assessments in two distinct cohorts of depressed patients (original dataset: n = 24; testing dataset: n = 24). Clinical response was defined as a decrease from baseline of >33% on the Montgomery-Åsberg Depression Rating Scale (MADRS) 24 h after infusion. EEG recordings were obtained pre-, start-, end- and 24 h post- infusion, and the resting EEG was automatically scored using the Vigilance Algorithm Leipzig (VIGALL). Relative to placebo (sodium chloride 0.9%), ketamine increased the amount of low-vigilance stage B1 at end-infusion. This increase in B1 was positively related to serum concentrations of ketamine, but not to norketamine, and was independent of clinical response. In contrast, treatment responders showed a distinct EEG pattern characterized by a decrease in high-vigilance stage A1 and an increase in low-vigilance B2/3, regardless of whether placebo or ketamine had been given. Furthermore, pretreatment EEG differed between responders and non-responders with responders showing a higher percentage of stage A1 (53% vs. 21%). The logistic regression fitted on the percent of A1 stages was able to predict treatment outcomes in the testing dataset with an area under the ROC curve of 0.7. Ketamine affects EEG vigilance in a distinct pattern observed only in responders. Consequently, the percentage of pretreatment stage A1 shows significant potential as a predictive biomarker of treatment response.Clinical Trials Registration: https://www.clinicaltrialsregister.eu/ctr-search/trial/2013-000952-17/CZ Registration number: EudraCT Number: 2013-000952-17.

SUBMITTER: Ip CT

PROVIDER: S-EPMC10810879 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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EEG-vigilance regulation is associated with and predicts ketamine response in major depressive disorder.

Ip Cheng-Teng CT de Bardeci Mateo M Kronenberg Golo G Pinborg Lars Hageman LH Seifritz Erich E Brunovsky Martin M Olbrich Sebastian S

Translational psychiatry 20240126 1

Ketamine offers promising new therapeutic options for difficult-to-treat depression. The efficacy of treatment response, including ketamine, has been intricately linked to EEG measures of vigilance. This research investigated the interplay between intravenous ketamine and alterations in brain arousal, quantified through EEG vigilance assessments in two distinct cohorts of depressed patients (original dataset: n = 24; testing dataset: n = 24). Clinical response was defined as a decrease from base ...[more]

PMID: 38272875

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Project description:ObjectiveInsomnia and disrupted sleep are associated with increased risk of suicide. The N-methyl-d-aspartate antagonist ketamine has been associated with reduced suicidal thoughts, but the mechanism of action is unknown. This study sought to evaluate differences in nocturnal wakefulness in depressed individuals who did and did not have an antisuicidal response to ketamine.MethodsThirty-four participants with baseline suicidal ideation diagnosed with either DSM-IV major depressive disorder (n = 23) or bipolar depression (n = 11) between 2006 and 2013 completed nighttime electroencephalography (EEG) the night before and the night after a single ketamine infusion (0.5 mg/kg over 40 minutes). Suicidal ideation was assessed at baseline and the morning after ketamine infusion via several measures, including the Hamilton Depression Rating Scale suicide item, the suicide item of the Montgomery-Asberg Depression Rating Scale, and the first 5 items of the Scale for Suicide Ideation. A generalized linear mixed model evaluated differences in nocturnal wakefulness, as verified by EEG, between those who had an antisuicidal response to ketamine and those who did not, controlling for baseline nocturnal wakefulness. Results were also compared to the sleep of healthy controls (n = 22).ResultsAfter analyses adjusted for baseline sleep, participants with an antisuicidal response to ketamine showed significantly reduced nocturnal wakefulness the night after ketamine infusion compared to those without an antisuicidal response (F₁,₂₂ = 5.04, P = .04). Level of nocturnal wakefulness after antisuicidal response to ketamine did not differ significantly from nocturnal wakefulness in the control sample but did differ at a trend level (F₁,₄₀ = 3.15, P = .08).ConclusionsReductions in wakefulness following ketamine may point to a biological mechanism underlying the effect of ketamine on suicidal ideation.Trial registrationClinicalTrials.gov identifier: NCT00088699.

Project description:Selecting a course of treatment in psychiatry remains a trial-and-error process, and this long-standing clinical challenge has prompted an increased focus on predictive models of treatment response using machine learning techniques. Electroencephalography (EEG) represents a cost-effective and scalable potential measure to predict treatment response to major depressive disorder. We performed separate meta-analyses to determine the ability of models to distinguish between responders and non-responders using EEG across treatments, as well as a performed subgroup analysis of response to transcranial magnetic stimulation (rTMS), and antidepressants (Registration Number: CRD42021257477) in Major Depressive Disorder by searching PubMed, Scopus, and Web of Science for articles published between January 1960 and February 2022. We included 15 studies that predicted treatment responses among patients with major depressive disorder using machine-learning techniques. Within a random-effects model with a restricted maximum likelihood estimator comprising 758 patients, the pooled accuracy across studies was 83.93% (95% CI: 78.90-89.29), with an Area-Under-the-Curve (AUC) of 0.850 (95% CI: 0.747-0.890), and partial AUC of 0.779. The average sensitivity and specificity across models were 77.96% (95% CI: 60.05-88.70), and 84.60% (95% CI: 67.89-92.39), respectively. In a subgroup analysis, greater performance was observed in predicting response to rTMS (Pooled accuracy: 85.70% (95% CI: 77.45-94.83), Area-Under-the-Curve (AUC): 0.928, partial AUC: 0.844), relative to antidepressants (Pooled accuracy: 81.41% (95% CI: 77.45-94.83, AUC: 0.895, pAUC: 0.821). Furthermore, across all meta-analyses, the specificity (true negatives) of EEG models was greater than the sensitivity (true positives), suggesting that EEG models thus far better identify non-responders than responders to treatment in MDD. Studies varied widely in important features across models, although relevant features included absolute and relative power in frontal and temporal electrodes, measures of connectivity, and asymmetry across hemispheres. Predictive models of treatment response using EEG hold promise in major depressive disorder, although there is a need for prospective model validation in independent datasets, and a greater emphasis on replicating physiological markers. Crucially, standardization in cut-off values and clinical scales for defining clinical response and non-response will aid in the reproducibility of findings and the clinical utility of predictive models. Furthermore, several models thus far have used data from open-label trials with small sample sizes and evaluated performance in the absence of training and testing sets, which increases the risk of statistical overfitting. Large consortium studies are required to establish predictive signatures of treatment response using EEG, and better elucidate the replicability of specific markers. Additionally, it is speculated that greater performance was observed in rTMS models, since EEG is assessing neural networks more likely to be directly targeted by rTMS, comprising electrical activity primarily near the surface of the cortex. Prospectively, there is a need for models that examine the comparative effectiveness of multiple treatments across the same patients. However, this will require a thoughtful consideration towards cumulative treatment effects, and whether washout periods between treatments should be utilised. Regardless, longitudinal cross-over trials comparing multiple treatments across the same group of patients will be an important prerequisite step to both facilitate precision psychiatry and identify generalizable physiological predictors of response between and across treatment options.

Project description:BackgroundThis randomized, placebo-controlled, crossover trial examined the antidepressant efficacy of the muscarinic antagonist scopolamine in major depressive disorder subjects with more severe and refractory forms of major depressive disorder relative to previous reports.MethodsParticipants included 23 medication-free major depressive disorder subjects (12 F/11 M, 20-55 years) currently experiencing a major depressive episode. Subjects had scored ≥20 on the Montgomery-Asberg Depression Rating Scale. Following a single-blind, placebo lead-in, participants were randomized to receive 2 counterbalanced blocks of 3 i.v. infusions of scopolamine (4 μg/kg) and placebo in a double-blind manner. The primary and secondary outcomes were the Montgomery-Asberg Depression Rating Scale and the Hamilton Anxiety Rating Scale, respectively. Magnetoencephalography and plasma brain-derived neurotrophic factor concentrations were obtained prior to and after each treatment phase.ResultsAs assessed by both the Montgomery-Asberg Depression Rating Scale and Hamilton Anxiety Rating Scale, scopolamine had no significant antidepressant or anxiolytic effects relative to placebo. No significant drug vs placebo effects were seen in magnetoencephalography gamma power or brain-derived neurotrophic factor plasma concentrations, and brain-derived neurotrophic factor changes did not correlate with change in Montgomery-Asberg Depression Rating Scale score in response to scopolamine.ConclusionsThese results do not support the efficacy of scopolamine for more severe or refractory forms of depression. No pre- to post-infusion changes in plasma brain-derived neurotrophic factor were detected, and magnetoencephalography gamma power changed only in the placebo lead-in, suggesting that these biomarker measures were not affected by scopolamine in this cohort. While difficult to interpret given the lack of antidepressant response, the findings suggest that the neurobiological effects of ketamine and scopolamine are at least partly distinct.

Project description:Background: Major Depressive Disorder (MDD) is a moderately heritable disorder with a high lifetime prevalence. At present, laboratory blood tests to support MDD diagnosis are not available. Methods: We used a classifier approach on blood gene expression profiles of a unique set of non-medicated subjects (MDD patients and controls) to select genes of which expression is predictive for disease status. To reveal blood gene expression changes related to MDD disease, we applied a powerful ex vivo stimulus to the blood, i.e. incubation with lipopolysaccharide (LPS; 10 ng/ml blood). Results: Based on LPS-stimulated blood gene expression using whole-genome microarrays in 42 subjects (primary cohort; 21 MDD patients (mean age 42.3 years), 21 healthy controls (mean age 41.9 years)), we identified a set of genes (CAPRIN1, CLEC4A, KRT23, MLC1, PLSCR1, PROK2, ZBTB16) that serves as a molecular signature of MDD. These findings were validated for the primary cohort using an independent quantitative PCR method (P = 0.007). The difference between depressive patients and controls was confirmed (P = 0.019) in a replication cohort of 13 patients with MDD (mean age 42.8 years) and 14 controls (mean age 45.6 years). The MDD-signature score comprised of expression levels of 7 genes could discriminate depressive patients from controls with sensitivity of 76.9% and specificity of 71.8%. Conclusions: We show for the first time that molecular analysis of stimulated blood cells can be used as an endophenotype for MDD diagnosis, which is a milestone in establishing biomarkers for neuropsychiatric disorders with moderate heritability in general. Our results may provide a new entry point for following and predicting treatment outcome, as well as prediction of severity and recurrence of MDD. In total, 33 MDD patients and 34 healthy controls were analyzed using basal gene expression in whole blood, and gene expression from whole blood that was stimulated with LPS for 5-6 h, using microarrays. Patients were arbitrarily selected from all patients to serve as primary cohort (nMDD = 21 (MDD01-MDD21); nControls = 21 (Con01-Con21)), or replication cohort (nMDD = 12 (MDD22-MDD35); nControls = 13 (Con22-Con37)) using microarrays. This submission does not include Samples CON21_LPS or CON30_LPS.

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EEG-vigilance regulation is associated with and predicts ketamine response in major depressive disorder.

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EEG-vigilance regulation is associated with and predicts ketamine response in major depressive disorder.

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