Identifying Ketamine Responses in Treatment-Resistant Depression Using a Wearable Forehead EEG

Classifying Routine Clinical Electroencephalograms With Multivariate Iterative Filtering and Convolutional Neural Networks

Electroencephalogram (EEG) is widely used in basic and clinical neuroscience to explore neural states in various populations, and classifying these EEG recordings is a fundamental challenge. While machine learning shows promising results in classifying long multivariate time series, optimal prediction models and feature extraction methods for EEG classification remain elusive. Our study addressed the problem of EEG classification under the framework of brain age prediction, applying a deep learning model on EEG time series. We hypothesized that decomposing EEG signals into oscillatory modes would yield more accurate age predictions than using raw or canonically frequency-filtered EEG. Specifically, we employed multivariate intrinsic mode functions (MIMFs), an empirical mode decomposition (EMD) variant based on multivariate iterative filtering (MIF), with a convolutional neural network (CNN) model. Testing a large dataset of routine clinical EEG scans (n = 6540) from patients aged 1 to 103 years, we found that an ad-hoc CNN model without fine-tuning could reasonably predict brain age from EEGs. Crucially, MIMF decomposition significantly improved performance compared to canonical brain rhythms (from delta to lower gamma oscillations). Our approach achieved a mean absolute error (MAE) of 13.76 ± 0.33 and a correlation coefficient of 0.64 ± 0.01 in brain age prediction over the entire lifespan. Our findings indicate that CNN models applied to EEGs, preserving their original temporal structure, remains a promising framework for EEG classification, wherein the adaptive signal decompositions such as the MIF can enhance CNN models' performance in this task.

Comparative efficacy, tolerability and acceptability of intravenous racemic ketamine with intranasal esketamine, aripiprazole and lithium as augmentative treatments for treatment-resistant unipolar depression: A systematic review and network meta-analysis

BACKGROUND

Intravenous racemic ketamine is a promising treatment for treatment-resistant depression. However, its clinical utility compared with intranasal esketamine and the other well-studied conventional pharmacological interventions (i.e., aripiprazole and lithium) as augmentative treatments for treatment-resistant unipolar depression in adults remains unclear. Therefore, we aimed to compare the efficacy, tolerability and acceptability of intravenous racemic ketamine with intranasal esketamine, aripiprazole and lithium under such conditions.

METHODS

The Cochrane Library, PubMed, CINHAL and ClinicalTrials.gov databases were systematically searched from their inception to 10 May 2023. Randomised controlled trials evaluating these drugs were included. A random-effects network meta-analysis was also performed.

RESULTS

In the primary analysis, all four drugs were significantly more effective than placebo. In addition, intravenous racemic ketamine was significantly more effective and acceptable than intranasal esketamine and aripiprazole. Intravenous racemic ketamine was not significantly different from placebo in tolerability, whereas intranasal esketamine and aripiprazole were significantly less tolerable than placebo. Lithium did not differ significantly from intravenous racemic ketamine in efficacy, tolerability and acceptability.

LIMITATIONS

The sample size of patients treated with intravenous racemic ketamine was small.

CONCLUSIONS

Intravenous racemic ketamine may be a better augmentative treatment for treatment-resistant unipolar depression than intranasal esketamine and aripiprazole. Whether intravenous racemic ketamine or lithium is superior is unclear currently. A larger head-to-head trial of intravenous racemic ketamine versus conventional augmentative treatments for treatment-resistant unipolar depression is needed.

Non-linear processing and reinforcement learning to predict rTMS treatment response in depression

BACKGROUND

Forecasting the efficacy of repetitive transcranial magnetic stimulation (rTMS) therapy can lead to substantial time and cost savings by preventing futile treatments. To achieve this objective, we've formulated a machine learning approach aimed at categorizing patients with major depressive disorder (MDD) into two groups: individuals who respond (R) positively to rTMS treatment and those who do not respond (NR).

METHODS

Preceding the commencement of treatment, we obtained resting-state EEG data from 106 patients diagnosed with MDD, employing 32 electrodes for data collection. These patients then underwent a 7-week course of rTMS therapy, and 54 of them exhibited positive responses to the treatment. Employing Independent Component Analysis (ICA) on the EEG data, we successfully pinpointed relevant brain sources that could potentially serve as markers of neural activity within the dorsolateral prefrontal cortex (DLPFC). These identified sources were further scrutinized to estimate the sources of activity within the sensor domain. Then, we integrated supplementary physiological data and implemented specific criteria to yield more realistic estimations when compared to conventional EEG analysis. In the end, we selected components corresponding to the DLPFC region within the sensor domain. Features were derived from the time-series data of these relevant independent components. To identify the most significant features, we used Reinforcement Learning (RL). In categorizing patients into two groups - R and NR to rTMS treatment - we utilized three distinct classification algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). We assessed the performance of these classifiers through a ten-fold cross-validation method. Additionally, we conducted a statistical test to evaluate the discriminative capacity of these features between responders and non-responders, opening the door for further exploration in this field.

RESULTS

We identified EEG features that can anticipate the response to rTMS treatment. The most robust discriminators included EEG beta power, the sum of bispectrum diagonal elements in the delta and beta frequency bands. When these features were combined into a single vector, the classification of responders and non-responders achieved impressive performance, with an accuracy of 95.28 %, specificity at 94.23 %, sensitivity reaching 96.29 %, and precision standing at 94.54 %, all achieved using SVM.

CONCLUSIONS

The results of this study suggest that the proposed approach, utilizing power, non-linear, and bispectral features extracted from relevant independent component time-series, has the capability to forecast the treatment outcome of rTMS for MDD patients based solely on a single pre-treatment EEG recording session. The achieved findings demonstrate the superior performance of our method compared to previous techniques.

Recent Progress in Wearable Brain-Computer Interface (BCI) Devices Based on Electroencephalogram (EEG) for Medical Applications: A Review

Importance: Brain-computer interface (BCI) decodes and converts brain signals into machine instructions to interoperate with the external world. However, limited by the implantation risks of invasive BCIs and the operational complexity of conventional noninvasive BCIs, applications of BCIs are mainly used in laboratory or clinical environments, which are not conducive to the daily use of BCI devices. With the increasing demand for intelligent medical care, the development of wearable BCI systems is necessary. Highlights: Based on the scalp-electroencephalogram (EEG), forehead-EEG, and ear-EEG, the state-of-the-art wearable BCI devices for disease management and patient assistance are reviewed. This paper focuses on the EEG acquisition equipment of the novel wearable BCI devices and summarizes the development direction of wearable EEG-based BCI devices. Conclusions: BCI devices play an essential role in the medical field. This review briefly summarizes novel wearable EEG-based BCIs applied in the medical field and the latest progress in related technologies, emphasizing its potential to help doctors, patients, and caregivers better understand and utilize BCI devices.

Higher Order Fractal Belief Rényi Divergence With Its Applications in Pattern Classification

Information can be quantified and expressed by uncertainty, and improving the decision level of uncertain information is vital in modeling and processing uncertain information. Dempster-Shafer evidence theory can model and process uncertain information effectively. However, the Dempster combination rule may provide counter-intuitive results when dealing with highly conflicting information, leading to a decline in decision level. Thus, measuring conflict is significant in the improvement of decision level. Motivated by this issue, this paper proposes a novel method to measure the discrepancy between bodies of evidence. First, the model of dynamic fractal probability transformation is proposed to effectively obtain more information about the non-specificity of basic belief assignments (BBAs). Then, we propose the higher-order fractal belief Rényi divergence (HOFBReD). HOFBReD can effectively measure the discrepancy between BBAs. Moreover, it is the first belief Rényi divergence that can measure the discrepancy between BBAs with dynamic fractal probability transformation. HoFBReD has several properties in terms of probability transformation as well as measurement. When the dynamic fractal probability transformation ends, HoFBReD is equivalent to measuring the Rényi divergence between the pignistic probability transformations of BBAs. When the BBAs degenerate to the probability distributions, HoFBReD will also degenerate to or be related to several well-known divergences. In addition, based on HoFBReD, a novel multisource information fusion algorithm is proposed. A pattern classification experiment with real-world datasets is presented to compare the proposed algorithm with other methods. The experiment results indicate that the proposed algorithm has a higher average pattern recognition accuracy with all datasets than other methods. The proposed discrepancy measurement method and multisource information algorithm contribute to the improvement of decision level.

The Three-Lead EEG Sensor: Introducing an EEG-Assisted Depression Diagnosis System Based on Ant Lion Optimization

For depression diagnosis, traditional methods such as interviews and clinical scales have been widely leveraged in the past few decades, but they are subjective, time-consuming, and labor-consuming. With the development of affective computing and Artificial Intelligence (AI) technologies, Electroencephalogram (EEG)-based depression detection methods have emerged. However, previous research has virtually neglected practical application scenarios, as most studies have focused on analyzing and modeling EEG data. Furthermore, EEG data is typically obtained from specialized devices that are large, complex to operate, and poorly ubiquitous. To address these challenges, a wearable three-lead EEG sensor with flexible electrodes was developed to obtain prefrontal-lobe EEG data. Experimental measurements show that the EEG sensor achieves promising performance (background noise of no more than 0.91 μVpp, Signal-to-Noise Ratio (SNR) of 26--48 dB, and electrode-skin contact impedance of less than 1 K Ω). In addition, EEG data from 70 depressed patients and 108 healthy controls were collected using the EEG sensor, and the linear and nonlinear features were extracted. The features were then weighted and selected using the Ant Lion Optimization (ALO) algorithm to improve classification performance. The experimental results show that the k-NN classifier achieves a classification accuracy of 90.70%, specificity of 96.53%, and sensitivity of 81.79%, indicating the promising potential of the three-lead EEG sensor combined with the ALO algorithm and the k-NN classifier for EEG-assisted depression diagnosis.

Brain-based correlates of antidepressant response to ketamine: a comprehensive systematic review of neuroimaging studies

Ketamine is an effective antidepressant, but there is substantial variability in patient response and the precise mechanism of action is unclear. Neuroimaging can provide predictive and mechanistic insights, but findings are limited by small sample sizes. This systematic review covers neuroimaging studies investigating baseline (pre-treatment) and longitudinal (post-treatment) biomarkers of responses to ketamine. All modalities were included. We performed searches of five electronic databases (from inception to April 26, 2022). 69 studies were included (with 1751 participants). There was substantial methodological heterogeneity and no well replicated biomarker. However, we found convergence across some significant results, particularly in longitudinal biomarkers. Response to ketamine was associated with post-treatment increases in gamma power in frontoparietal regions in electrophysiological studies, post-treatment increases in functional connectivity within the prefrontal cortex, and post-treatment increases in the functional activation of the striatum. Although a well replicated neuroimaging biomarker of ketamine response was not identified, there are biomarkers that warrant further investigation.

Ketamine for the treatment of major depression: a systematic review and meta-analysis

Background

Intranasal esketamine has received regulatory approvals for the treatment of depression. Recently a large trial of repeated dose racemic ketamine also demonstrated efficacy in severe depression. However, uncertainties remain regarding comparative efficacy, dosage, and the time course of response.

Methods

In this systematic review and meta-analysis, we searched Embase, Medline, Pubmed, PsycINFO, and CENTRAL up to April 13, 2023, for randomised controlled trials (RCTs) investigating ketamine for depression. Two investigators independently assessed study eligibility and risk of bias and extracted the data on depression severity scores, response and remission rates, and all-cause dropouts. Multivariable mixed-effects meta-regressions incorporated drug formulation (racemic (Rac) or esketamine (Esket)) and dose (Low or High) as covariates. Treatment effects were assessed: immediately following the first dose, during further repeated dosing, and follow-up after the final dose of a treatment course. This study is registered with PROSPERO (CRD42021221157).

Findings

The systematic review identified 687 articles, of which 49 RCTs were eligible for analysis, comprising 3299 participants. Standardised mean differences (95% confidence intervals) immediately following the first/single treatment were moderate-high for all conditions (Rac-High: -0.73, -0.91 to -0.56; Esket-High: -0.48, -0.75 to -0.20; Rac-Low: -0.33, -0.54 to -0.12; Esket-Low: -0.55, -0.87 to -0.24). Ongoing effects during repeated dosing were significantly greater than the control for Rac-High (-0.61; -1.02 to -0.20) and Rac-Low (-0.55, -1.09 to -0.00), but not Esket-Low (-0.15, -0.49 to 0.19) or Esket-High (-0.22, -0.54 to 0.10). At follow-up effects remained significant for racemic ketamine (-0.65; -1.23 to -0.07) but not esketamine (-0.33; -0.96 to 0.31). All-cause dropout was similar between experiment and control conditions for both formulations combined (Odds Ratio = 1.18, 0.85-1.64). Overall heterogeneity varied from 5.7% to 87.6.

Interpretation

Our findings suggested that effect sizes for depression severity, as well as response and remission rates, were numerically greater for racemic ketamine than esketamine. Higher doses were more effective than low doses. Differences were evident in initial effects, ongoing treatment, and lasting effects after the final dose.

Funding

None.

Diversity and Suitability of the State-of-the-Art Wearable and Wireless EEG Systems Review

Wireless electroencephalography (EEG) systems have been attracting increasing attention in recent times. Both the number of articles discussing wireless EEG and their proportion relative to general EEG publications have increased over years. These trends indicate that wireless EEG systems could be more accessible to researchers and the research community has recognized the potential of wireless EEG systems. To explore the development and diverse applications of wireless EEG systems, this review highlights the trends in wearable and wireless EEG systems over the past decade and compares the specifications and research applications of the major wireless systems marketed by 16 companies. For each product, five parameters (number of channels, sampling rate, cost, battery life, and resolution) were assessed for comparison. Currently, these wearable and portable wireless EEG systems have three main application areas: consumer, clinical, and research. To address this multitude of options, the article also discussed the thought process to find a suitable device that meets personalization and use cases specificities. These investigations suggest that low-price and convenience are key factors for consumer applications, wireless EEG systems with FDA or CE-certification may be more suitable for clinical settings, and devices that provide raw EEG data with high-density channels are important for laboratory research. This article presents an overview of the current state of the wireless EEG systems specifications and possible applications and serves as a guide point as it is expected that more influential and novel research will cyclically promote the development of such EEG systems.

Remote collection of electrophysiological data with brain wearables: opportunities and challenges

Collection of electroencephalographic (EEG) data provides an opportunity to non-invasively study human brain plasticity, learning and the evolution of various neuropsychiatric disorders. Traditionally, due to sophisticated hardware, EEG studies have been largely limited to research centers which restrict both testing contexts and repeated longitudinal measures. The emergence of low-cost "wearable" EEG devices now provides the prospect of frequent and remote monitoring of the human brain for a variety of physiological and pathological brain states. In this manuscript, we survey evidence that EEG wearables provide high-quality data and review various software used for remote data collection. We then discuss the growing body of evidence supporting the feasibility of remote and longitudinal EEG data collection using wearables including a discussion of potential biomedical applications of these protocols. Lastly, we discuss some additional challenges needed for EEG wearable research to gain further widespread adoption.

Electrophysiological phenotypes of suicidality predict prolonged response to oral ketamine treatment

Oral ketamine has shown to be a rapid-acting antidepressant and a potential treatment option for suicidality, however, repeated doses are often required. Objective markers of prolonged treatment response are needed to help individuals and clinicians make informed treatment decisions. This secondary analysis sought to identify objective electrophysiological predictors of both prolonged response and dose sensitivity to low-dose oral ketamine in people with chronic suicidality. Individuals with a Beck Scale for Suicide Ideation total score (BSS) ≥ 6 (N = 29) completed a six-week ketamine treatment, pre-treatment electroencephalography and follow-up assessment of suicidality (four weeks from the final ketamine dose). Prolonged response was observed in 52% of participants (follow-up BSS reduced by 50% or ≤6); nearly half were prolonged non-responders. There was decisive evidence for a predictive Bayesian linear regression model with follow-up BSS score as the response variable and pre-treatment auditory evoked power bands as predictors (theta, alpha and beta frequencies, BF₁₀ = 17,948, R² = 0.70). A Bayesian one-way ANOVA indicated strong evidence for a model of positive association between auditory evoked power and ketamine dose sensitivity (theta-alpha BF₊₀ = 108, effect size δ = 1.3, 95% CI 0.5-2.1; high-beta BF₊₀ = 7.4, δ = 0.8, 95% CI 0.1-1.6). Given auditory evoked power may index serotonin neurotransmission, these results suggest that a prolonged response to ketamine may, in part, be mediated by pre-treatment serotonergic functioning. In addition, the observed beta power differences may arise from GABAergic functioning. These suicidality phenotypes, identifiable by pre-treatment electrophysiology, may aid diagnosis, treatment selection and prediction of prolonged treatment outcome.

Soft Electronics for Health Monitoring Assisted by Machine Learning

Due to the development of the novel materials, the past two decades have witnessed the rapid advances of soft electronics. The soft electronics have huge potential in the physical sign monitoring and health care. One of the important advantages of soft electronics is forming good interface with skin, which can increase the user scale and improve the signal quality. Therefore, it is easy to build the specific dataset, which is important to improve the performance of machine learning algorithm. At the same time, with the assistance of machine learning algorithm, the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis. The soft electronics and machining learning algorithms complement each other very well. It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future. Therefore, in this review, we will give a careful introduction about the new soft material, physiological signal detected by soft devices, and the soft devices assisted by machine learning algorithm. Some soft materials will be discussed such as two-dimensional material, carbon nanotube, nanowire, nanomesh, and hydrogel. Then, soft sensors will be discussed according to the physiological signal types (pulse, respiration, human motion, intraocular pressure, phonation, etc.). After that, the soft electronics assisted by various algorithms will be reviewed, including some classical algorithms and powerful neural network algorithms. Especially, the soft device assisted by neural network will be introduced carefully. Finally, the outlook, challenge, and conclusion of soft system powered by machine learning algorithm will be discussed.

Machine learning approaches and non-linear processing of extracted components in frontal region to predict rTMS treatment response in major depressive disorder

Predicting the therapeutic result of repetitive transcranial magnetic stimulation (rTMS) treatment could save time and costs as ineffective treatment can be avoided. To this end, we presented a machine-learning-based strategy for classifying patients with major depression disorder (MDD) into responders (R) and nonresponders (NR) to rTMS treatment. Resting state EEG data were recorded using 32 electrodes from 88 MDD patients before treatment. Then, patients underwent 7 weeks of rTMS, and 46 of them responded to treatment. By applying Independent Component Analysis (ICA) on EEG, we identified the relevant brain sources as possible indicators of neural activity in the dorsolateral prefrontal cortex (DLPFC). This was served through estimating the generators of activity in the sensor domain. Subsequently, we added physiological information and placed certain terms and conditions to offer a far more realistic estimation than the classic EEG. Ultimately, those components mapped in accordance with the region of the DLPFC in the sensor domain were chosen. Features extracted from the relevant ICs time series included permutation entropy (PE), fractal dimension (FD), Lempel-Ziv Complexity (LZC), power spectral density, correlation dimension (CD), features based on bispectrum, frontal and prefrontal cordance, and a combination of them. The most relevant features were selected by a Genetic Algorithm (GA). For classifying two groups of R and NR, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) were applied to predict rTMS treatment response. To evaluate the performance of classifiers, a 10-fold cross-validation method was employed. A statistical test was used to assess the capability of features in differentiating R and NR for further research. EEG characteristics that can predict rTMS treatment response were discovered. The strongest discriminative indicators were EEG beta power, the sum of bispectrum diagonal elements in delta and beta bands, and CD. The Combined feature vector classified R and NR with a high performance of 94.31% accuracy, 92.85% specificity, 95.65% sensitivity, and 92.85% precision using SVM. This result indicates that our proposed method with power and nonlinear and bispectral features from relevant ICs time-series can predict the treatment outcome of rTMS for MDD patients only by one session pretreatment EEG recording. The obtained results show that the proposed method outperforms previous methods.

Self-Management of Subclinical Common Mental Health Disorders (Anxiety, Depression and Sleep Disorders) Using Wearable Devices

RATIONALE

Common mental health disorders (CMD) (anxiety, depression, and sleep disorders) are among the leading causes of disease burden globally. The economic burden associated with such disorders is estimated at $2.4 trillion as of 2010 and is expected to reach $16 trillion by 2030. The UK has observed a 21-fold increase in the economic burden associated with CMD over the past decade. The recent COVID-19 pandemic was a catalyst for adopting technologies for mental health support and services, thereby increasing the reception of personal health data and wearables. Wearables hold considerable promise to empower users concerning the management of subclinical common mental health disorders. However, there are significant challenges to adopting wearables as a tool for the self-management of the symptoms of common mental health disorders.

AIMS

This review aims to evaluate the potential utility of wearables for the self-management of sub-clinical anxiety and depressive mental health disorders. Furthermore, we seek to understand the potential of wearables to reduce the burden on the healthcare system.

METHODOLOGY

a systematic review of research papers was conducted, focusing on wearable devices for the self-management of CMD released between 2018-2022, focusing primarily on mental health management using technology.

RESULTS

We screened 445 papers and analysed the reports from 12 wearable devices concerning their device type, year, biometrics used, and machine learning algorithm deployed. Electrodermal activity (EDA/GSR/SC/Skin Temperature), physical activity, and heart rate (HR) are the most common biometrics with nine, six and six reference counts, respectively. Additionally, while smartwatches have greater penetration and integration within the marketplace, fitness trackers have the most significant public value benefit of £513.9 M, likely due to greater retention.

Depression signal correlation identification from different EEG channels based on CNN feature extraction

Depression is a mental illness and can even lead to suicide if not be diagnosed and treated. Electroencephalograph (EEG) is used to diagnose depression and it is more complexity to extract the features from all the multimodal channel information . In order to simplify the diagnose process and detect clinical depression, the EEG channels with strong depression information should be identified firstly. Therefore, a depression signal correlation identification method based on convolutional neural network (CNN) is proposed. In the method, the labeled multi-channel EEG is used as data. The EEG signals of each channel are divided into neural network training data set and these data is trained by AlexNet network. Then the correlation classification of each channel for depression is identified based on the trained sample. Accuracy and loss functions are used to evaluate classification index.Conversely, the correlation is lower. An experiments is conducted and the results show that the correlation is not consistent. A few of channels are strongly correlated with depression, such as 13, 17, 28, 40, 46, 66 and 69. These EEG channels are selected to diagnose depression.

An adaptive acoustoelectric signal decoding algorithm based on Fourier fitting for brain function imaging

Acousticelectric brain imaging (ABI), which is based on the acoustoelectric (AE) effect, is a potential brain function imaging method for mapping brain electrical activity with high temporal and spatial resolution. To further enhance the quality of the decoded signal and the resolution of the ABI, the decoding accuracy of the AE signal is essential. An adaptive decoding algorithm based on Fourier fitting (aDAF) is suggested to increase the AE signal decoding precision. The envelope of the AE signal is first split into a number of harmonics by Fourier fitting in the suggested aDAF. The least square method is then utilized to adaptively select the greatest harmonic component. Several phantom experiments are implemented to assess the performance of the aDAF, including 1-source with various frequencies, multiple-source with various frequencies and amplitudes, and multiple-source with various distributions. Imaging resolution and decoded signal quality are quantitatively evaluated. According to the results of the decoding experiments, the decoded signal amplitude accuracy has risen by 11.39% when compared to the decoding algorithm with envelope (DAE). The correlation coefficient between the source signal and the decoded timing signal of aDAF is, on average, 34.76% better than it was for DAE. Finally, the results of the imaging experiment show that aDAF has superior imaging quality than DAE, with signal-to noise ratio (SNR) improved by 23.32% and spatial resolution increased by 50%. According to the experiments, the proposed aDAF increased AE signal decoding accuracy, which is vital for future research and applications related to ABI.

The association between stage of treatment-resistant depression and clinical utility of ketamine/esketamine: A systematic review

OBJECTIVE

Ketamine has demonstrated rapid and significant antidepressant effects in patients with treatment resistant depression (TRD). Herein, we conducted a systematic review to determine ketamine's efficacy as a function of the stage of treatment resistance (e.g., number of failed treatments) among individuals with TRD.

METHODS

A systematic search of PubMed and Scopus from inception to August 2021 was conducted. Where applicable, the studies were categorized into low and high stages of resistance, where low category included studies where the mean number of failed antidepressants was <3 or had a higher proportion of subjects with ≤2 antidepressant trials. Reported indicators of treatment resistance and efficacy were extracted from randomized-controlled trials (RCTs) assessing ketamine or esketamine for TRD.

RESULTS

In total, 18 RCTs were included in the current review. There was variability across reported indicators of disease severity, definition of treatment resistance, as well as treatment protocols, preventing clear direct and indirect comparison of relative efficacy of ketamine at different stages of treatment resistance. Ketamine was effective in reducing depressive symptoms in RCTs at both lower and higher stages of treatment resistance; however, the effect size and duration of effects was greater in RCTs of lower stage of treatment resistance.

CONCLUSIONS

Our findings suggested that ketamine has antidepressant efficacy across all identified stages of treatment resistance, however with increasing failed treatment trials, treatment might be less efficacious. At this time, the comparative efficacy as a function of resistance stage remains to be well-established. Evaluation of participant level data is required to more clearly determine the association between level of treatment resistance and likelihood of response.

Magnetoencephalography biomarkers of suicide attempt history and antidepressant response to ketamine in treatment-resistant major depression

BACKGROUND

This study examined magnetoencephalographic (MEG) correlates of suicidal ideation (SI) and suicide attempt history in patients with treatment-resistant major depression (TRD) at baseline and following subanesthetic-dose ketamine infusion.

METHODS

Twenty-nine drug-free TRD patients (12 suicide attempters/17 non-attempters) participated in a crossover randomized trial of ketamine. MEG data were collected during an attentional dot probe task with emotional face stimuli at baseline and several hours post-ketamine infusion. Synthetic aperture magnetometry was used to project source power in the theta, alpha, beta, and gamma frequencies for angry-neutral, happy-neutral, and neutral-neutral face pairings during a one-second peristimulus period. Mixed models were used to test for clinical, behavioral, and electrophysiological effects of group, emotion, session, and SI score.

RESULTS

Ketamine significantly reduced SI and depression across the sample. Post-ketamine, attempters had improved accuracy and non-attempters had reduced accuracy on the task. SI was positively associated with gamma power in regions of the frontal and parietal cortices across groups. In an extended amygdala-hippocampal region, attempters differed significantly in their emotional reactivity to angry versus happy faces as indexed by theta power differences, irrespective of drug. Ketamine significantly reduced the association between alpha power and SI for angry compared with happy faces in a fronto-insular/anterior cingulate region important for regulating sensory attentiveness.

LIMITATIONS

Limitations include a small sample size of attempters.

CONCLUSIONS

The findings highlight key differences in band-limited power between attempters and non-attempters and reinforce previous findings that ketamine has distinct response properties in patients with a suicide history.

Predicting treatment response using EEG in major depressive disorder: A machine-learning meta-analysis

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.

Resting and TMS-EEG markers of treatment response in major depressive disorder: A systematic review

Electroencephalography (EEG) is a non-invasive method to identify markers of treatment response in major depressive disorder (MDD). In this review, existing literature was assessed to determine how EEG markers change with different modalities of MDD treatments, and to synthesize the breadth of EEG markers used in conjunction with MDD treatments. PubMed and EMBASE were searched from 2000 to 2021 for studies reporting resting EEG (rEEG) and transcranial magnetic stimulation combined with EEG (TMS-EEG) measures in patients undergoing MDD treatments. The search yielded 966 articles, 204 underwent full-text screening, and 51 studies were included for a narrative synthesis of findings along with confidence in the evidence. In rEEG studies, non-linear quantitative algorithms such as theta cordance and theta current density show higher predictive value than traditional linear metrics. Although less abundant, TMS-EEG measures show promise for predictive markers of brain stimulation treatment response. Future focus on TMS-EEG measures may prove fruitful, given its ability to target cortical regions of interest related to MDD.

Uso da cetamina na depressão resistente ao tratamento: uma revisão sistemática

RESUMO Objetivo A cetamina apresenta-se como uma alternativa promissora contra a depressão resistente ao tratamento (DRT), no entanto o conhecimento de sua aplicação como antidepressivo ainda é restrito. Diante disso, objetivou-se investigar sua eficácia em pacientes com DRT. Métodos A busca da literatura foi efetuada na base de dados MedLine. Os critérios de inclusão foram: estudos clínicos controlados e randomizados dos últimos cinco anos e em inglês. Excluímos os artigos que não responderam à pergunta PICO e aqueles com tamanho de amostra e metodologia de estudo destoantes, quando comparados aos ensaios que basearam esta revisão. Resultados Considerando uma amostra final de seis artigos, observou-se uma melhor resposta à cetamina (principalmente em ganho de humor), quando comparada ao tratamento convencional contra a DRT. Já com sua primeira infusão, na dose de 0,5 mg/kg, foi possível perceber seus efeitos antidepressivos. A manutenção desses efeitos parece ser obtida com a administração de 0,5 mg/kg do medicamento, três vezes por semana. Por outro lado, a redução de tal dosagem pode diminuir ou anular os efeitos. Conclusões O uso da cetamina apresentou resultados efetivos na melhora do quadro de DRT, com efeitos adversos de pequena gravidade e de fácil controle. Entretanto, outros estudos, com amostras maiores e métodos diferentes, são necessários, para uma conclusão de maior consistência.

Design of hydrogel-based wearable EEG electrodes for medical applications

The electroencephalogram (EEG) is considered to be a promising method for studying brain disorders. Because of its non-invasive nature, subjects take a lower risk compared to some other invasive methods, while the systems record the brain signal. With the technological advancement of neural and material engineering, we are in the process of achieving continuous monitoring of neural activity through wearable EEG. In this article, we first give a brief introduction to EEG bands, circuits, wired/wireless EEG systems, and analysis algorithms. Then, we review the most recent advances in the interfaces used for EEG recordings, focusing on hydrogel-based EEG electrodes. Specifically, the advances for important figures of merit for EEG electrodes are reviewed. Finally, we summarize the potential medical application of wearable EEG systems.

Efficacy and safety of racemic ketamine and esketamine for depression: a systematic review and meta-analysis

BACKGROUND

Racemic ketamine and esketamine have demonstrated rapid antidepressant effects. We aimed to review the efficacy and safety of racemic and esketamine for depression.

RESEARCH DESIGN AND METHODS

We conducted a PRISMA-guided review for relevant randomized controlled trials of racemic or esketamine for unipolar or bipolar major depression from database inception through 2021. We conducted random-effects meta-analyses using pooled rate ratios (RRs) and Cohen's standardized mean differences (d) with their 95% confidence intervals (CI).

RESULTS

We found 36 studies (2903 participants, 57% female, 45.1 +/- 7.0 years). Nine trials used esketamine, while the rest used racemic ketamine. The overall study quality was high. Treatment with any form of ketamine was associated with improved response (RR=2.14; 95% CI, 1.72-2.66; I2=65%), remission (RR=1.64; 95% CI, 1.33-2.02; I2=39%), and depression severity (d=-0.63; 95% CI, -0.80 to -0.45; I2=78%) against placebo. Overall, there was no association between treatment with any form of ketamine and retention in treatment (RR=1.00; 95% CI, 0.99-1.01; I2<1%), dropouts due to adverse events (RR=1.56; 95% CI, 1.00-2.45; I2<1%), or the overall number of adverse events reported per participant (OR=2.14; 95% CI, 0.82-5.60; I2=62%) against placebo.

CONCLUSIONS

Ketamine and esketamine are effective, safe, and acceptable treatments for individuals living with depression.

Electrophysiological correlates and predictors of the antidepressant response to repeated ketamine infusions in treatment-resistant depression

BACKGROUND

Sub-anesthetic ketamine doses rapidly reduce depressive symptoms, although additional investigations of the underlying neural mechanisms and the prediction of response outcomes are needed. Electroencephalographic (EEG)-derived measures have shown promise in predicting antidepressant response to a variety of treatments, and are sensitive to ketamine administration. This study examined their utility in characterizing changes in depressive symptoms following single and repeated ketamine infusions.

METHODS

Recordings were obtained from patients with treatment-resistant major depressive disorder (MDD) (N = 24) enrolled in a multi-phase clinical ketamine trial. During the randomized, double-blind, crossover phase (Phase 1), patients received intravenous ketamine (0.5 mg/kg) and midazolam (30 μg/kg), at least 1 week apart. For each medication, three resting, eyes-closed recordings were obtained per session (pre-infusion, immediately post-infusion, 2 h post-infusion), and changes in power (delta, theta1/2/total, alpha1/2/total, beta, gamma), alpha asymmetry, theta cordance, and theta source-localized anterior cingulate cortex activity were quantified. The relationships between ketamine-induced changes with early (Phase 1) and sustained (Phases 2,3: open-label repeated infusions) decreases in depressive symptoms (Montgomery-Åsberg Depression Rating Score, MADRS) and suicidal ideation (MADRS item 10) were examined.

RESULTS

Both medications decreased alpha and theta immediately post-infusion, however, only midazolam increased delta (post-infusion), and only ketamine increased gamma (immediately post- and 2 h post-infusion). Regional- and frequency-specific ketamine-induced EEG changes were related to and predictive of decreases in depressive symptoms (theta, gamma) and suicidal ideation (alpha). Early and sustained treatment responders differed at baseline in surface-level and source-localized theta.

CONCLUSIONS

Ketamine exerts frequency-specific changes on EEG-derived measures, which are related to depressive symptom decreases in treatment-resistant MDD and provide information regarding early and sustained individual response to ketamine.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov: Action of Ketamine in Treatment-Resistant Depression, NCT01945047.

EEG Beta-Band Spectral Entropy Can Predict the Effect of Drug Treatment on Pain in Patients With Herpes Zoster

BACKGROUND

Medication is the main approach for early treatment of herpes zoster, but it could be ineffective in some patients. It is highly desired to predict the medication responses to control the degree of pain for herpes zoster patients. The present study is aimed to elucidate the relationship between medication outcome and neural activity using EEG and to establish a machine learning model for early prediction of the medication responses from EEG.

METHODS

The authors acquired and analyzed eye-closed resting-state EEG data 1 to 2 days after medication from 70 herpes zoster patients with different drug treatment outcomes (measured 5-6 days after medication): 45 medication-sensitive pain patients and 25 medication-resistant pain patients. EEG power spectral entropy of each frequency band was compared at each channel between medication-sensitive pain and medication-resistant pain patients, and those features showing significant difference between two groups were used to predict medication outcome with different machine learning methods.

RESULTS

Medication-sensitive pain patients showed significantly weaker beta-band power spectral entropy in the central-parietal regions than medication-resistant pain patients. Based on these EEG power spectral entropy features and a k-nearest neighbors classifier, the medication outcome can be predicted with 80% ± 11.7% accuracy, 82.5% ± 14.7% sensitivity, 77.7% ± 27.3% specificity, and an area under the receiver operating characteristic curve of 0.85.

CONCLUSIONS

EEG beta-band power spectral entropy in the central-parietal region is predictive of the effectiveness of drug treatment on herpes zoster patients, and it could potentially be used for early pain management and therapeutic prognosis.

Electroencephalography Correlates of Well-Being Using a Low-Cost Wearable System

Electroencephalography (EEG) alpha asymmetry is thought to reflect crucial brain processes underlying executive control, motivation, and affect. It has been widely used in psychopathology and, more recently, in novel neuromodulation studies. However, inconsistencies remain in the field due to the lack of consensus in methodological approaches employed and the recurrent use of small samples. Wearable technologies ease the collection of large and diversified EEG datasets that better reflect the general population, allow longitudinal monitoring of individuals, and facilitate real-world experience sampling. We tested the feasibility of using a low-cost wearable headset to collect a relatively large EEG database (N = 230, 22-80 years old, 64.3% female), and an open-source automatic method to preprocess it. We then examined associations between well-being levels and the alpha center of gravity (CoG) as well as trait EEG asymmetries, in the frontal and temporoparietal (TP) areas. Robust linear regression models did not reveal an association between well-being and alpha (8-13 Hz) asymmetry in the frontal regions, nor with the CoG. However, well-being was associated with alpha asymmetry in the TP areas (i.e., corresponding to relatively less left than right TP cortical activity as well-being levels increased). This effect was driven by oscillatory activity in lower alpha frequencies (8-10.5 Hz), reinforcing the importance of dissociating sub-components of the alpha band when investigating alpha asymmetries. Age was correlated with both well-being and alpha asymmetry scores, but gender was not. Finally, EEG asymmetries in the other frequency bands were not associated with well-being, supporting the specific role of alpha asymmetries with the brain mechanisms underlying well-being levels. Interpretations, limitations, and recommendations for future studies are discussed. This paper presents novel methodological, experimental, and theoretical findings that help advance human neurophysiological monitoring techniques using wearable neurotechnologies and increase the feasibility of their implementation into real-world applications.

Ketamine in depression and electroconvulsive therapy

PURPOSE OF REVIEW

The antidepressant effect of subanesthetic doses of ketamine was recognized 20 years ago. This review briefly summarizes the current understanding of the antidepressant mechanisms and the available clinical research on the use of racemic ketamine and enantiomer esketamine for depression.

RECENT FINDINGS

The antidepressant effect of subanesthetic doses of ketamine is currently considered to be predominantly mediated by improved neuroplasticity in cortico-limbic areas in the brain. Single dose of 0.5 mg/kg of ketamine infused intravenously over 40 min, or single intranasal dose of esketamine cause rapid antidepressant and antisuicidal effects within hours of administration, and the antidepressant effect may last up to a week. Repeated administration of nasal spray esketamine is considered to prevent relapse of depression. Longitudinal studies are currently insufficient. When used in various doses for anesthetic induction for electroconvulsive therapy, ketamine improves seizure quality and may possibly diminish posttherapy cognitive impairment.

SUMMARY

A rapid onset antidepressive effect of ketamine and esketamine has been proven conclusively. The results of extensive basic science research of the mechanism of action of low-dose ketamine doses has led to an alternative hypothesis of the pathophysiology of depression and the development of a novel neurotrophic concept of depression. Further longitudinal studies are warranted to determine the safety and efficacy of repeated administration of ketamine and its analogs to prevent relapse and recurrence of depression.

EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research.

Blinding and expectancy confounds in psychedelic randomized controlled trials

Introduction: There is increasing interest in the potential for psychedelic drugs such as psilocybin, LSD and ketamine to treat several mental health disorders, with a growing number of randomized controlled trials (RCTs) being conducted to investigate the therapeutic effectiveness of psychedelics.Areas covered: We review previous literature on expectancy effects and blinding in the context of psychedelic RCTs - literature which strongly suggest that psychedelic RCTs might be confounded by de-blinding and expectancy. We conduct systematic reviews of psychedelic RCTs using Medline, PsychInfo and EMBASE (Jan 1990 - Nov 2020) and show that currently reported psychedelic RCTs have generally not reported pre-trial expectancy, nor the success of blinding procedures.Expert opinion: While psychedelic RCTs have generally shown promising results, with large effect sizes reported, we argue that treatment effect sizes in psychedelic RCTs are likely over-estimated due to de-blinding of participants and high levels of response expectancy. We suggest that psychedelic RCTs should routinely measure de-blinding and expectancy. Careful attention should be paid to clinical trial design and the instructions given to participants to allow these confounds to be reduced, estimated and removed from effect size estimates. We urge caution in interpreting effect size estimates from extant psychedelic RCTs.

Efficacy of ketamine for major depressive episodes at 2, 4, and 6-weeks post-treatment: A meta-analysis

RATIONALE

Major depressive episodes are severe mood episodes which occur both in major depressive disorder and bipolar I and II disorder. Major depressive episodes are characterized by debilitating symptoms that often persist and interfere with typical daily functioning. Various treatments exist for major depressive episodes; however, most primary pharmacologic treatments may take weeks to months to provide relief from depressive symptoms. Ketamine is a demonstrated treatment for major depressive episodes, as relief from depressive symptoms can occur rapidly following treatment.

OBJECTIVES

Prior meta-analyses have been conducted to analyze the effectiveness of ketamine for the treatment of major depressive episodes, but at the time of this writing, no meta-analysis had been conducted to observe ketamine treatment efficacy beyond 2 weeks.

METHODS

The present meta-analysis evaluated the efficacy of ketamine for the treatment of major depressive episodes; observations of depressive episode severity were analyzed at 2, 4, and 6-weeks post-treatment.

RESULTS

The present meta-analysis observed large effects at 2 weeks (g = -1.28), 4 weeks, (g = -1.28), and 6 weeks (g = -1.36) post-treatment.

CONCLUSIONS

The results from the present meta-analysis indicate that ketamine can be an effective pharmacologic intervention for major depressive episodes, with treatment effects lasting up to 6 weeks post-ketamine administration, which has many positive implications for treatment.

AutoDep: automatic depression detection using facial expressions based on linear binary pattern descriptor

The psychological health of a person plays an important role in their daily life activities. The paper addresses depression issues with the machine learning model using facial expressions of the patient. Some research has already been done on visual based on depression detection methods, but those are illumination variant. The paper uses feature extraction using LBP (Local Binary Pattern) descriptor, which is illumination invariant. The Viola-Jones algorithm is used for face detection and SVM (support vector machine) is considered for classification along with the LBP descriptor to make a complete model for depression level detection. The proposed method captures frontal face from the videos of subjects and their facial features are extracted from each frame. Subsequently, the facial features are analyzed to detect depression levels with the post-processing model. The performance of the proposed system is evaluated using machine learning algorithms in MATLAB. For the real-time system design, it is necessary to test it on the hardware platform. The LBP descriptor has been implemented on FPGA using Xilinx VIVADO 16.4. The results of the proposed method show satisfactory performance and accuracy for depression detection comparison with similar previous work.

Biomarkers of ketamine's antidepressant effect: a clinical review of genetics, functional connectivity, and neurophysiology

Major depressive disorder (MDD) is one of the leading causes of morbidity and all-cause mortality (including suicide) worldwide, and, unfortunately, first-line monoaminergic antidepressants and evidence-based psychotherapies are not effective for all patients. Subanesthetic doses of the N-methyl-D-aspartate receptor antagonists and glutamate modulators ketamine and S-ketamine have rapid and robust antidepressant efficacy in such treatment-resistant depressed patients (TRD). Yet, as with all antidepressant treatments including electroconvulsive therapy (ECT), not all TRD patients adequately respond, and we are presently unable to a priori predict who will respond or not respond to ketamine. Therefore, antidepressant treatment response biomarkers to ketamine have been a major focus of research for over a decade. In this article, we review the evidence in support of treatment response biomarkers, with a particular focus on genetics, functional magnetic resonance imaging, and neurophysiological studies, i.e. electroencephalography and magnetoencephalography. The studies outlined here lay the groundwork for replication and dissemination.

Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review

Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.

Ketamine for depression

Over the last two decades, the dissociative anaesthetic agent ketamine, an uncompetitive N-Methyl-D-Aspartate (NMDA) receptor antagonist, has emerged as a novel therapy for treatment-resistant depression (TRD), demonstrating rapid and robust antidepressant effects within hours of administration. Ketamine is a racemic mixture composed of equal amounts of (S)-ketamine and (R)-ketamine. Although ketamine currently remains an off-label treatment for TRD, an (S)-ketamine nasal spray has been approved for use in TRD (in conjunction with an oral antidepressant) in the United States and Europe. Despite the promise of ketamine, key challenges including how to maintain response, concerns regarding short and long-term side-effects and the potential for abuse remain. This review provides an overview of the history of ketamine, its use in psychiatry and its basic pharmacology. The clinical evidence for the use of ketamine in depression and potential adverse effects associated with treatment are summarized. A synopsis of some of the putative neurobiological mechanisms underlying ketamine's rapid-acting antidepressant effects is provided before finally outlining future research directions, including the need to identify biomarkers for predicting response and treatment targets that may be used in the development of next-generation rapid-acting antidepressants that may lack ketamine's side-effects or abuse potential.

Transient Dose-dependent Effects of Ketamine on Neural Oscillatory Activity in Wistar-Kyoto Rats

Ketamine is a promising therapeutic for treatment-resistant depression (TRD) but is associated with an array of short-term psychomimetic side-effects. These disparate drug effects may be elicited through the modulation of neural circuit activity. The purpose of this study was to therefore delineate dose- and time-dependent changes in ketamine-induced neural oscillatory patterns in regions of the brain implicated in depression. Wistar-Kyoto rats were used as a model system to study these aspects of TRD neuropathology whereas Wistar rats were used as a control strain. Animals received a low (10 mg/kg) or high (30 mg/kg) dose of ketamine and temporal changes in neural oscillatory activity recorded from the prefrontal cortex (PFC), cingulate cortex (Cg), and nucleus accumbens (NAc) for ninety minutes. Effects of each dose of ketamine on immobility in the forced swim test were also evaluated. High dose ketamine induced a transient increase in theta power in the PFC and Cg, as well as a dose-dependent increase in gamma power in these regions 10-min, but not 90-min, post-administration. In contrast, only low dose ketamine normalized innate deficits in fast gamma coherence between the NAc-Cg and PFC-Cg, an effect that persisted at 90-min post-injection. These low dose ketamine-induced oscillatory alterations were accompanied by a reduction in immobility time in the forced swim test. These results show that ketamine induces time-dependent effects on neural oscillations at specific frequencies. These drug-induced changes may differentially contribute to the psychomimetic and therapeutic effects of the drug.

Efficacy of single and repeated administration of ketamine in unipolar and bipolar depression: a meta-analysis of randomized clinical trials

BACKGROUND

Due to unmet clinical needs for efficient drugs with a rapid onset of antidepressant effects, we aimed to evaluate the efficacy of single-dose ketamine in different subgroups of patients with major depression and establish whether repeated ketamine administration could be a viable strategy to maintain treatment gains.

METHODS

Electronic databases (Medline via PubMed, Embase, Cochrane Library, Trip Database) were systematically searched until February 22, 2019, for published peer-reviewed randomized controlled trials (RCTs) concerning a single and repeated administration of ketamine in patients with major depression. All relevant RCTs were selected and critically appraised, and a meta-analysis of eligible studies was performed.

RESULTS

A total of 20 studies were included in the meta-analysis. The largest effect of ketamine vs. controls in reducing depressive symptoms was observed at 24 h (SMD = - 0.89; 95% CI - 1.24; - 0.53; p < 0.00001); however, a significant difference was shown for up to 7 days after a single dose. Significant differences compared with controls were observed for up to 7 days in treatment-resistant patients and when ketamine was added to ongoing antidepressant treatment, while there were no significant differences at 7 days when ketamine was used as monotherapy. In patients with major depression, initial antidepressant effects of ketamine were maintained during repeated dosing. At 2-3 weeks of repeated ketamine treatment, significant reduction of depression severity scores was observed: SMD = - 0.70; 95% CI - 1.15; - 0.25 or SMD = - 0.81; 95% CI - 1.41; - 0.20 (depending on the dosing regimen used); p ≤ 0.009 vs placebo.

CONCLUSIONS

Our meta-analysis revealed rapid and robust antidepressant effects of single-dose ketamine in patients with treatment-resistant depression (TRD). By pooling data from RCTs, we showed for the first time that repeated ketamine administration is effective in sustaining initial antidepressant effects observed after single dosing.