The use of referenced-EEG (rEEG) in assisting medication selection for the treatment of depression

Neuroelectric Correlates of Human Sexuality: A Review and Meta-Analysis

Many reviews on sexual arousal in humans focus on different brain imaging methods and behavioral observations. Although neurotransmission in the brain is mainly performed through electrochemical signals, there are no systematic reviews of the electrophysiological correlates of sexual arousal. We performed a systematic search on this subject and reviewed 255 studies including various electrophysiological methods. Our results show how neuroelectric signals have been used to investigate genital somatotopy as well as basic genital physiology during sexual arousal and how cortical electric signals have been recorded during orgasm. Moreover, experiments on the interactions of cognition and sexual arousal in healthy subjects and in individuals with abnormal sexual preferences were analyzed as well as case studies on sexual disturbances associated with diseases of the nervous system. In addition, 25 studies focusing on brain potentials during the interaction of cognition and sexual arousal were eligible for meta-analysis. The results showed significant effect sizes for specific brain potentials during sexual stimulation (P3: Cohen's d = 1.82, N = 300, LPP: Cohen's d = 2.30, N = 510) with high heterogeneity between the combined studies. Taken together, our review shows how neuroelectric methods can consistently differentiate sexual arousal from other emotional states.

Electroencephalography for predicting antidepressant treatment success: A systematic review and meta-analysis

BACKGROUND

Patients suffering from major depressive disorder (MDD) regularly experience non-response to treatment for their depressive episode. Personalized clinical decision making could shorten depressive episodes and reduce patient suffering. Although no clinical tools are currently available, machine learning analysis of electroencephalography (EEG) shows promise in treatment response prediction.

METHODS

With a systematic review and meta-analysis, we evaluated the accuracy of EEG for individual patient response prediction. Importantly, we included only prediction studies that used cross-validation. We used a bivariate model to calculate prediction success, as expressed by area-under the curve, sensitivity and specificity. Furthermore, we analyzed prediction success for separate antidepressant interventions.

RESULTS

15 studies with 12 individual patient samples and a total of 479 patients were included. Research methods varied considerably between studies. Meta-analysis of results from this heterogeneous set of studies resulted in an area under the curve of 0.91, a sensitivity of 83 % (95 % CI 74-89 %), and a specificity of 86 % (95 % CI 81-90 %). Classification performance did not significantly differ between treatments. Although studies were all internally validated, no externally validated studies have been reported. We found substantial risk of bias caused by methodological shortcomings such as non-independent feature selection, though performance of non-biased studies was comparable.

LIMITATIONS

Sample sizes were relatively small and no study used external validation, increasing the risk of overestimation of accuracy.

CONCLUSIONS

Electroencephalography can predict the response to antidepressant treatment with high accuracy. However, future studies with more rigorous validation are needed to produce a clinical tool to guide interventions in MDD.

PROSPERO REGISTRATION NUMBER

CRD42021268169.

Components of Behavioral Activation Therapy for Depression Engage Specific Reinforcement Learning Mechanisms in a Pilot Study

Background

Behavioral activation is an evidence-based treatment for depression. Theoretical considerations suggest that treatment response depends on reinforcement learning mechanisms. However, which reinforcement learning mechanisms are engaged by and mediate the therapeutic effect of behavioral activation remains only partially understood, and there are no procedures to measure such mechanisms.

Objective

To perform a pilot study to examine whether reinforcement learning processes measured through tasks or self-report are related to treatment response to behavioral activation.

Method

The pilot study enrolled 13 outpatients (12 completers) with major depressive disorder, from July of 2018 through February of 2019, into a nine-week trial with BA. Psychiatric evaluations, decision-making tests and self-reported reward experience and anticipations were acquired before, during and after the treatment. Task and self-report data were analysed by using reinforcement-learning models. Inferred parameters were related to measures of depression severity through linear mixed effects models.

Results

Treatment effects during different phases of the therapy were captured by specific decision-making processes in the task. During the weeks focusing on the active pursuit of reward, treatment effects were more pronounced amongst those individuals who showed an increase in Pavlovian appetitive influence. During the weeks focusing on the avoidance of punishments, treatment responses were more pronounced in those individuals who showed an increase in Pavlovian avoidance. Self-reported anticipation of reinforcement changed according to formal RL rules. Individual differences in the extent to which learning followed RL rules related to changes in anhedonia.

Conclusions

In this pilot study both task- and self-report-derived measures of reinforcement learning captured individual differences in treatment response to behavioral activation. Appetitive and aversive Pavlovian reflexive processes appeared to be modulated by separate psychotherapeutic interventions, and the modulation strength covaried with response to specific interventions. Self-reported changes in reinforcement expectations are also related to treatment response.

Trial Registry Name

Set Your Goal: Engaging in GO/No-Go Active Learning, #NCT03538535, http://www.clinicaltrials.gov.

A benchmark for prediction of psychiatric multimorbidity from resting EEG data in a large pediatric sample

Psychiatric disorders are among the most common and debilitating illnesses across the lifespan and begin usually during childhood and adolescence, which emphasizes the importance of studying the developing brain. Most of the previous pediatric neuroimaging studies employed traditional univariate statistics on relatively small samples. Multivariate machine learning approaches have a great potential to overcome the limitations of these approaches. On the other hand, the vast majority of existing multivariate machine learning studies have focused on differentiating between children with an isolated psychiatric disorder and typically developing children. However, this line of research does not reflect the real-life situation as the majority of children with a clinical diagnosis have multiple psychiatric disorders (multimorbidity), and consequently, a clinician has the task to choose between different diagnoses and/or the combination of multiple diagnoses. Thus, the goal of the present benchmark is to predict psychiatric multimorbidity in children and adolescents. For this purpose, we implemented two kinds of machine learning benchmark challenges: The first challenge targets the prediction of the seven most prevalent DSM-V psychiatric diagnoses for the available data set, of which each individual can exhibit multiple ones concurrently (i.e. multi-task multi-label classification). Based on behavioral and cognitive measures, a second challenge focuses on predicting psychiatric symptom severity on a dimensional level (i.e. multiple regression task). For the present benchmark challenges, we will leverage existing and future data from the biobank of the Healthy Brain Network (HBN) initiative, which offers a unique large-sample dataset (N = 2042) that provides a wide array of different psychiatric developmental disorders and true hidden data sets. Due to limited real-world practicability and economic viability of MRI measurements, the present challenge will permit only resting state EEG data and demographic information to derive predictive models. We believe that a community driven effort to derive predictive markers from these data using advanced machine learning algorithms can help to improve the diagnosis of psychiatric developmental disorders.

[EEG predictors of therapeutic response in psychiatry]

The literature review provides data on one of the types of biomarkers - EEG predictors of the therapeutic response of patients with different types of mental pathology. It has been shown that the quantitative parameters of the electroencephalogram (EEG) recorded before the start of the treatment course reflect not only the current functional state of the patient's brain, but also its adaptive resources in terms of the possibility and magnitude of response to therapy. The identified EEG predictors of the therapeutic response in patients with depression, schizophrenia and some other mental disorders have a sufficiently high prognostic ability, sensitivity and specificity in determining responders and non-responders, make it possible to carry out a quantitative prediction of the patient's condition after a course of treatment, and also to assist the clinician in choosing medications for optimal therapy.

EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks:A review

Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare of individuals. The present comprehensive review concentrate on the two mental disorders: Major depressive Disorder (MDD) and Bipolar Disorder (BD) with noteworthy publications during the last ten years. There is a big need nowadays for phenotypic characterization of psychiatric disorders with biomarkers. Electroencephalography (EEG) signals could offer a rich signature for MDD and BD and then they could improve understanding of pathophysiological mechanisms underling these mental disorders. In this review, we focus on the literature works adopting neural networks fed by EEG signals. Among those studies using EEG and neural networks, we have discussed a variety of EEG based protocols, biomarkers and public datasets for depression and bipolar disorder detection. We conclude with a discussion and valuable recommendations that will help to improve the reliability of developed models and for more accurate and more deterministic computational intelligence based systems in psychiatry. This review will prove to be a structured and valuable initial point for the researchers working on depression and bipolar disorders recognition by using EEG signals.

Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression

OBJECTIVES

Biomarkers of major depressive disorder (MDD), its phases and forms have long been sought. Objectives were to examine whether the complexity of EEG activity, measured by Higuchi's fractal dimension (HFD) and sample entropy (SampEn), differs between healthy subjects, patients in remission, and in episode phase of the recurrent depression and whether the changes are differentially distributed between hemispheres and cortical regions.

METHODS

Resting state EEG with eyes closed was recorded from 22 patients suffering from recurrent depression (11 in remission, 11 in the episode), and 20 age and sex-matched healthy control subjects. Artifact-free EEG epochs were analyzed by in-house developed programs running HFD and SampEn algorithms.

RESULTS

Depressed patients had higher HFD and SampEn complexity compared to healthy subjects. The complexity was higher in patients who were in remission than in those in the acute episode. Altered complexity was present in the frontal and centro-parietal regions when compared to control group. The complexity in frontal and parietal regions differed between the two phases of depressive disorder.

CONCLUSIONS

Complexity measures of EEG distinguish between the healthy controls, patients in remission and episode. Further studies are needed to establish whether these measures carry a potential to aid clinically relevant decisions about depression.

Machine learning and big data in psychiatry: toward clinical applications

Psychiatry is a medical field concerned with the treatment of mental illness. Psychiatric disorders broadly relate to higher functions of the brain, and as such are richly intertwined with social, cultural, and experiential factors. This makes them exquisitely complex phenomena that depend on and interact with a large number of variables. Computational psychiatry provides two ways of approaching this complexity. Theory-driven computational approaches employ mechanistic models to make explicit hypotheses at multiple levels of analysis. Data-driven machine-learning approaches can make predictions from high-dimensional data and are generally agnostic as to the underlying mechanisms. Here, we review recent advances in the use of big data and machine-learning approaches toward the aim of alleviating the suffering that arises from psychiatric disorders.

Electroencephalographic Biomarkers for Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis

OBJECTIVE

Reducing unsuccessful treatment trials could improve depression treatment. Quantitative EEG (QEEG) may predict treatment response and is being commercially marketed for this purpose. The authors sought to quantify the reliability of QEEG for response prediction in depressive illness and to identify methodological limitations of the available evidence.

METHOD

The authors conducted a meta-analysis of diagnostic accuracy for QEEG in depressive illness, based on articles published between January 2000 and November 2017. The review included all articles that used QEEG to predict response during a major depressive episode, regardless of patient population, treatment, or QEEG marker. The primary meta-analytic outcome was the accuracy for predicting response to depression treatment, expressed as sensitivity, specificity, and the logarithm of the diagnostic odds ratio. Raters also judged each article on indicators of good research practice.

RESULTS

In 76 articles reporting 81 biomarkers, the meta-analytic estimates showed a sensitivity of 0.72 (95% CI=0.67-0.76) and a specificity of 0.68 (95% CI=0.63-0.73). The logarithm of the diagnostic odds ratio was 1.89 (95% CI=1.56-2.21), and the area under the receiver operator curve was 0.76 (95% CI=0.71-0.80). No specific QEEG biomarker or specific treatment showed greater predictive power than the all-studies estimate in a meta-regression. Funnel plot analysis suggested substantial publication bias. Most studies did not use ideal practices.

CONCLUSIONS

QEEG does not appear to be clinically reliable for predicting depression treatment response, as the literature is limited by underreporting of negative results, a lack of out-of-sample validation, and insufficient direct replication of previous findings. Until these limitations are remedied, QEEG is not recommended for guiding selection of psychiatric treatment.

A Promising New Strategy to Improve Treatment Outcomes for Patients with Depression

Each year, ineffective medical management of patients with mental illness compromises the health and well-being of individuals, and also impacts communities and our society. A variety of interrelated factors have impeded the health system's ability to treat patients with behavior health conditions adequately. A key contributing factor is a lack of objective markers to help predict patient response to specific drugs that has led to patterns of “trial and error” prescribing. For many years, clinicians have sought objective data (eg, a laboratory or imaging test) to assist them in selecting appropriate treatments for individual patients. Electroencephalogram (EEG) findings coupled with medication outcomes data may provide a solution. “Crowdsourced” physician registries that reference clinical outcomes to individual patient physiology have been used successfully for cancers. These techniques are now being explored in the context of behavioral health care. The Psychiatric EEG Evaluation Registry (PEER) is one such approach. PEER is a clinical phenotypic database comprising more than 11,000 baseline EEGs and more than 39,000 outcomes of medication treatment for a variety of mental health diagnoses. Collective findings from 45 studies (3130 patients) provide compelling evidence for PEER as a relatively simple, inexpensive predictor of likely patient response to specific antidepressants and likely treatment-related side effects (including suicidal ideation).

Quantitative Electroencephalography in Guiding Treatment of Major Depression

This paper reviews significant contributions to the evidence for the use of quantitative electroencephalography features as biomarkers of depression treatment and examines the potential of such technology to guide pharmacotherapy. Frequency band abnormalities such as alpha and theta band abnormalities have shown promise as have combinatorial measures such as cordance (a measure combining alpha and theta power) and the Antidepressant Treatment Response Index in predicting medication treatment response. Nevertheless, studies have been hampered by methodological problems and inconsistencies, and these approaches have ultimately failed to elicit any significant interest in actual clinical practice. More recent machine learning approaches such as the Psychiatric Encephalography Evaluation Registry (PEER) technology and other efforts analyze large datasets to develop variables that may best predict response rather than test a priori hypotheses. PEER is a technology that may go beyond predicting response to a particular antidepressant and help to guide pharmacotherapy.

A wavelet-based technique to predict treatment outcome for Major Depressive Disorder

Treatment management for Major Depressive Disorder (MDD) has been challenging. However, electroencephalogram (EEG)-based predictions of antidepressant’s treatment outcome may help during antidepressant’s selection and ultimately improve the quality of life for MDD patients. In this study, a machine learning (ML) method involving pretreatment EEG data was proposed to perform such predictions for Selective Serotonin Reuptake Inhibitor (SSRIs). For this purpose, the acquisition of experimental data involved 34 MDD patients and 30 healthy controls. Consequently, a feature matrix was constructed involving time-frequency decomposition of EEG data based on wavelet transform (WT) analysis, termed as EEG data matrix. However, the resultant EEG data matrix had high dimensionality. Therefore, dimension reduction was performed based on a rank-based feature selection method according to a criterion, i.e., receiver operating characteristic (ROC). As a result, the most significant features were identified and further be utilized during the training and testing of a classification model, i.e., the logistic regression (LR) classifier. Finally, the LR model was validated with 100 iterations of 10-fold cross-validation (10-CV). The classification results were compared with short-time Fourier transform (STFT) analysis, and empirical mode decompositions (EMD). The wavelet features extracted from frontal and temporal EEG data were found statistically significant. In comparison with other time-frequency approaches such as the STFT and EMD, the WT analysis has shown highest classification accuracy, i.e., accuracy = 87.5%, sensitivity = 95%, and specificity = 80%. In conclusion, significant wavelet coefficients extracted from frontal and temporal pre-treatment EEG data involving delta and theta frequency bands may predict antidepressant’s treatment outcome for the MDD patients.

Computational psychiatry as a bridge from neuroscience to clinical applications

Translating advances in neuroscience into benefits for patients with mental illness presents enormous challenges because it involves both the most complex organ, the brain, and its interaction with a similarly complex environment. Dealing with such complexities demands powerful techniques. Computational psychiatry combines multiple levels and types of computation with multiple types of data in an effort to improve understanding, prediction and treatment of mental illness. Computational psychiatry, broadly defined, encompasses two complementary approaches: data driven and theory driven. Data-driven approaches apply machine-learning methods to high-dimensional data to improve classification of disease, predict treatment outcomes or improve treatment selection. These approaches are generally agnostic as to the underlying mechanisms. Theory-driven approaches, in contrast, use models that instantiate prior knowledge of, or explicit hypotheses about, such mechanisms, possibly at multiple levels of analysis and abstraction. We review recent advances in both approaches, with an emphasis on clinical applications, and highlight the utility of combining them.

Computational neuroimaging strategies for single patient predictions

Neuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to machine learning, which tries to establish predictive links between features of the observed data and clinical variables, is the deployment of computational models for inferring on the (patho)physiological and cognitive mechanisms that generate behavioural and neuroimaging responses. This paper discusses the rationale behind a computational approach to neuroimaging-based single-subject inference, focusing on its potential for characterising disease mechanisms in individual subjects and mapping these characterisations to clinical predictions. Following an overview of two main approaches - Bayesian model selection and generative embedding - which can link computational models to individual predictions, we review how these methods accommodate heterogeneity in psychiatric and neurological spectrum disorders, help avoid erroneous interpretations of neuroimaging data, and establish a link between a mechanistic, model-based approach and the statistical perspectives afforded by machine learning.

Using Electroencephalography for Treatment Guidance in Major Depressive Disorder

Given the high prevalence of treatment-resistant depression and the long delays in finding effective treatments via trial and error, valid biomarkers of treatment outcome with the ability to guide treatment selection represent one of the most important unmet needs in mood disorders. A large body of research has investigated, for this purpose, biomarkers derived from electroencephalography (EEG), using resting state EEG or evoked potentials. Most studies have focused on specific EEG features (or combinations thereof), whereas more recently machine-learning approaches have been used to define the EEG features with the best predictive abilities without a priori hypotheses. While reviewing these different approaches, we have focused on the predictor characteristics and the quality of the supporting evidence.

The use of the Psychiatric Electroencephalography Evaluation Registry (PEER) to personalize pharmacotherapy

PURPOSE

This study aims to determine whether Psychiatric Electroencephalography Evaluation Registry (PEER) Interactive (an objective, adjunctive tool based on a comparison of a quantitative electroencephalogram to an existing registry of patient outcomes) is more effective than the current standard of care in treatment of subjects suffering from depression.

PATIENTS AND METHODS

This is an interim report of an ongoing, 2-year prospective, randomized, double blind, controlled study to evaluate PEER Interactive in guiding medication selection in subjects with a primary diagnosis of depression vs standard treatment. Subjects in treatment at two military hospitals were blinded as to study group assignment and their self-report symptom ratings were also blinded. Quick Inventory of Depressive Symptomatology, Self-Report (QIDS-SR16) depression scores were the primary efficacy endpoint. One hundred and fifty subjects received a quantitative electroencephalography exam and were randomized to either treatment as usual or PEER-informed pharmacotherapy. Subjects in the control group were treated according to Veterans Administration/Department of Defense Guidelines, the current standard of care. In the experimental group, the attending physician received a PEER report ranking the subject's likely clinical response to on-label medications.

RESULTS

In this post hoc interim analysis subjects were separated into Report Followed and Report Not Followed groups - based on the concordance between their subsequent treatment and PEER medication guidance. We thus evaluated the predictive validity of PEER recommendations. We found significantly greater improvements in depression scores (QIDS-SR16 P<0.03), reduction in suicidal ideation (Concise Health Risk Tracking Scale-SR7 P<0.002), and post-traumatic stress disorder (PTSD) score improvement (PTSD Checklist Military/Civilian P<0.04) for subjects treated with PEER-recommended medications compared to those who did not follow PEER recommendations.

CONCLUSION

This interim analysis suggests that an objective tool such as PEER Interactive can help improve medication selection. Consistent with results of earlier studies, it supports the hypothesis that PEER-guided treatment offers distinct advantages over the current standard of care.

Neuroimaging and psychiatry: the long road from bench to bedside

Advances in neuroscience have revolutionized our understanding of the central nervous system. Neuroimaging technologies, in particular, have begun to reveal the complex anatomical, physiological, biochemical, genetic, and molecular organizational structure of the organ at the center of that system: the human brain. More recently, neuroimaging technologies have enabled the investigation of normal brain function and are being used to gain important new insights into the mechanisms behind many neuropsychiatric disorders. This research has implications for psychiatric diagnosis, treatment, and risk assessment. However, with some exceptions, neuroimaging is still a research tool, not ready for use in clinical psychiatry.

Personalized Medicine in ADHD and Depression: Use of Pharmaco-EEG

This chapter summarises recent developments on personalised medicine in psychiatry with a focus on ADHD and depression and their associated biomarkers and phenotypes. Several neurophysiological subtypes in ADHD and depression and their relation to treatment outcome are reviewed. The first important subgroup consists of the 'impaired vigilance' subgroup with often-reported excess frontal theta or alpha activity. This EEG subtype explains ADHD symptoms well based on the EEG Vigilance model, and these ADHD patients responds well to stimulant medication. In depression this subtype might be unresponsive to antidepressant treatments, and some studies suggest these depressive patients might respond better to stimulant medication. Further research should investigate whether sleep problems underlie this impaired vigilance subgroup, thereby perhaps providing a route to more specific treatments for this subgroup. Finally, a slow individual alpha peak frequency is an endophenotype associated with treatment resistance in ADHD and depression. Future studies should incorporate this endophenotype in clinical trials to investigate further the efficacy of new treatments in this substantial subgroup of patients.

Toward a neuroimaging treatment selection biomarker for major depressive disorder

IMPORTANCE

Currently, fewer than 40% of patients treated for major depressive disorder achieve remission with initial treatment. Identification of a biological marker that might improve these odds could have significant health and economic impact.

OBJECTIVE

To identify a candidate neuroimaging "treatment-specific biomarker" that predicts differential outcome to either medication or psychotherapy.

DESIGN

Brain glucose metabolism was measured with positron emission tomography prior to treatment randomization to either escitalopram oxalate or cognitive behavior therapy for 12 weeks. Patients who did not remit on completion of their phase 1 treatment were offered enrollment in phase 2 comprising an additional 12 weeks of treatment with combination escitalopram and cognitive behavior therapy.

SETTING

Mood and anxiety disorders research program at an academic medical center.

PARTICIPANTS

Men and women aged 18 to 60 years with currently untreated major depressive disorder.

INTERVENTION

Randomized assignment to 12 weeks of treatment with either escitalopram oxalate (10-20 mg/d) or 16 sessions of manual-based cognitive behavior therapy.

MAIN OUTCOME AND MEASURE

Remission, defined as a 17-item Hamilton depression rating scale score of 7 or less at both weeks 10 and 12, as assessed by raters blinded to treatment.

RESULTS

Positive and negative predictors of remission were identified with a 2-way analysis of variance treatment (escitalopram or cognitive behavior therapy) × outcome (remission or nonresponse) interaction. Of 65 protocol completers, 38 patients with clear outcomes and usable positron emission tomography scans were included in the primary analysis: 12 remitters to cognitive behavior therapy, 11 remitters to escitalopram, 9 nonresponders to cognitive behavior therapy, and 6 nonresponders to escitalopram. Six limbic and cortical regions were identified, with the right anterior insula showing the most robust discriminant properties across groups (effect size = 1.43). Insula hypometabolism (relative to whole-brain mean) was associated with remission to cognitive behavior therapy and poor response to escitalopram, while insula hypermetabolism was associated with remission to escitalopram and poor response to cognitive behavior therapy.

CONCLUSIONS AND RELEVANCE

If verified with prospective testing, the insula metabolism-based treatment-specific biomarker defined in this study provides the first objective marker, to our knowledge, to guide initial treatment selection for depression.

TRIAL REGISTRATION

Registered at clinicaltrials.gov (NCT00367341).

A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder

OBJECTIVE

The problem of identifying, in advance, the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we investigate the performance of the proposed machine learning (ML) methodology (based on the pre-treatment electroencephalogram (EEG)) for prediction of response to treatment with a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD).

METHODS

A relatively small number of most discriminating features are selected from a large group of candidate features extracted from the subject's pre-treatment EEG, using a machine learning procedure for feature selection. The selected features are fed into a classifier, which was realized as a mixture of factor analysis (MFA) model, whose output is the predicted response in the form of a likelihood value. This likelihood indicates the extent to which the subject belongs to the responder vs. non-responder classes. The overall method was evaluated using a "leave-n-out" randomized permutation cross-validation procedure.

RESULTS

A list of discriminating EEG biomarkers (features) was found. The specificity of the proposed method is 80.9% while sensitivity is 94.9%, for an overall prediction accuracy of 87.9%. There is a 98.76% confidence that the estimated prediction rate is within the interval [75%, 100%].

CONCLUSIONS

These results indicate that the proposed ML method holds considerable promise in predicting the efficacy of SSRI antidepressant therapy for MDD, based on a simple and cost-effective pre-treatment EEG.

SIGNIFICANCE

The proposed approach offers the potential to improve the treatment of major depression and to reduce health care costs.

The vigilance regulation model of affective disorders and ADHD

According to the recently proposed vigilance model of affective disorders (vigilance in the sense of "brain arousal"), manic behaviour is partly interpreted as an autoregulatory attempt to stabilise vigilance by creating a stimulating environment, and the sensation avoidance and withdrawal in Major Depressive Disorder (MDD) is seen as an autoregulatory reaction to tonically increased vigilance. Indeed, using a newly developed EEG-based algorithm, hyperstable vigilance was found in MDD, and the contrary, with rapid drops to sleep stages, in mania. Furthermore, destabilising vigilance (e.g. by sleep deprivation) triggers (hypo)mania and improves depression, whereas stabilising vigilance, e.g. by prolonged sleep, improves mania. ADHD and mania have common symptoms, and the unstable vigilance might be a common pathophysiology. There is even evidence that psychostimulants might ameliorate both ADHD and mania. Hyperactivity of the noradrenergic system could explain both the high vigilance level in MDD and, as recently argued, anhedonia and behavioural inhibition. Interestingly, antidepressants and electroconvulsions decrease the firing rate of neurons in the noradrenergic locus coeruleus, whereas many antimanic drugs have opposite effects.

Citalopram versus other anti-depressive agents for depression

BACKGROUND

Recent US and UK clinical practice guidelines recommend that second-generation antidepressants should be considered amongst the best first-line options when drug therapy is indicated for a depressive episode. Systematic reviews have already highlighted some differences in efficacy between second-generation antidepressants. Citalopram, one of the first selective serotonin reuptake inhibitors (SSRI) introduced in the market, is one of these antidepressant drugs that clinicians use for routine depression care.

OBJECTIVES

To assess the evidence for the efficacy, acceptability and tolerability of citalopram in comparison with tricyclics, heterocyclics, other SSRIs and other conventional and non-conventional antidepressants in the acute-phase treatment of major depression.

SEARCH METHODS

We searched The Cochrane Collaboration Depression, Anxiety and Neurosis Controlled Trials Register and the Cochrane Central Register of Controlled Trials up to February 2012. No language restriction was applied. We contacted pharmaceutical companies and experts in this field for supplemental data.

SELECTION CRITERIA

Randomised controlled trials allocating patients with major depression to citalopram versus any other antidepressants.

DATA COLLECTION AND ANALYSIS

Two reviewers independently extracted data. Information extracted included study characteristics, participant characteristics, intervention details and outcome measures in terms of efficacy (the number of patients who responded or remitted), patient acceptability (the number of patients who failed to complete the study) and tolerability (side-effects).

MAIN RESULTS

Thirty-seven trials compared citalopram with other antidepressants (such as tricyclics, heterocyclics, SSRIs and other antidepressants, either conventional ones, such as mirtazapine, venlafaxine and reboxetine, or non-conventional, like hypericum). Citalopram was shown to be significantly less effective than escitalopram in achieving acute response (odds ratio (OR) 1.47, 95% confidence interval (CI) 1.08 to 2.02), but more effective than paroxetine (OR 0.65, 95% CI 0.44 to 0.96) and reboxetine (OR 0.63, 95% CI 0.43 to 0.91). Significantly fewer patients allocated to citalopram withdrew from trials due to adverse events compared with patients allocated to tricyclics (OR 0.54, 95% CI 0.38 to 0.78) and fewer patients allocated to citalopram reported at least one side effect than reboxetine or venlafaxine (OR 0.64, 95% CI 0.42 to 0.97 and OR 0.46, 95% CI 0.24 to 0.88, respectively).

AUTHORS' CONCLUSIONS

Some statistically significant differences between citalopram and other antidepressants for the acute phase treatment of major depression were found in terms of efficacy, tolerability and acceptability. Citalopram was more efficacious than paroxetine and reboxetine and more acceptable than tricyclics, reboxetine and venlafaxine, however, it seemed to be less efficacious than escitalopram. As with most systematic reviews in psychopharmacology, the potential for overestimation of treatment effect due to sponsorship bias and publication bias should be borne in mind when interpreting review findings. Economic analyses were not reported in the included studies, however, cost effectiveness information is needed in the field of antidepressant trials.

Measuring severe adverse events and medication selection using a "PEER Report" for nonpsychotic patients: a retrospective chart review

We previously reported on an objective new tool that uses quantitative electroencephalography (QEEG) normative- and referenced-electroencephalography sampling databases (currently called Psychiatric EEG Evaluation Registry [PEER]), which may assist physicians in determining medication selection for optimal efficacy to overcome trial-and-error prescribing. The PEER test compares drug-free QEEG features for individual patients to a database of patients with similar EEG patterns and known outcomes after pharmacological interventions. Based on specific EEG data elements and historical outcomes, the PEER Report may also serve as a marker of future severe adverse events (eg, agitation, hostility, aggressiveness, suicidality, homicidality, mania, hypomania) with specific medications. We used a retrospective chart review to investigate the clinical utility of such a registry in a naturalistic environment.

RESULTS

This chart review demonstrated significant improvement on the global assessment scales Clinical Global Impression - Improvement and Quality of Life Enjoyment and Satisfaction - Short Form as well as time to maximum medical improvement and decreased suicidality occurrences. The review also showed that 54.5% of previous medications causing a severe adverse event would have been raised as a caution had the PEER Report been available at the time the drug was prescribed. Finally, due to the significant amount of off-label prescribing of psychotropic medications, additional, objective, evidence-based data aided the prescriber toward better choices.

CONCLUSION

The PEER Report may be useful, particularly in treatment-resistant patients, in helping to guide medication selection. Based on the preliminary data obtained from this chart review, additional studies are warranted to establish the safety and efficacy of adding PEER data when making medication decisions.

Electroencephalography-derived biomarkers of antidepressant response

Recent meta-analyses point to the relatively low efficacy of commonly used antidepressant medications. Selecting the most effective medications for depressed subjects having failed previous treatments is especially difficult. There is a clear need for objective biomarkers that could assist and optimize such treatment selection. We will review here a growing body of evidence suggesting that several electroencephalography (EEG)-based methods may be useful for predicting antidepressant response and eventually for guiding clinical treatment decisions. While most of these methods are based on resting-state EEGs (e.g., alpha- and theta-band EEG abnormalities, the combined Antidepressant Response Index (ATR), cordance, referenced EEG), others include EEG source localization and evoked potentials. The limitations of these technologies and the potential clinical uses will also be outlined.

Retrospective chart review of a referenced EEG database in assisting medication selection for treatment of depression in patients with eating disorders

BACKGROUND

A retrospective chart review was undertaken in a private clinic to examine the clinical outcomes for patients with an eating disorder comorbid with depression or bipolar illness who underwent a referenced electroencephalographic (EEG) database analysis to help guide medication selection.

METHOD

We examined 33 charts for patients with the primary psychiatric diagnosis of an eating disorder and comorbid major depressive disorder or bipolar disorder who underwent a quantitative EEG database assessment to provide additional information for choices of medication. The current analysis includes data from 22 subjects who accepted treatments based on information from the referenced-EEG medication database. Hamilton Depression Rating Scale, Clinical Global Impression-Severity, Clinical Global Impression-Improvement, and hospitalization data were examined for these patients.

RESULTS

Patients whose EEG data was used for clinical treatment reported significant decreases in associated depressive symptoms (HDRS scores), overall severity of illness (Clinical Global Impression-Severity), and overall clinical global improvement (Clinical Global Impression- Improvement). This cohort also reported fewer inpatient, residential, and partial hospitalization program days following referenced-EEG compared with the two-year period prior to treatment.

CONCLUSION

These findings are consistent with previously reported data for patients with eating disorders and suggest the need for future studies using EEG data correlated with those from other patients with similar quantitative EEG features.

Polypharmacy or medication washout: an old tool revisited

There has been a rapid increase in the use of polypharmacy in psychiatry possibly due to the introduction of newer drugs, greater availability of these newer drugs, excessive confidence in clinical trial results, widespread prescribing of psychotropic medications by primary care, and pressure to augment with additional medications for unresolved side effects or greater efficacy. Even the new generation of medications may not hold significant advantages over older drugs. In fact, there may be additional safety risks with polypharmacy being so widespread. Washout, as a clinical tool, is rarely done in medication management today. Studies have shown that augmenting therapy with additional medications resulted in 9.1%-34.1% dropouts due to intolerance of the augmentation, whereas studies of medication washout demonstrated only 5.9%-7.8% intolerance to the washout procedure. These perils justify reconsideration of medication washout before deciding on augmentation. There are unwarranted fears and resistance in the medical community toward medication washout, especially at the moment a physician is trying to decide whether to washout or add more medications to the treatment regimen. However, medication washout provides unique benefits to the physician: it establishes a new baseline of the disorder, helps identify medication efficacy from their adverse effects, and provides clarity of diagnosis and potential reduction of drug treatments, drug interactions, and costs. It may also reduce overall adverse events, not to mention a potential to reduce liability. After washout, physicians may be able to select the appropriate polypharmacy more effectively and safely, if necessary. Washout, while not for every patient, may be an effective tool for physicians who need to decide on whether to add potentially risky polypharmacy for a given patient. The risks of washout may, in some cases, be lower and the benefits may be clearly helpful for diagnosis, understanding medication effects, the doctor/patient relationship, and safer use of polypharmacy if indicated.