The promise of the quantitative electroencephalogram as a predictor of antidepressant treatment outcomes in major depressive disorder

[Electroencephalography: features of the obtained data and its applicability in psychiatry]

Presently, there is an increased interest in expanding the range of diagnostic and scientific applications of electroencephalography (EEG). The method is attractive due to non-invasiveness, availability of equipment with a wide range of modifications for various purposes, and the ability to track the dynamics of brain electrical activity directly and with high temporal resolution. Spectral, coherency and other types of analysis provide volumetric information about its power, frequency distribution, spatial organization of signal and its self-similarity in dynamics or in different sections at a time. The development of computing technologies provides processing of volumetric data obtained using EEG and a qualitatively new level of their analysis using various mathematical models. This review discusses benefits and limitations of using the EEG in scientific research, currently known interpretation of the obtained data and its physiological and pathological correlates. It is expected to determine the complex relationship between the parameters of brain electrical activity and various functional and pathological conditions. The possibility of using EEG characteristics as biomarkers of various physiological and pathological conditions is being considered. Electronic databases, including MEDLINE (on PubMed), Google Scholar and Russian Scientific Citation Index (RSCI, on elibrary.ru), scientific journals and books were searched to find relevant studies.

Sub-Scalp Implantable Telemetric EEG (SITE) for the Management of Neurological and Behavioral Disorders beyond Epilepsy

Sub-scalp Implantable Telemetric EEG (SITE) devices are under development for the treatment of epilepsy. However, beyond epilepsy, continuous EEG analysis could revolutionize the management of patients suffering from all types of brain disorders. This article reviews decades of foundational EEG research, collected from short-term routine EEG studies of common neurological and behavioral disorders, that may guide future SITE management and research. Established quantitative EEG methods, like spectral EEG power density calculation combined with state-of-the-art machine learning techniques applied to SITE data, can identify new EEG biomarkers of neurological disease. From distinguishing syncopal events from seizures to predicting the risk of dementia, SITE-derived EEG biomarkers can provide clinicians with real-time information about diagnosis, treatment response, and disease progression.

Deep transcranial magnetic stimulation alters resting-state neurophysiological traits in major depressive disorder

OBJECTIVE

Major depressive disorder (MDD) is one of the most prevalent and debilitating health conditions worldwide; unfortunately, many patients do not respond to traditional antidepressant medication or talk therapy approaches. Deep transcranial magnetic stimulation (Deep TMS) has emerged as an effective treatment option for such "treatmentresistant" cases; however, the mechanisms by which Deep TMS attenuates depressive symptoms are still ambiguous.

METHODS

In the current study, resting-state quantitative electroencephalography (QEEG) measures were assessed pre-and-post treatment to illustrate neurophysiological changes resulting from Deep TMS.

RESULTS

The results showed reduced slow-frequency brain activity (delta and theta waves) in the prefrontal cortex following 36 treatments. Additionally, baseline QEEG measures predicted treatment response with approximately 90 % accuracy.

CONCLUSIONS

These findings provide preliminary evidence that TMS improves depressive symptoms by mitigating slow-wave brain activity in the prefrontal cortex.

SIGNIFICANCE

Deep TMS paired with QEEG should continue to be utilized for treatment of MDD in clinical practice and future studies should explore its potential for other neuropsychiatric conditions.

Retrospective analysis of quantitative electroencephalography changes in a dissimulating patient after dying by suicide: A single case report

We present the case of a 49-year-old man who was diagnosed with depressive disorder, with the first episode having a strong reactive factor. He was involuntarily admitted to a psychiatric hospital after a failed attempt at taking his own life, where he responded to psychotherapy and antidepressant therapy, as evidenced by a >60% reduction in his MADRS total score. He was discharged after 10 days of treatment, denied having suicidal ideations, and was motivated to follow the recommended outpatient care. The risk for suicide during hospitalization was also assessed using suicide risk assessment tools and psychological assessments, including projective tests. The patient underwent a follow-up examination with an outpatient psychiatrist on the 7th day after discharge, during which the suicide risk assessment tool was administered. The results indicated no acute suicide risk or worsening of depressive symptoms. On the 10th day after discharge, the patient took his own life by jumping out of the window of his flat. We believe that the patient had dissimulated his symptoms and possessed suicidal ideations, which were not detected despite repeated examinations specifically designed to assess suicidality and depression symptoms. We retrospectively analyzed his quantitative electroencephalography (QEEG) records to evaluate the change in prefrontal theta cordance as a potentially promising biomarker of suicidality, given the inconclusive results of studies published to date. An increase in prefrontal theta cordance value was found after the first week of antidepressant therapy and psychotherapy in contrast to the expected decrease due to the fading of depressive symptoms. As demonstrated by the provided case study, we hypothesized that prefrontal theta cordance may be an EEG indicator of a higher risk of non-responsive depression and suicidality despite therapeutic improvement.

An effective brain connectivity technique to predict repetitive transcranial magnetic stimulation outcome for major depressive disorder patients using EEG signals

One of the most effective treatments for drug-resistant Major depressive disorder (MDD) patients is repetitive transcranial magnetic stimulation (rTMS). To improve treatment efficacy and reduce health care costs, it is necessary to predict the treatment response. In this study, we intend to predict the rTMS treatment response in MDD patients from electroencephalogram (EEG) signals before starting the treatment using machine learning approaches. Effective brain connectivity of 19-channel EEG data of MDD patients was calculated by the direct directed transfer function (dDTF) method. Then, using three feature selection methods, the best features were selected and patients were classified as responders or non-responders to rTMS treatment by using the support vector machine (SVM). Results on the 34 MDD patients indicated that the Fp2 region in the delta and theta frequency bands has a significant difference between the two groups and can be used as a significant brain biomarker to assess the rTMS treatment response. Also, the highest accuracy (89.6%) using the SVM classifier for the best features of the dDTF method based on the area under the receiver operating characteristic curve (AUC-ROC) criteria was obtained by combining the delta and theta frequency bands. Consequently, the proposed method can accurately detect the rTMS treatment response in MDD patients before starting treatment on the EEG signal to avoid financial and time costs to patients and medical centers.

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.

Changes in Electric Brain Response to Affective Stimuli in the First Week of Antidepressant Treatment: An Exploratory Study

INTRODUCTION

Asymmetrical alpha and frontal theta activity have been discussed as neurobiological markers for antidepressant treatment response. While most studies focus on resting-state EEG, there is evidence that task-related activity assessed at multiple time points might be superior in detecting subtle early differences.

METHODS

This was a naturalistic study design assessing participants in a psychiatric in- and outpatient hospital setting. We investigated stimulus-related EEG asymmetry (frontal and occipital alpha-1 and alpha-2) and power (frontal midline theta) assessed at baseline and 1 week after initiation of pharmacological depression treatment while presenting affective stimuli. We then compared week 4 responders and nonresponders to antidepressant treatment.

RESULTS

Follow-up analyses of a significant group × emotion × time interaction (p < 0.04) for alpha-1 asymmetry showed that responders differed significantly at baseline in their asymmetry scores in response to sad compared to happy faces with a change in this pattern 1 week later. Nonresponders did not show this pattern. No significant results were found for alpha-2, occipital alpha-1, and occipital alpha-2 asymmetry or frontal midline theta power.

DISCUSSION

Our study addresses the gap in comparisons of task-related EEG activity changes measured at two time points and supports the potential value of this approach in detecting early differences in responders versus nonresponders to pharmacological treatment. Important limitations include the small sample size and the noncontrolled study design.

Investigating the Effects of Auditory and Vibrotactile Rhythmic Sensory Stimulation on Depression: An EEG Pilot Study

Background

Major depressive disorder (MDD) is a persistent psychiatric condition and one of the leading causes of global disease burden. In a previous study, we investigated the effects of a five-week intervention consisting of rhythmic gamma frequency (30-70 Hz) vibroacoustic stimulation in 20 patients formally diagnosed with MDD. In that study, the findings suggested a significant clinical improvement in depression symptoms as measured using the Montgomery-Asberg Depression Rating Scale (MADRS), with 37% of participants meeting the criteria for clinical response. The goal of the present research was to examine possible changes from baseline to posttreatment in resting-state electroencephalography (EEG) recordings using the same treatment protocol and to characterize basic changes in EEG related to treatment response.

Materials and methods

The study sample consisted of 19 individuals aged 18-70 years with a clinical diagnosis of MDD. The participants were assessed before and after a five-week treatment period, which consisted of listening to an instrumental musical track on a vibroacoustic device, delivering auditory and vibrotactile stimulus in the gamma-band range (30-70 Hz, with particular emphasis on 40 Hz). The primary outcome measure was the change in Montgomery-Asberg Depression Rating Scale (MADRS) from baseline to posttreatment and resting-state EEG.

Results

Analysis comparing MADRS score at baseline and post-intervention indicated a significant change in the severity of depression symptoms after five weeks (t = 3.9923, df = 18, p = 0.0009). The clinical response rate was 36.85%. Resting-state EEG power analysis revealed a significant increase in occipital alpha power (t = -2.149, df = 18, p = 0.04548), as well as an increase in the prefrontal gamma power of the responders (t = 2.8079, df = 13.431, p = 0.01442).

Conclusions

The results indicate that improvements in MADRS scores after rhythmic sensory stimulation (RSS) were accompanied by an increase in alpha power in the occipital region and an increase in gamma in the prefrontal region, thus suggesting treatment effects on cortical activity in depression. The results of this pilot study will help inform subsequent controlled studies evaluating whether treatment response to vibroacoustic stimulation constitutes a real and replicable reduction of depressive symptoms and to characterize the underlying mechanisms.

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.

Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021

BACKGROUND

With the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis.

METHODS

QEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis.

RESULTS

A total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends.

CONCLUSION

The present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.

The Role of Quantitative EEG in the Diagnosis of Neuropsychiatric Disorders

Quantitative electroencephalography (QEEG) is a modern type of electroencephalography (EEG) analysis that involves recording digital EEG signals which are processed, transformed, and analyzed using complex mathematical algorithms. QEEG has brought new techniques of EEG signals feature extraction: analysis of specific frequency band and signal complexity, analysis of connectivity, and network analysis. The clinical application of QEEG is extensive, including neuropsychiatric disorders, epilepsy, stroke, dementia, traumatic brain injury, mental health disorders, and many others. In this review, we talk through existing evidence on the practical applications of this clinical tool. We conclude that to date, the role of QEEG is not necessarily to pinpoint an immediate diagnosis but to provide additional insight in conjunction with other diagnostic evaluations in order to objective information necessary for obtaining a precise diagnosis, correct disease severity assessment, and specific treatment response evaluation.

Spatio-temporal dynamics of EEG features during sleep in major depressive disorder after treatment with escitalopram: a pilot study

BACKGROUND

Previous studies have shown escitalopram is related to sleep quality. However, effects of escitalopram on dynamics of electroencephalogram (EEG) features especially during different sleep stages have not been reported. This study may help to reveal pharmacological mechanism underlying escitalopram treatment.

METHODS

The spatial and temporal responses of patients with major depressive disorder (MDD) to escitalopram treatment were analyzed in this study. Eleven MDD patients and eleven healthy control subjects who completed eight weeks' treatment of escitalopram were included in the final statistics. Six-channel sleep EEG signals were acquired during sleep. Power spectrum and nonlinear dynamics were used to analyze the spatio-temporal dynamics features of the sleep EEG after escitalopram treatment.

RESULTS

For temporal dynamics: after treatment, there was a significant increase in the relative energy (RE) of δ1 band (0.5 - 2 Hz), accompanied by a significant decrease in the RE of β2 band (20 - 30 Hz). Lempel-Ziv complexity and Co - complexity values were significantly lower. EEG changes at different sleep stages also showed the same regulation as throughout the night sleep. For spatio dynamics: after treatment, the EEG response of the left and right hemisphere showed asymmetry. Regarding band-specific EEG complexity estimations, δ1 and β2 in stage-1 and δ1 in stage-2 sleep stage in frontal cortex is found to be much more sensitive to escitalopram treatment in comparison to central and occipital cortices.

CONCLUSIONS

The sleep quality of MDD patients improved, EEG response occurred asymmetry in left and right hemispheres due to escitalopram treatment, and frontal cortex is found to be much more sensitive to escitalopram treatment. These findings may contribute to a comprehensive understanding of the pharmacological mechanism of escitalopram in the treatment of depression.

Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression

IMPORTANCE

Social and economic costs of depression are exacerbated by prolonged periods spent identifying treatments that would be effective for a particular patient. Thus, a tool that reliably predicts an individual patient's response to treatment could significantly reduce the burden of depression.

OBJECTIVE

To estimate how accurately an outcome of escitalopram treatment can be predicted from electroencephalographic (EEG) data on patients with depression.

DESIGN, SETTING, AND PARTICIPANTS

This prognostic study used a support vector machine classifier to predict treatment outcome using data from the first Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study. The CAN-BIND-1 study comprised 180 patients (aged 18-60 years) diagnosed with major depressive disorder who had completed 8 weeks of treatment. Of this group, 122 patients had EEG data recorded before the treatment; 115 also had EEG data recorded after the first 2 weeks of treatment.

INTERVENTIONS

All participants completed 8 weeks of open-label escitalopram (10-20 mg) treatment.

MAIN OUTCOMES AND MEASURES

The ability of EEG data to predict treatment outcome, measured as accuracy, specificity, and sensitivity of the classifier at baseline and after the first 2 weeks of treatment. The treatment outcome was defined in terms of change in symptom severity, measured by the Montgomery-Åsberg Depression Rating Scale, before and after 8 weeks of treatment. A patient was designated as a responder if the Montgomery-Åsberg Depression Rating Scale score decreased by at least 50% during the 8 weeks and as a nonresponder if the score decrease was less than 50%.

RESULTS

Of the 122 participants who completed a baseline EEG recording (mean [SD] age, 36.3 [12.7] years; 76 [62.3%] female), the classifier was able to identify responders with an estimated accuracy of 79.2% (sensitivity, 67.3%; specificity, 91.0%) when using only the baseline EEG data. For a subset of 115 participants who had additional EEG data recorded after the first 2 weeks of treatment, use of these data increased the accuracy to 82.4% (sensitivity, 79.2%; specificity, 85.5%).

CONCLUSIONS AND RELEVANCE

These findings demonstrate the potential utility of EEG as a treatment planning tool for escitalopram therapy. Further development of the classification tools presented in this study holds the promise of expediting the search for optimal treatment for each patient.

Baseline Difference in Quantitative Electroencephalography Variables Between Responders and Non-Responders to Low-Frequency Repetitive Transcranial Magnetic Stimulation in Depression

Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depressive disorder, with outcomes approaching 45-55% response and 30-40% remission. Eligible predictors of treatment outcome, however, are still lacking. Few studies have investigated quantitative electroencephalography (QEEG) parameters as predictors of rTMS treatment outcome and none of them have addressed the source localization techniques to predict the response to low-frequency rTMS (LF rTMS). We investigated electrophysiological differences based on scalp EEG data and inverse solution method, exact low-resolution brain electromagnetic tomography (eLORETA), between responders and non-responders to LF rTMS in resting brain activity recorded prior to the treatment. Twenty-five unmedicated depressive patients (mean age of 45.7 years, 20 females) received a 4-week treatment of LF rTMS (1 Hz; 20 sessions per 600 pulses; 100% of the motor threshold) over the right dorsolateral prefrontal cortex. Comparisons between responders (≥50% reduction in Montgomery-Åsberg Depression Rating Scale score) and non-responders were made at baseline for measures of eLORETA current density, spectral absolute power, and inter-hemispheric and intra-hemispheric EEG asymmetry. Responders were found to have lower current source densities in the alpha-2 and beta-1 frequency bands bilaterally (with predominance on the left side) in the inferior, medial, and middle frontal gyrus, precentral gyrus, cingulate gyrus, anterior cingulate, and insula. The most pronounced difference was found in the left middle frontal gyrus for alpha-2 and beta-1 bands (p < 0.05). Using a spectral absolute power analysis, we found a negative correlation between the absolute power in beta and theta frequency bands on the left frontal electrode F7 and the change in depressive symptomatology. None of the selected asymmetries significantly differentiated responders from non-responders in any frequency band. Pre-treatment reduction of alpha-2 and beta-1 sources, but not QEEG asymmetry, was found in patients with major depressive disorder who responded to LF rTMS treatment. Prospective trials with larger groups of subjects are needed to further validate these findings.

Early change of prefrontal theta cordance and occipital alpha asymmetry in the prediction of responses to antidepressants

BACKGROUND

The study evaluated the effectiveness of EEG alpha 1, alpha 2 and theta power, along with prefrontal theta cordance (PFC), frontal and occipital alpha 1, alpha 2 asymmetry (FAA1/2, OAA1/2) at baseline and their changes at week 1 in predicting response to antidepressants.

METHOD

Resting-state EEG data were recorded from 103 depressive patients that were treated in average for 5.1 ± 0.9 weeks with SSRIs (n = 57) and SNRIs (n = 46).

RESULTS

Fifty-five percent of patients (n = 56) responded to treatment (i.e.reduction of Montgomery-Åsberg Depression Rating Scale score ≥ 50%) and 45% (n = 47) of treated subjects did not reach positive treatment outcome. No differences in EEG baseline alpha and theta power or changes at week 1 for prefrontal, frontal, central, temporal and occipital regions were found between responders and non-responders. Both groups showed no differences at baseline PFC, FAA1/2 and OAA1/2 as well as change of FAA1/2 at week 1. The only parameters associated with treatment outcome were decrease of PFC in responders and increase of OAA1/2 at week 1 in non-responders. There was no influence of the used antidepressant classes on the results. The PFC change at week 1 (PFCC) (area under curve-AUC = 0.75) showed only a numerically higher predictive ability than OAA change in alpha 1 (OAA1C, AUC = 0.64)/alpha 2 (OAA2C, AUC = 0.63). A combined model, where OAA1C was added to PFCC (AUC = 0.79), did not significantly improve response prediction.

CONCLUSION

Besides PFCC, we found that OAA1C/OAA2C might be another candidate for EEG predictors of antidepressant response.

A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder

INTRODUCTION

The heterogeneity of symptoms and complex etiology of depression pose a significant challenge to the personalization of treatment. Meanwhile, the current application of generic treatment approaches to patients with vastly differing biological and clinical profiles is far from optimal. Here, we conduct a meta-review to identify predictors of response to antidepressant therapy in order to select robust input features for machine learning models of treatment response. These machine learning models will allow us to learn associations between patient features and treatment response which have predictive value at the individual patient level; this learning can be optimized by selecting high-quality input features for the model. While current research is difficult to directly apply to the clinic, machine learning models built using knowledge gleaned from current research may become useful clinical tools.

METHODS

The EMBASE and MEDLINE/PubMed online databases were searched from January 1996 to August 2017, using a combination of MeSH terms and keywords to identify relevant literature reviews. We identified a total of 1909 articles, wherein 199 articles met our inclusion criteria.

RESULTS

An array of genetic, immune, endocrine, neuroimaging, sociodemographic, and symptom-based predictors of treatment response were extracted, varying widely in clinical utility.

LIMITATIONS

Due to heterogeneous sample sizes, effect sizes, publication biases, and methodological disparities across reviews, we could not accurately assess the strength and directionality of every predictor.

CONCLUSION

Notwithstanding our cautious interpretation of the results, we have identified a multitude of predictors that can be used to formulate a priori hypotheses regarding the input features for a computational model. We highlight the importance of large-scale research initiatives and clinically accessible biomarkers, as well as the need for replication studies of current findings. In addition, we provide recommendations for future improvement and standardization of research efforts in this field.

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

This study explores responses to ketamine in patients with treatment-resistant depression (TRD) using a wearable forehead electroencephalography (EEG) device. We recruited and randomly assigned 55 outpatients with TRD into three approximately equal-sized groups (A: 0.5-mg/kg ketamine; B: 0.2-mg/kg ketamine; and C: normal saline) under double-blind conditions. The ketamine responses were measured by EEG signals and Hamilton depression rating scale scores. At baseline, the responders showed significantly weaker EEG theta power than the non-responders (p < 0.05). Compared to the baseline, the responders exhibited higher EEG alpha power but lower EEG alpha asymmetry and theta cordance post-treatment (p < 0.05). Furthermore, our baseline EEG predictor classified the responders and non-responders with 81.3 ± 9.5% accuracy, 82.1 ± 8.6% sensitivity, and 91.9 ± 7.4% specificity. In conclusion, the rapid antidepressant effects of mixed doses of ketamine are associated with prefrontal EEG power, asymmetry, and cordance at baseline and early post-treatment changes. Prefrontal EEG patterns at baseline may serve as indicators of ketamine effects. Our randomized double-blind placebo-controlled study provides information regarding the clinical impacts on the potential targets underlying baseline identification and early changes from the effects of ketamine in patients with TRD.

Change in Quantitative EEG Theta Cordance as a Potential Predictor of Repetitive Transcranial Magnetic Stimulation Clinical Outcome in Major Depressive Disorder

Repetitive transcranial magnetic stimulation (rTMS) has demonstrated efficacy in major depressive disorder (MDD), although clinical outcome is variable. Change in the resting-state quantitative electroencephalogram (qEEG), particularly in theta cordance early in the course of treatment, has been linked to antidepressant medication outcomes but has not been examined extensively in clinical rTMS. This study examined change in theta cordance over the first week of clinical rTMS and sought to identify a biomarker that would predict outcome at the end of 6 weeks of treatment. Clinically stable outpatients (n = 18) received nonblinded rTMS treatment administered to the dorsolateral prefrontal cortex (DLPFC). Treatment parameters (site, intensity, number of pulses) were adjusted on an ongoing basis guided by changes in symptom severity rating scale scores. qEEGs were recorded at pretreatment baseline and after 1 week of left DLPFC (L-DLPFC) rTMS using a 21-channel dry-electrode headset. Analyses examined the association between week 1 regional changes in theta band (4-8 Hz) cordance, and week 6 patient- and physician-rated outcomes. Theta cordance change in the central brain region predicted percent change in Inventory of Depressive Symptomology-Self-Report (IDS-SR) score, and improvement versus nonimprovement on the Clinical Global Impression-Improvement Inventory (CGI-I) ( R² = .38, P = .007; and Nagelkerke R² = .78, P = .0001, respectively). The cordance biomarker remained significant when controlling for age, gender, and baseline severity. Treatment-emergent change in EEG theta cordance in the first week of rTMS may predict acute (6-week) treatment outcome in MDD. This oscillatory synchrony biomarker merits further study in independent samples.

The comparative effectiveness of electroencephalographic indices in predicting response to escitalopram therapy in depression: A pilot study

BACKGROUND

This study aims to compare the effectiveness of EEG frequency band activity including interhemispheric asymmetry and prefrontal theta cordance in predicting response to escitalopram therapy at 8-weeks post-treatment, in a multi-site initiative.

METHODS

Resting state 64-channel EEG data were recorded from 44 patients with a diagnosis of major depressive disorder (MDD) as part of a larger, multisite discovery study of biomarkers in antidepressant treatment response, conducted by the Canadian Biomarker Integration Network in Depression (CAN-BIND). Clinical response was measured at 8-weeks post-treatment as change from baseline Montgomery-Asberg Depression Rating Scale (MADRS) score of 50% or more. EEG measures were analyzed at (1) pre-treatment baseline (2) 2 weeks post-treatment and (3) as an ''early change" variable defined as change in EEG from baseline to 2 weeks post-treatment.

RESULTS

At baseline, treatment responders showed elevated absolute alpha power in the left hemisphere while non-responders showed the opposite. Responders further exhibited a cortical asymmetry in the parietal region. Groups also differed in pre-treatment relative delta power with responders showing greater power in the right hemisphere over the left while non-responders showed the opposite. At 2 weeks post-treatment, responders exhibited greater absolute beta power in the left hemisphere relative to the right and the opposite was noted for non-responders. A reverse pattern was noted for absolute and relative delta power at 2 weeks post-treatment. Responders exhibited early reductions in relative alpha power and early increments in relative theta power. Non-responders showed a significant early increase in prefrontal theta cordance.

CONCLUSIONS

Hemispheric asymmetries in the alpha and delta bands at baseline and at 2 weeks post-treatment have moderately strong predictive utility in predicting response to antidepressant treatment.

Neuroimaging Measures to Assess the Effectiveness of a Two-Day Marathon Group of Individuals with Early Developmental Trauma: A Pilot Study

Group therapy can be effective with individuals with developmental trauma who re-experience intense physiological traumatic distress and struggle with terror and despair. This modality can address the long-term ramifications of developmental trauma, including problems with perceptions, trust, emotional regulation, and loss of sense of self. Neuroimaging techniques can be combined with group therapy interventions as a way to empirically validate the effectiveness of group psychotherapy on brain structures and networks impacted by trauma. The neuroscience explaining overpowering traumatic responses and related emotions will be reviewed. Results of a pilot study combining group therapy with neuroimaging are presented.

The Antidepressant Treatment Response Index as a Predictor of Reboxetine Treatment Outcome in Major Depressive Disorder

Biomarkers to predict clinical outcomes early during the treatment of major depressive disorder (MDD) could reduce suffering and improve outcomes. A quantitative electroencephalogram (qEEG) biomarker, the Antidepressant Treatment Response (ATR) index, has been associated with outcomes of treatment with selective serotonin reuptake inhibitor antidepressants in patients with MDD. Here, we report the results of a post hoc analysis initiated to evaluate whether the ATR index may also be associated with reboxetine treatment outcome, given that its putative mechanism of action is via norepinephrine reuptake inhibition (NRI). Twenty-five adults with MDD underwent qEEG studies during open-label treatment with reboxetine at doses of 8 to 10 mg daily for 8 weeks. The ATR index calculated after 1 week of reboxetine treatment was significantly associated with overall Hamilton Depression Rating Scale (HAM-D) improvement at week 8 (r=0.605, P=.001), even after controlling for baseline depression severity (P=.002). The ATR index predicted response (≥50% reduction in HAM-D) with 70.6% sensitivity and 87.5% specificity, and remission (final HAM-D≤7) with 87.5% sensitivity and 64.7% specificity. These results suggest that the ATR index may be a useful biomarker of clinical response during NRI treatment of adults with MDD. Future studies are warranted to investigate further the potential utility of the ATR index as a predictor of noradrenergic antidepressant treatment response.

QEEG Theta Cordance in the Prediction of Treatment Outcome to Prefrontal Repetitive Transcranial Magnetic Stimulation or Venlafaxine ER in Patients With Major Depressive Disorder

The aims of this double-blind study were to assess and compare the efficacy of quantitative electroencephalographic (QEEG) prefrontal theta band cordance in the prediction of response to 4-week, right, prefrontal, 1-Hz repetitive transcranial magnetic stimulation (rTMS) or venlafaxine ER in patients with major depressive disorder (MDD). Prefrontal QEEG cordance values of 50 inpatients (25 subjects in each group) completing 4 weeks of the study were obtained at baseline and after 1 week of treatment. Depressive symptoms were assessed using Montgomery-Åsberg Depression Rating Scale (MADRS) at baseline and at week 1 and 4. Treatment response was defined as a ≥50% reduction in baseline MADRS total score. All responders (n = 9) and 6 of 16 nonresponders in the rTMS group had reduced cordance at week 1 (P < .01). Reduction of theta cordance value at week 1 was detected in all responders (n = 10) to venlafaxine ER, but only in 4 of 15 nonresponders (P = .005). The comparison of the areas under the curve of cordance change for prediction of response between rTMS (0.75) and venlafaxine ER (0.89) treated groups yielded no significant difference (P = .27). Our study indicates that prefrontal QEEG cordance is a promising tool not only for predicting the response to certain antidepressants but also to rTMS treatment, with comparable predictive efficacy for both therapeutic interventions.

The effectiveness of prefrontal theta cordance and early reduction of depressive symptoms in the prediction of antidepressant treatment outcome in patients with resistant depression: analysis of naturalistic data

Current studies suggest that an early improvement of depressive symptoms and the reduction of prefrontal theta cordance value predict the subsequent response to antidepressants. The aim of our study was (1) to compare the predictive abilities of early clinical improvement defined as ≥ 20 % reduction in Montgomery and Åsberg Depression Rating Scale (MADRS) total score at week 1 and 2, and the decrease of prefrontal theta cordance at week 1 in resistant depressive patients and (2) to assess whether the combination of individual predictors yields more robust predictive power than either predictor alone. Eighty-seven subjects were treated (≥ 4 weeks) with various antidepressants chosen according to the judgment of attending psychiatrists. Areas under curve (AUC) were calculated to compare predictive effect of defined single predictors (≥ 20 % reduction in MADRS total score at week 1 and 2, and the decrease of cordance at week 1) and combined prediction models. AUCs of all three predictors were not statistically different (pair-wise comparison). The model combining all predictors yielded an AUC value 0.91 that was significantly higher than AUCs of each individual predictor. The results indicate that the combined predictor model may be a useful and clinically meaningful tool for the prediction of antidepressant response in patients with resistant depression.

Analysis of Brain Functional Changes in High-Frequency Repetitive Transcranial Magnetic Stimulation in Treatment-Resistant Depression

Repetitive transcranial magnetic stimulation (rTMS) is a treatment procedure that uses magnetic fields to stimulate nerve cells in the brain, and is associated with significant improvements in clinical symptoms of major depressive disorder (MDD). The effect of rTMS treatment on the brain can be evaluated by cordance, a quantitative electroencephalography (QEEG) method that extracts information from absolute and relative power of EEG spectra. In this study, to analyze brain functional changes, pre- and post-rTMS, QEEG data were collected from 6 frontal electrodes (Fp1, Fp2, F3, F4, F7, and F8) in 2 slow bands (delta and theta) for 55 MDD subjects. To examine brain changes, cordance scores were determined, using repeated-measures analysis of variance (ANOVA). High-frequency rTMS was associated with cordance decrease in left frontal and right prefrontal regions in both delta and theta for nonresponders; it was associated with cordance increase in all right and left frontal electrodes, except F8, for responders.

Quantitative EEG and neurofeedback in children and adolescents: anxiety disorders, depressive disorders, comorbid addiction and attention-deficit/hyperactivity disorder, and brain injury

This article explores the science surrounding neurofeedback. Both surface neurofeedback (using 2-4 electrodes) and newer interventions, such as real-time z-score neurofeedback (electroencephalogram [EEG] biofeedback) and low-resolution electromagnetic tomography neurofeedback, are reviewed. The limited literature on neurofeedback research in children and adolescents is discussed regarding treatment of anxiety, mood, addiction (with comorbid attention-deficit/hyperactivity disorder), and traumatic brain injury. Future potential applications, the use of quantitative EEG for determining which patients will be responsive to medications, the role of randomized controlled studies in neurofeedback research, and sensible clinical guidelines are considered.

Pretreatment anterior cingulate activity predicts antidepressant treatment response in major depressive episodes

Major depressive disorder leads to substantial individual and socioeconomic costs. Despite the ongoing efforts to improve the treatment for this condition, a trial-and-error approach until an individually effective treatment is established still dominates clinical practice. Searching for clinically useful treatment response predictors is one of the most promising strategies to change this quandary therapeutic situation. This study evaluated the predictive value of a biological and a clinical predictor, as well as a combination of both. Pretreatment EEGs of 31 patients with a major depressive episode were analyzed with neuroelectric brain imaging technique to assess cerebral oscillations related to treatment response. Early improvement of symptoms served as a clinical predictor. Treatment response was assessed after 4 weeks of antidepressant treatment. Responders showed more slow-frequency power in the right anterior cingulate cortex compared to non-responders. Slow-frequency power in this region was found to predict response with good sensitivity (82 %) and specificity (100 %), while early improvement showed lower accuracy (73 % sensitivity and 65 % specificity). Combining both parameters did not further improve predictive accuracy. Pretreatment activity within the anterior cingulate cortex is related to antidepressive treatment response. Our results support the search for biological treatment response predictors using electric brain activity. This technique is advantageous due to its low individual and socioeconomic burden. The benefits of combining both, a clinically and a biologically based predictor, should be further evaluated using larger sample sizes.

Is EEG a useful test in adult psychiatry?

BACKGROUND

We present a brief overview of the use of EEG in psychiatry, with particular emphasis on differential diagnosis examination in case of acute psychiatric conditions.

METHOD

The article is based on a literature search in PubMed and the authors' own collections of articles and personal experience.

RESULTS

Onset of epilepsy, encephalitis or other cerebral diseases may be accompanied by psychiatric or cognitive symptoms. Slow EEG activity may be an unspecific sign of cerebral disease. Psychiatric patients are also at an increased risk of epilepsy. In case of seizure symptoms such as convulsions or conditions with rapid changes in affective states, epileptiform activity during EEG is a specific sign of epileptic aetiology or comorbidity. Quantitative frequency analysis (QEEG) is useful for research purposes and in exceptional cases also in a clinical context. No QEEG method has as yet become accepted as providing reliable, independent markers for psychiatric disease or treatment response.

INTERPRETATION

EEG should be undertaken in case of newly occurring psychoses and for conditions with a fluctuating or progradiating loss of cognitive function. Adult psychiatric patients with seizure symptoms or rapid changes in affective states should also be referred to EEG.

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.

Multiweek resting EEG cordance change patterns from repeated olfactory activation with two constitutionally salient homeopathic remedies in healthy young adults

OBJECTIVES

Electroencephalography (EEG) offers psychophysiologic tools to improve sensitivity for detecting objective effects in complementary and alternative medicine. This current investigation extended prior clinical research studies to evaluate effects of one of two different homeopathic remedies on resting EEG cordance after an olfactory activation protocol on healthy young adults with remedy-relevant, self-perceived characteristics.

METHODS

Ninety-seven (7) young adults (N=97, mean age 19 years, 55% women) with good self-rated global health and screened for homeopathic constitutional types consistent with one of two remedies (either Sulphur or Pulsatilla) underwent three weekly laboratory sessions. At each visit, subjects had 5-minute resting, eyes-closed EEG recordings before and after a placebo-controlled olfactory activation task with their constitutionally relevant verum remedy. One remedy potency (6c, 12c, or 30c) used per week, was presented in a randomized order over the 3 sessions. Prefrontal resting EEG cordance values at Fp1 and Fp2 were computed from artifact-free 2-minute EEG samples from the presniffing and postsniffing rest periods. Cordance derives from an algorithm that incorporates absolute and relative EEG values.

RESULTS

The data showed significant two-way oscillatory interactions of remedy by time for ß, α, θ, and δ cordance, controlling for gender and chemical sensitivity.

CONCLUSIONS

EEG cordance provided a minimally invasive technique for assessing objective nonlinear physiologic effects of two different homeopathic remedies salient to the individuals who received them. Time factors modulated the direction of effects. Given previous evidence of correlations between cordance and single-photon emission computed tomography, these findings encourage additional neuroimaging research on nonlinear psychophysiologic effects of specific homeopathic remedies.

Hyperstable regulation of vigilance in patients with major depressive disorder

OBJECTIVES

This study tested the hypothesis that patients with depression show less and later declines into lower EEG vigilance stages (different global functional brain states) under resting conditions than healthy controls, as proposed by the vigilance theory of affective disorders.

METHODS

Thirty patients with Major Depressive Disorder (19 female; mean age: 37.2 years, SD: 12.6) without psychotropic medication and 30 carefully age- and sex-matched controls (19 female; mean age: 37.3 years, SD: 12.8) without past or present mental disorders underwent a 15-min resting EEG. EEG-vigilance regulation was determined with a computer-based vigilance classification algorithm (VIGALL, Vigilance Algorithm Leipzig), allowing a classification of vigilance stages A (with substages A1, A2 and A3), B (with substages B1 and B2/3) and C.

RESULTS

Depressive patients spent significantly more time in the highest EEG vigilance substage A1, and less time in substages A2, A3 and B2/3 than controls. In depressive patients, a significantly longer latency until the occurrence of substages A2, A3 and B2/3 was observed. No significant group differences in the percentage of B1 segments or the latency until occurrence of B1 were found.

CONCLUSIONS

The results confirm the hypothesis that patients with depression show less (and later) declines into lower EEG vigilance stages under resting conditions than healthy controls, and support the vigilance theory of affective disorders linking a hyperstable vigilance regulation to depression.

Surrogate markers of treatment outcome in major depressive disorder

Major depressive disorder (MDD) is a common medical illness affecting millions worldwide. Despite their widespread use since the 1950s and 1960s, the 'downstream' mechanism by which antidepressants ultimately exert their therapeutic effects remains elusive. In addition, except for a few exceptions such as episode severity and the presence of comorbid Axis-I or Axis-III disorders, biological or clinical characteristics which can accurately quantify the risk of poor treatment outcome are lacking, as are factors which could help patients and clinicians select treatment options that would result in superior outcome. The identification of such markers, termed 'surrogate' markers, could help shed further insights into what constitutes illness and recovery, help identify molecular targets for the development of future antidepressants, and lead the way to the design and refinement of a personalized medicine treatment model for MDD. In the following text, several major areas ('leads') where evidence exists regarding the presence of surrogate markers of efficacy outcome in MDD will be briefly reviewed. Leads include evidence from the role of demographic and clinical factors as surrogate markers, to the role of various biological markers including genotype, brain functional imaging, electroencephalography, dichotic listening, and molecular biology and immunology. The purpose of this work is to focus selectively on areas where there have been findings, as opposed to conducting an exhaustive literature review of studies which have failed to yield any significant breakthrough in our knowledge.

The antidepressant treatment response index and treatment outcomes in a placebo-controlled trial of fluoxetine

Recent research aims at developing a biomarker to predict antidepressant treatment outcomes in major depressive disorder. The Antidepressant Treatment Response (ATR) index has been correlated with response to antidepressant medication (, ) but has not been assessed in a placebo-controlled trial. EEGs recorded at pretreatment baseline and after 1 week of randomized treatment were used to calculate ATR index for 23 subjects with major depressive disorder who were treated for 8 weeks with fluoxetine (FLX) 20 mg (n = 12) or placebo (n = 11). The 17-item Hamilton Depression Rating Scale (HamD17) assessed symptom severity; ATR index was assessed as a predictor of percent change in HamD17 score, endpoint response (≥ 50% improvement) and remission (HamD17 score ≤ 7). The ATR index was significantly associated with improvement on FLX (r = 0.64, P = 0.01), with a higher mean ATR index for FLX responders than for nonresponders (t(10) = -2.07, P = 0.03). Receiver operating characteristic analysis found a 0.83 area under the curve (P = 0.03), for ATR index as a predictor for FLX, while an optimized ATR index cutoff of 47.3 yielded 100% sensitivity, 66.7% specificity, 75% positive predictive value, and 100% negative predictive value. Importantly, ATR index did not correlate significantly with placebo outcomes. Results extend ATR index findings to include predictive validity with fluoxetine, suggesting that this biomarker has specificity for drug effects.

The implication of functional connectivity strength in predicting treatment response of major depressive disorder: a resting EEG study

Predicting treatment response in major depressive disorder (MDD) has been an important clinical issue given that the initial intent-to-treat response rate is only 50 to 60%. This study was designed to examine whether functional connectivity strengths of resting EEG could be potential biomarkers in predicting treatment response at 8 weeks of treatment. Resting state 3-min eyes-closed EEG activity was recorded at baseline and compared in 108 depressed patients. All patients were being treated with selective serotonin-reuptake inhibitors. Baseline coherence and power series correlation were compared between responders and non-responders evaluated at the 8th week by Hamilton Depression Rating Scale. Pearson correlation and receiver operating characteristic (ROC) analyses were applied to evaluate the performance of connectivity strengths in predicting/classifying treatment responses. The connectivity strengths of right fronto-temporal network at delta/theta frequencies differentiated responders and non-responders at the 8th week of treatment, such that the stronger the connectivity strengths, the poorer the treatment response. ROC analyses supported the value of these measures in classifying responders/non-responders. Our results suggest that fronto-temporal connectivity strengths could be potential biomarkers to differentiate responders and slow responders or non-responders in MDD.

Clinical trial design in non-invasive brain stimulation psychiatric research

Major depressive disorder (MDD) trials - investigating either non-pharmacological or pharmacological interventions - have shown mixed results. Many reasons explain this heterogeneity, but one that stands out is the trial design due to specific challenges in the field. We aimed therefore to review the methodology of non-invasive brain stimulation (NIBS) trials and provide a framework to improve clinical trial design. We performed a systematic review for randomized, controlled MDD trials whose intervention was transcranial magnetic stimulation (rTMS) or transcranial direct current stimulation (tDCS) in MEDLINE and other databases from April 2002 to April 2008. We created an unstructured checklist based on CONSORT guidelines to extract items such as power analysis, sham method, blinding assessment, allocation concealment, operational criteria used for MDD, definition of refractory depression and primary study hypotheses. Thirty-one studies were included. We found that the main methodological issues can be divided in to three groups: (1) issues related to phase II/small trials, (2) issues related to MDD trials and, (3) specific issues of NIBS studies. Taken together, they can threaten study validity and lead to inconclusive results. Feasible solutions include: estimating the sample size a priori; measuring the degree of refractoriness of the subjects; specifying the primary hypothesis and statistical tests; controlling predictor variables through stratification randomization methods or using strict eligibility criteria; adjusting the study design to the target population; using adaptive designs and exploring NIBS efficacy employing biological markers. In conclusion, our study summarizes the main methodological issues of NIBS trials and proposes a number of alternatives to manage them.

Using pre-treatment EEG data to predict response to SSRI treatment for MDD

The problem of identifying in advance the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we propose a machine learning (ML) methodology to predict the response to a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD), using pre-treatment electroencephalograph (EEG) measurements. The proposed feature selection technique is a modification of the method of Peng et al [10] that is based on a Kullback-Leibler (KL) distance measure. The classifier was realized as a kernelized partial least squares regression procedure, whose output is the predicted response. A low-dimensional kernelized principal component representation of the feature space was used for the purposes of visualization and clustering analysis. The overall method was evaluated using an 11-fold nested cross-validation procedure for which over 85% average prediction performance is obtained. The results indicate that ML methods hold considerable promise in predicting the efficacy of SSRI antidepressant therapy for major depression.

Treatment results of anorexia nervosa and bulimia nervosa in a residential treatment program

Data on the effectiveness of residential treatment for patients with anorexia nervosa (AN) and bulimia nervosa (BN) are limited. We analyzed patient survey results at admission and discharge from Monte Nido Residential Treatment Program. Of 287 consecutive admissions, 80% (231) "graduated" (completed ≥ 30 days of treatment), and of these (all of whom gave consent), only patients with AN (N = 120) or BN (N = 95) were included (215 of 231, 93%) in this study. Analyses included a comparison of admission vs. discharge variables (paired t-tests) for each diagnosis. At each assessment, graduates completed the Eating Disorders Inventory-2 (EDI-2), the Beck Depression Inventory (BDI), and a structured eating disorder assessment questionnaire. For patients with AN, there were statistically significant improvements in mean BMI. In addition, for both AN and BN patients, there were statistically significant improvements in BDI scores, all 11 EDI-2 subscales, and frequencies of bingeing, vomiting, laxative abuse, chewing and spitting, stimulant abuse, and restricting behavior. The great majority of patients completing treatment showed significant improvement at discharge from intensive residential treatment.

Brain functional changes (QEEG cordance) and worsening suicidal ideation and mood symptoms during antidepressant treatment

OBJECTIVE

Antidepressant medications are efficacious overall; however, some individuals experience worsening mood symptoms and increased suicidal ideation (SI) during treatment. We examined the quantitative electroencephalographic (QEEG) cordance biomarker of brain function biomarker in relation to treatment-emergent symptom worsening.

METHOD

Seventy-two major depressive disorder (MDD) subjects were treated with fluoxetine 20 mg (n = 13), venlafaxine 150 mg (n = 24), or placebo (n = 35) under double-blind conditions. Behavioral ratings determined whether each subject demonstrated worsening of depressed mood, anxiety, or SI during treatment. QEEG cordance data were analyzed to determine whether symptom worsening was associated with neurophysiological changes.

RESULTS

Antidepressant treatment-emergent SI (13.5%) was associated with a large transient decrease in midline-and-right-frontal (MRF) cordance 48 h after start of medication.

CONCLUSION

Hypothesis-generating results suggest a pattern of functional changes in midline and right frontal brain regions associated with antidepressant treatment-emergent SI in MDD.

Subanesthetic dose of ketamine decreases prefrontal theta cordance in healthy volunteers: implications for antidepressant effect

BACKGROUND

Theta cordance is a novel quantitative electroencephalography (QEEG) measure that correlates with cerebral perfusion. A series of clinical studies has demonstrated that the prefrontal theta cordance value decreases after 1 week of treatment in responders to antidepressants and that this effect precedes clinical improvement. Ketamine, a non-competitive antagonist of N-methyl-D-aspartate (NMDA) receptors, has a unique rapid antidepressant effect but its influence on theta cordance is unknown.

METHOD

In a double-blind, cross-over, placebo-controlled experiment we studied the acute effect of ketamine (0.54 mg/kg within 30 min) on theta cordance in a group of 20 healthy volunteers.

RESULTS

Ketamine infusion induced a decrease in prefrontal theta cordance and an increase in the central region theta cordance after 10 and 30 min. The change in prefrontal theta cordance correlated with ketamine and norketamine blood levels after 10 min of ketamine infusion.

CONCLUSIONS

Our data indicate that ketamine infusion immediately induces changes similar to those that monoamineric-based antidepressants induce gradually. The reduction in theta cordance could be a marker and a predictor of the fast-acting antidepressant effect of ketamine, a hypothesis that could be tested in depressive patients treated with ketamine.

The change of prefrontal QEEG theta cordance as a predictor of response to bupropion treatment in patients who had failed to respond to previous antidepressant treatments

The aim of the study was to examine whether the reduction of theta prefrontal quantitative EEG (QEEG) cordance after one week of bupropion administration is a predictor of response to a 4-week treatment in patients that had failed to respond to previous antidepressant treatments.

METHOD

EEG data of 18 inpatients were monitored at baseline and after one week. QEEG cordance was computed at 3 frontal electrodes (Fp1, Fp2, Fz). Response to treatment was defined as a >/=50% reduction of MADRS score.

RESULTS

Nine of the eleven responders and one of the seven non-responders showed decreased prefrontal cordance value after the first week of treatment (p=0.01). Positive and negative predictive values of cordance reduction for the prediction of response to the treatment were 0.9 and 0.75, respectively.

CONCLUSION

Similar to other antidepressants, the reduction of prefrontal QEEG cordance might be helpful in the prediction of the acute outcome of bupropion treatment.

Antidepressant response trajectories and quantitative electroencephalography (QEEG) biomarkers in major depressive disorder

Individuals with Major Depressive Disorder (MDD) vary regarding the rate, magnitude and stability of symptom changes during antidepressant treatment. Growth mixture modeling (GMM) can be used to identify patterns of change in symptom severity over time. Quantitative electroencephalographic (QEEG) cordance within the first week of treatment has been associated with endpoint clinical outcomes but has not been examined in relation to patterns of symptom change. Ninety-four adults with MDD were randomized to eight weeks of double-blinded treatment with fluoxetine 20mg or venlafaxine 150mg (n=49) or placebo (n=45). An exploratory random effect GMM was applied to Hamilton Depression Rating Scale (Ham-D(17)) scores over 11 timepoints. Linear mixed models examined 48-h, and 1-week changes in QEEG midline-and-right-frontal (MRF) cordance for subjects in the GMM trajectory classes. Among medication subjects an estimated 62% of subjects were classified as responders, 21% as non-responders, and 17% as symptomatically volatile-i.e., showing a course of alternating improvement and worsening. MRF cordance showed a significant class-by-time interaction (F((2,41))=6.82, p=.003); as hypothesized, the responders showed a significantly greater 1-week decrease in cordance as compared to non-responders (mean difference=-.76, Std. Error=.34, df=73, p=.03) but not volatile subjects. Subjects with a volatile course of symptom change may merit special clinical consideration and, from a research perspective, may confound the interpretation of typical binary endpoint outcomes. Statistical methods such as GMM are needed to identify clinically relevant symptom response trajectories.

Effect of a psychoneurotherapy on brain electromagnetic tomography in individuals with major depressive disorder

Recent advances in power spectral analysis of electroencephalography (EEG) signals and brain-computer interface (BCI) technology may significantly contribute to the development of psychoneurotherapies. The goal of this study was to measure the effect of a psychoneurotherapy on brain source generators of abnormal EEG activity in individuals with major depressive disorder (MDD). Thirty participants with unipolar MDD were recruited in the community. The proposed psychoneurotherapy was developed based on the relationship between the localization of abnormal EEG activity and depressive symptomatology. Brain electromagnetic abnormalities in MDD were identified with low resolution brain electromagnetic tomography (LORETA) and a normative EEG database. Localization of brain changes after treatment was assessed through the standardized version of LORETA (sLORETA). Before treatment, excessive high-beta (18-30 Hz) activity was noted in several brain regions located in the fronto-temporal regions. After treatment, only participants who successfully normalized EEG activity in cortico-limbic/paralimbic regions could be considered in clinical remission. In these regions, significant correlations were found between the percentage of change of depressive symptoms and the percentage of reduction in high-beta activity. These results suggest that the normalization of high-beta activity in cortico-limbic/paralimbic regions can be associated with a significant reduction of depressive symptoms.

The clinical use of quantitative EEG in cognitive disorders

The primary diagnosis of most cognitive disorders is clinically based, but the EEG plays a role in evaluating, classifying and following some of these disorders. There is an ongoing debate over routine use of qEEG. Although many findings regarding the clinical use of quantitative EEG are awaiting validation by independent investigators while confirmatory clinical follow-up studies are also needed, qEEG can be cautiously used by a skilled neurophysiologist in cognitive dysfunctions to improve the analysis of background activity, slow/fast focal activity, subtle asymmetries, spikes and waves, as well as in longitudinal follow-ups.

Brain functional changes and duloxetine treatment response in fibromyalgia: a pilot study

OBJECTIVES

Serotonin-norepinephrine reuptake inhibitor (SNRI) antidepressant medications may have efficacy in relieving pain associated with fibromyalgia syndrome (FMS), even in the absence of major depressive disorder (MDD). Current practice is to use a trial-and-error treatment strategy, often requiring 8-12 weeks to determine the effectiveness of a given pharmacological intervention. The ability to predict response to antidepressant medications would facilitate clinical management of FMS. Prior work in MDD has shown that the quantitative electroencephalographic (QEEG) cordance biomarker of brain functional changes early in the course of antidepressant treatment is related to later clinical response. We hypothesized that cordance might also predict response to antidepressant medications for symptoms of FMS.

DESIGN

Twelve adults (9 females) meeting American College of Rheumatology criteria for FMS participated in a double-blind placebo-controlled treatment trial utilizing duloxetine 60 mg. QEEG cordance changes were examined over the first week of treatment. Primary clinical outcomes included change in average pain severity on the Brief Pain Inventory (BPI) and global improvement in pain on the Patient's Global Impressions of Improvement (PGI-I) scale at 12 weeks.

RESULTS

Changes in left frontal QEEG cordance after the first week of duloxetine treatment significantly predicted BPI pain improvement (regression coefficient = 2.9, R(2) = 0.93, P = 0.008) and PGI-I global improvement (regression coefficient = 0.94, R(2) = 0.81, P = 0.04).

CONCLUSIONS

This pilot study suggests that QEEG biomarkers may prove useful for predicting improvement in painful symptoms during SNRI treatment in FMS. Larger studies are needed to confirm this finding.

Brain functional changes during placebo lead-in and changes in specific symptoms during pharmacotherapy for major depression

OBJECTIVE

Brain functional changes during placebo lead-in have been associated with antidepressant response in clinical trials for major depressive disorder (MDD); however, the relationship between such non-pharmacodynamic changes in brain function and changes in specific symptoms is unknown.

METHOD

Fifty-eight adults with MDD completed a 1-week single-blind placebo lead-in preceding 8 weeks of double-blind randomized treatment with fluoxetine or venlafaxine (n = 30) or placebo (n = 28). Brain functional change during lead-in was assessed using quantitative electroencephalographic (qEEG) prefrontal theta-band cordance. Symptoms were assessed using the Symptom Checklist-90-Revised (SCL-90-R).

RESULTS

The multiple regression model examining the qEEG parameter in relation to SCL-90-R subscales was significant [F(9,9) = 4.27, P = 0.021, R(2) = 0.81] in females, with a significant association for the interpersonal sensitivity subscale (beta coefficient = 1.94, P = 0.001).

CONCLUSION

Prefrontal neurophysiologic change during placebo lead-in may indicate subsequent antidepressant-related improvement in symptoms of interpersonal sensitivity.

Use of clinical neurophysiology for the selection of medication in the treatment of major depressive disorder: the state of the evidence

Approximately 50% of patients with Major Depressive Disorder (MDD) respond to the first antidepressant medication prescribed, and fewer than one-third experience remission of symptoms. The most significant challenge in the management of MDD, therefore, is selection of the antidepressant medication that is most likely to lead to response or to remission for an individual patient. There is a growing body of evidence that certain clinical neurophysiologic techniques may be useful for selecting the medication that is most likely benefit each patient. Use of low resolution electromagnetic tomography (LORETA), loudness dependent auditory evoked potentials (LDAEP), and resting state quantitative electroencephalography (QEEG) in the clinical setting is increasingly supported by studies indicating that these techniques may help identify particular medications that are most likely to lead to response or remission. The current state of evidence supporting the use of each technique is reviewed.

Predictors, moderators, and mediators (correlates) of treatment outcome in major depressive disorder

Major Depressive Disorder (MDD) is a prevalent illness that is frequently associated with significant disability, morbidity and mortality. Despite the development and availability of numerous treatment options for MDD, studies have shown that antidepressant monotherapy yields only modest rates of response and remission. Clearly, there is an urgent need to develop more effective treatment strategies for patients with MDD, One possible approach towards the development of novel pharmacotherapeuiic strategies for MDD involves identifying subpopulations of depressed patients who are more likely to experience the benefits of a given (existing) treatment versus placebo, or versus a second treatment. Attempts have been made to identify such “subpopulations, ” specifically by testing whether a given biological or clinical marker also serves as a moderator, mediator (correlate), or predictor of clinical improvement following the treatment of MDD with standard, first-line antidepressants. In the following article, we will attempt to summarize the literature focusing on several major areas (“leads”) where preliminary evidence exists regarding clinical and biologic moderators, mediators, and predictors of symptom improvement in MDD, Such clinical leads will include the presence of hopelessness, anxious symptoms, or medical comorbidity. Biologic leads will include gene polymorphisms, brain metabolism, quantitative electroencephalography, loudness dependence of auditory evoked potentials, and functional brain asymmetry

Toward achieving optimal response: understanding and managing antidepressant side effects

The safety and tolerability of antidepressants have improved considerably over the past two decades. Nevertheless, antidepressant side effects are still common and problematic. The majority of patients treated with contemporaty agents experience one or more bothersome side effects. These side effects often create barriers to achieving depressive remission, as well as to preventing relapse and recurrence. Clinicians tend to underestimate the prevalence of side effects, and as many as one quarter of patients discontinue their antidepressants because of difficult-to-tolerate side effects; others may continue on antidepressant therapy but experience diminished quality of life related to troublesome side effects. This article reviews the prevalence of side effects, the impact of side effects on treatment adherence, and methodological issues including the challenge of distinguishing side effects from residual depressive symptoms, discontinuation effects, and general medical problems. In addition, we address the most common side effects such as sexual dysfunction, gastrointestinal problems, sleep disturbance, apathy and fatigue, and offer strategies for management that may help patients achieve optimal response to pharmacotherapy

Clinical characteristics and treatment outcome in a representative sample of depressed inpatients - findings from the Munich Antidepressant Response Signature (MARS) project

Depression is a common and often difficult-to-treat clinical condition with a high rate of patients showing insufficient treatment response and persistence of symptoms. We report the characteristics of a representative sample of depressed inpatients participating in the Munich Antidepressant Response Signature (MARS) project. Eight hundred and forty-two inpatients admitted to a psychiatric hospital for treatment of a major depressive episode, recurrent or bipolar depression were thoroughly characterized with respect to demographic factors, clinical history, and the degree of HPA-axis dysregulation evaluated by means of combined dex/CRH tests, and the predictive value of these factors for treatment outcome is investigated. 80.8% of patients responded to treatment (i.e., improvement in symptom severity of at least 50%) and 57.9% reached remission (i.e., near absence of residual depressive symptoms) at discharge after a mean treatment period of 11.8 weeks. Regression analysis identified early partial response (within 2 weeks) as the most important positive predictor for achieving remission. Previous ineffective treatment trials in the current episode and presence of a migration background are potent negative predictors for treatment outcome. In addition, remitters were characterized by a more pronounced normalization of an initially dysregulated HPA-axis. We could show that a large majority of inpatients suffering from depression benefits from antidepressant treatment during hospitalization. However, a considerable number of patients failed to achieve remission. We demonstrated that this subgroup can be characterized by a set of demographic, clinical and neuroendocrine variables allowing to predict unfavorable outcome at an early stage of treatment.