Changes in QEEG prefrontal cordance as a predictor of response to antidepressants in patients with treatment resistant depressive disorder: a pilot study

Meta-analysis on QEEG Changes to Antidepressant Treatment Among Patients with Depression

Introduction

Diagnostic and treatment accuracy of depression can lead to a better and possibly earlier response and remission in patients. The literature, though scanty, seems to suggest that quantitative electroencephalography (QEEG) can predict the outcome of antidepressant effects.

Methodology

Articles published between January 1990 and July 2019, including those dealing with QEEG recordings before and after the initiation of antidepressant medication, were included. The pooled effect size and subgroup analysis of waveforms were calculated to predict response to antidepressants.

Result

In all, 572 results were retrieved from the searches, of which 20 studies were included. Pooled data using a random-effects model (REM) calculated an effect size of 0.80 (95% CI [0.64-0.97]). Heterogeneity of the sample was low with Tau² = 0.02; df = 18 (P = .30); I² = 12%. Moreover, subgroup analysis showed that theta band frequencies were better at predicting response than alpha band frequencies (the standard mean difference [SMD] for theta was 0.91 compared to 0.68 for alpha waves).

Conclusions

QEEG is a valuable predictor of the antidepressant response. Among the EEG frequencies, the theta band showed the most significant change with treatment.

Neurophysiological and neuroimaging markers of repetitive transcranial magnetic stimulation treatment response in major depressive disorder: A systematic review and meta-analysis of predictive modeling studies

Predicting repetitive transcranial magnetic stimulation (rTMS) treatment outcomes in major depressive disorder (MDD) could reduce the financial and psychological risks of treatment failure. We systematically reviewed and meta-analyzed studies that leveraged neurophysiological and neuroimaging markers to predict rTMS response in MDD. Five databases were searched from inception to May 25, 2023. The primary meta-analytic outcome was predictive accuracy pooled from classification models. Regression models were summarized qualitatively. A promising marker was identified if it showed a sensitivity and specificity of 80% or higher in at least two independent studies. Searching yielded 36 studies. Twenty-two classification modeling studies produced an estimated area under the summary receiver operating characteristic curve of 0.87 (95% CI = 0.83-0.92), with 86.8% sensitivity (95% CI = 80.6-91.2%) and 81.9% specificity (95% CI = 76.1-86.4%). Frontal theta cordance measured by electroencephalography is closest to proof of concept. Predicting rTMS response using neurophysiological and neuroimaging markers is promising for clinical decision-making. However, replications by different research groups are needed to establish rigorous markers.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Resting-state EEG delta and alpha power predict response to cognitive behavioral therapy in depression: a Canadian biomarker integration network for depression study

Cognitive behavioral therapy (CBT) is often recommended as a first-line treatment in depression. However, access to CBT remains limited, and up to 50% of patients do not benefit from this therapy. Identifying biomarkers that can predict which patients will respond to CBT may assist in designing optimal treatment allocation strategies. In a Canadian Biomarker Integration Network for Depression (CAN-BIND) study, forty-one adults with depression were recruited to undergo a 16-week course of CBT with thirty having resting-state electroencephalography (EEG) recorded at baseline and week 2 of therapy. Successful clinical response to CBT was defined as a 50% or greater reduction in Montgomery-Åsberg Depression Rating Scale (MADRS) score from baseline to post-treatment completion. EEG relative power spectral measures were analyzed at baseline, week 2, and as early changes from baseline to week 2. At baseline, lower relative delta (0.5-4 Hz) power was observed in responders. This difference was predictive of successful clinical response to CBT. Furthermore, responders exhibited an early increase in relative delta power and a decrease in relative alpha (8-12 Hz) power compared to non-responders. These changes were also found to be good predictors of response to the therapy. These findings showed the potential utility of resting-state EEG in predicting CBT outcomes. They also further reinforce the promise of an EEG-based clinical decision-making tool to support treatment decisions for each patient.

Low global frontal brain activity is associated with non-planned or impulsive suicide attempts. A preliminary study

BACKGROUND

Suicide prevention is limited by the frequent non-planned or impulsive nature of suicidal behavior. For instance, 25-62 % of suicide attempts, occur within 30 min of the onset of suicidal ideation. We aimed to examine frontal brain activity in depressed patients following a suicide attempt and its relationship with the duration of the suicidal process.

METHODS

We recruited 35 adult patients within three days of a suicide attempt of at least moderate lethality. Duration of the suicidal process was recorded in a semi-structured interview, including suicide contemplation (time from onset of suicidal ideation to decision to kill oneself) and suicide action intervals (time from the decision to kill oneself to suicide attempt). Resting state EEG data from AF7, AF8, TP9 and TP10 leads was collected with a portable MUSE 2 headband system. The average frequency values throughout a 5-minute portable EEG recording were extracted for delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) waves.

RESULTS

Delta (r = 0.450, p = 0.021) and theta power (r = 0.395, p = 0.044) were positively correlated with the duration of the suicide action interval. There were no significant correlations of the suicide contemplation interval with clinical or EEG measures. Patients with suicide action interval shorter than 30 min showed lower delta power (U = 113, p = 0.049) compared with those with longer duration.

CONCLUSIONS

Lower theta and delta activity may reflect hindered cognitive control and inhibition in impulsive suicide attempters. Portable EEG may provide a valuable tool for clinical research and in the management of acutely suicidal patients.

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.

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

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

CLINICAL TRIAL REGISTRATION

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

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.

[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.

Objective Assessments in Diagnoses and Treatment: A Proposed Change in Paradigm

For patients with psychiatric disorders, current diagnostic and treatment approaches are far from optimal. The clinical interview drives the standard approach-matching symptoms to diagnostic criteria-and results in standardized pharmacological and behavioral treatments, often, with inadequate outcome; but now, recent imaging advances can correlate behavioral assessments with brain function and measure them against normative databases to provide data critical for the reevaluation of patient diagnosis and treatment. This article addresses the data that support a redefinition of our current paradigm. We believe a neurobehavioral approach provides for more personalized treatment approaches unbound from classically defined diagnostic biases.

Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach

The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effects in the diagnosis and treatment of neurodegenerative diseases. Currently, there is no clinically specific diagnostic biomarker capable of confirming the diagnosis of major depressive disorder (MDD). Therefore, exploring translational biomarkers of mood disorders based on deep learning (DL) has valuable potential with its recently underlined promising outcomes. In this article, an electroencephalography (EEG)-based diagnosis model for MDD is built through advanced computational neuroscience methodology coupled with a deep convolutional neural network (CNN) approach. EEG recordings are analyzed by modeling 3 different deep CNN structure, namely, ResNet-50, MobileNet, Inception-v3, in order to dichotomize MDD patients and healthy controls. EEG data are collected for 4 main frequency bands (Δ, θ, α, and β, accompanying spatial resolution with location information by collecting data from 19 electrodes. Following the pre-processing step, different DL architectures were employed to underline discrimination performance by comparing classification accuracies. The classification performance of models based on location data, MobileNet architecture generated 89.33% and 92.66% classification accuracy. As to the frequency bands, delta frequency band outperformed compared to other bands with 90.22% predictive accuracy and area under curve (AUC) value of 0.9 for ResNet-50 architecture. The main contribution of the study is the delineation of distinctive spatial and temporal features using various DL architectures to dichotomize 46 MDD subjects from 46 healthy subjects. Exploring translational biomarkers of mood disorders based on DL perspective is the main focus of this study and, though it is challenging, with its promising potential to improve our understanding of the psychiatric disorders, computational methods are highly worthy for the diagnosis process and valuable in terms of both speed and accuracy compared with classical approaches.

Using prefrontal and midline right frontal EEG-derived theta cordance and depressive symptoms to predict the differential response or remission to antidepressant treatment in major depressive disorder

There is a growing need for optimizing treatment selection and response prediction in individuals with major depressive disorder (MDD). Prior investigations have shown that changes in electroencephalographic (EEG)-based measures precede symptom improvement and could serve as biomarkers of treatment outcome. One such method is cordance, a computation of regional brain activity based on a combination of absolute and relative resting EEG activity. Specifically, early reduction in prefrontal (PF) and midline right frontal (MRF) theta (4-8Hz) cordance has been shown to predict response to various antidepressants, though replication is required. Thus, this study examined early changes (baseline to week 1) in PF and MRF cordance in 47 MDD patients undergoing antidepressant treatment. Early changes in cordance and in Montgomery Åsberg Depression Rating Scale (MADRS) scores were assessed alone, and in combination, to predict eventual (by week 12) treatment response and remission. Models combining early changes in theta cordance (PF and MRF) and depressive symptoms were most predictive of response to treatment at week 12; remission models (cordance, MADRS, and their combination) were weaker, though provided modest prediction values. These results suggest that antidepressant response may be optimally predicted by combining both EEG and symptom-based measures after one week of treatment.

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.

Depression and Sleep

Impaired sleep is both a risk factor and a symptom of depression. Objective sleep is assessed using the sleep electroencephalogram (EEG). Characteristic sleep-EEG changes in patients with depression include disinhibition of rapid eye movement (REM) sleep, changes of sleep continuity, and impaired non-REM sleep. Most antidepressants suppress REM sleep both in healthy volunteers and depressed patients. Various sleep-EEG variables may be suitable as biomarkers for diagnosis, prognosis, and prediction of therapy response in depression. In family studies of depression, enhanced REM density, a measure for frequency of rapid eye movements, is characteristic for an endophenotype. Cordance is an EEG measure distinctly correlated with regional brain perfusion. Prefrontal theta cordance, derived from REM sleep, appears to be a biomarker of antidepressant treatment response. Some predictive sleep-EEG markers of depression appear to be related to hypothalamo-pituitary-adrenocortical system activity.

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.

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.

QEEG in affective disorder: about to be a biomarker, endophenotype and predictor of treatment response

QEEG is a relatively easy to apply, cost effective method among many electrophysiologic and functional brain imaging techniques used to assess individuals for diagnosis and determination of the most suitable treatment. Its temporal resolution provides an important advantage. Many specific EEG indicators play a role in the differential diagnosis of neuropsychiatric disorders. QEEG has advantages over EEG in the dimensional approach to symptomatology of psychiatric disorders. The prognostic value of EEG has a long history. Slow wave EEG rhythm has been reported as a predictor and measure of clinical improvement under ECT. The induction level in delta band activity predicts the long term effect of ECT. Current studies focus on the predictive power of EEG on response to pharmacotherapy and somatic treatments other than ECT. This paper discusses either QEEG can be a biomarker and/or an endophenotype in affective disorders, if it has diagnostic and prognostic value and if it can contribute to personalized treatment design, through a review of relevant literature.

Progress in Elucidating Biomarkers of Antidepressant Pharmacological Treatment Response: A Systematic Review and Meta-analysis of the Last 15 Years

BACKGROUND

Antidepressant drugs are widely prescribed, but response rates after 3 months are only around one-third, explaining the importance of the search of objectively measurable markers predicting positive treatment response. These markers are being developed in different fields, with different techniques, sample sizes, costs, and efficiency. It is therefore difficult to know which ones are the most promising.

OBJECTIVE

Our purpose was to compute comparable (i.e., standardized) effect sizes, at study level but also at marker level, in order to conclude on the efficacy of each technique used and all analyzed markers.

METHODS

We conducted a systematic search on the PubMed database to gather all articles published since 2000 using objectively measurable markers to predict antidepressant response from five domains, namely cognition, electrophysiology, imaging, genetics, and transcriptomics/proteomics/epigenetics. A manual screening of the abstracts and the reference lists of these articles completed the search process.

RESULTS

Executive functioning, theta activity in the rostral Anterior Cingular Cortex (rACC), and polysomnographic sleep measures could be considered as belonging to the best objectively measured markers, with a combined d around 1 and at least four positive studies. For inter-category comparisons, the approaches that showed the highest effect sizes are, in descending order, imaging (combined d between 0.703 and 1.353), electrophysiology (0.294-1.138), cognition (0.929-1.022), proteins/nucleotides (0.520-1.18), and genetics (0.021-0.515).

CONCLUSION

Markers of antidepressant treatment outcome are numerous, but with a discrepant level of accuracy. Many biomarkers and cognitions have sufficient predictive value (d ≥ 1) to be potentially useful for clinicians to predict outcome and personalize antidepressant treatment.

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.

Heart rate variability and cordance in rapid eye movement sleep as biomarkers of depression and treatment response

OBJECTIVES

The relevance of rapid eye movement (REM) sleep in affective disorders originates from its well-known abnormalities in depressed patients, who display disinhibition of REM sleep reflected by increased frequency of rapid eye movements (REM density). In this study we examined whether heart rate variability (HRV) and prefrontal theta cordance, both derived from REM sleep, could represent biomarkers of antidepressant treatment response.

METHODS

In an open-label, case-control design, thirty-three in-patients (21 females) with a depressive episode were treated with various antidepressants for four weeks. Response to treatment was defined as a ≥50% reduction of HAM-D score at the end of the fourth week. Sleep EEG was recorded after the first and the fourth week of medication. HRV was derived from 3-min artifact-free electrocardiogram segments during REM sleep. Cordance was computed for prefrontal EEG channels in the theta frequency band during tonic REM sleep.

RESULTS

HRV during REM sleep was decreased in depressed patients at week four as compared to controls (high effect size; Cohen's d > 1), and showed a negative correlation with REM density in both, healthy subjects and patients at week four. Further, the fourteen responders had significantly higher prefrontal theta cordance as compared to the nineteen non-responders after the first week of antidepressant medication; in contrast, HRV at week one did not discriminate between responders and non-responders.

CONCLUSIONS

Our data suggest that HRV in REM sleep categorizes healthy subjects and depressed patients, whereas REM sleep-derived prefrontal cordance may predict the response to antidepressant treatment in depressed patients.

In patients suffering from major depressive disorders, quantitative EEG showed favorable changes in left and right prefrontal cortex

BACKGROUND

Patients suffering from major depressive disorders (MDD) report anhedonia, low concentration and lack of goal-oriented behavior. Data from imaging and quantitative EEG (QEEG) studies show an asymmetry in the prefrontal cortex (PFC), with lower left as compared to right PFC-activity, associated with specific depression-related behavior. Cordance is a QEEG measurement, which combines absolute and relative power of EEG-spectra with strong correlations with regional perfusion. The aim of the present study was to investigate to what extent a four weeks lasting treatment with a standard SSRI had an influence on neuronal activation and MDD-related symptoms.

METHOD

Twenty patients suffering from severe MDD were treated with citalopram (40mg) for four consecutive weeks. At baseline and at the end of the treatment, patients underwent QEEG. Experts rated the degree of depression with the Hamilton Depression Rating Scale (HDRS).

RESULTS

Over time, theta cordance increased over right ventromedial and left dorsolateral PFC, whereas alpha cordance decreased over dorsolateral PFC. Improvement in MDD-related symptoms was higher in patients showing decreased EEG theta cordance over right dorsal PFC and increased EEG alpha cordance over left dorsolateral PFC.

CONCLUSIONS

In patients suffering from MDD, treatment response was associated with favorable changes in neuronal activity.

Can neurophysiologic measures serve as biomarkers for the efficacy of repetitive transcranial magnetic stimulation treatment of major depressive disorder?

Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for Major Depressive Disorder (MDD). There are clinical data that support the efficacy of many different approaches to rTMS treatment, and it remains unclear what combination of stimulation parameters is optimal to relieve depressive symptoms. Because of the costs and complexity of studies that would be necessary to explore and compare the large number of combinations of rTMS treatment parameters, it would be useful to establish reliable surrogate biomarkers of treatment efficacy that could be used to compare different approaches to treatment. This study reviews the evidence that neurophysiologic measures of cortical excitability could be used as biomarkers for screening different rTMS treatment paradigms. It examines evidence that: (1) changes in excitability are related to the mechanism of action of rTMS; (2) rTMS has consistent effects on measures of excitability that could constitute reliable biomarkers; and (3) changes in excitability are related to the outcomes of rTMS treatment of MDD. An increasing body of evidence indicates that these neurophysiologic measures have the potential to serve as reliable biomarkers for screening different approaches to rTMS treatment of MDD.

Nonlinear analysis of EEG in major depression with fractal dimensions

Major depressive disorder (MDD) is a psychiatric mood disorder characterized by cognitive and functional impairments in attention, concentration, learning and memory. In order to investigate and understand its underlying neural activities and pathophysiology, EEG methodologies can be used. In this study, we estimated the nonlinearity features of EEG in MDD patients to assess the dynamical properties underlying the frontal and parietal brain activity. EEG data were obtained from 16 patients and 15 matched healthy controls. A wavelet-chaos methodology was used for data analysis. First, EEGs of subjects were decomposed into 5 EEG sub-bands by discrete wavelet transform. Then, both the Katz's and Higuchi's fractal dimensions (KFD and HFD) were calculated as complexity measures for full-band and sub-bands EEGs. Last, two-way analyses of variances were used to test EEG complexity differences on each fractality measures. As a result, a significantly increased complexity was found in both parietal and frontal regions of MDD patients. This significantly increased complexity was observed not only in full-band activity but also in beta and gamma sub-bands of EEG. The findings of the present study indicate the possibility of using the wavelet-chaos methodology to discriminate the EEGs of MDD patients from healthy controls.

Cortical functional activity in patients with generalized anxiety disorder

BACKGROUND

The neurological correlates of Generalised Anxiety Disorder (GAD) are not well known, however there is evidence of cortical dysregulation in patients with GAD. The aim of the study was to examine cortical functional activity in different cerebral regions in patients with GAD using electroencephalogram (EEG) nonlinear analysis to evaluate its contribution of anxiety severity.

METHODS

The cohorts consisted of 64 patients diagnosed with GAD as classified by the Structured Clinical Interview for the Diagnostic and Statistical Manual of the American Psychiatric Association-IV-TR. Anxiety severity was assessed using the Hamilton Rating Scale for Anxiety (HAMA) severity score, with 7 ≤ scores ≤ 17 indicating mild anxiety as A group (n = 31) and 18 and above indicating moderate-severe anxiety as B group (n = 33). Participants with clinical levels of depression symptoms were excluded. A healthy control group comprising 30 participants was matched for age and gender. Closed eyes EEGs were conducted, and between-group differences on non-linear parameter Correlation Dimension (D2) were analyzed. The association of D2 value with HAMA scores was analyzed using multiple linear stepwise regression.

RESULTS

Compared with the control group, D2 values were increased in anxiety groups (P < .05). For those with mild anxiety, this difference occurred in the left prefrontal regions (P < .05). For those with moderate-severe anxiety, significantly greater D2 values were observed in all of the cerebral regions, especially in the left cerebral regions and right temporal lobe (P < .01). When compared with those with mild anxiety, D2 values were significantly greater for those with moderate-severe anxiety in the right temporal lobe and all left cerebral regions except for left occipital lobe (P < .05). A positive correlation was observed between D2 values and moderate-severe anxiety HAMA scores.

CONCLUSIONS

The increased D2 values were found in the majority of cerebral regions in GAD patients, especially in the left cerebral regions and the right temporal lobe. The increased GAD severity positively correlates to the D2 values in a larger number of cerebral regions. This analysis method can potentially be used as a complementary tool to examine dysfunctional cortical activity in GAD.

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.

Intermediate phenotypes and biomarkers of treatment outcome in major depressive disorder

Major depressive disorder (MDD) is a pleomorphic illness originating from gene x environment interactions. Patients with differing symptom phenotypes receive the same diagnosis and similar treatment recommendations without regard to genomics, brain structure or function, or other physiologic or psychosocial factors. Using this present approach, only one third of patients enter remission with the first medication prescribed, and patients may take longer than 1 year to enter remission with repeated trials. Research to improve treatment effectiveness recently has focused on identification of intermediate phenotypes (IPs) that could parse the heterogeneous population of patients with MDD into subgroups with more homogeneous responses to treatment. Such IPs could be used to develop biomarkers that could be applied clinically to match patients with the treatment that would be most likely to lead to remission. Putative biomarkers include genetic polymorphisms, RNA and protein expression (transcriptome and proteome), neurotransmitter levels (metabolome), additional measures of signaling cascades, oscillatory synchrony, neuronal circuits and neural pathways (connectome), along with other possible physiologic measures. All of these measures represent components of a continuum that extends from proximity to the genome to proximity to the clinical phenotype of depression, and there are many levels along this continuum at which useful IPs may be defined. Because of the highly integrative nature of brain systems and the complex neurobiology of depression, the most useful biomarkers are likely to be those with intermediate proximity both to the genome and the clinical phenotype of MDD. Translation of findings across the spectrum from genotype to phenotype promises to better characterize the complex disruptions in signaling and neuroplasticity that accompany MDD, and ultimately to lead to greater understanding of the causes of depressive illness.

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.

Cordance derived from REM sleep EEG as a biomarker for treatment response in depression--a naturalistic study after antidepressant medication

OBJECTIVE

To evaluate whether prefrontal cordance in theta frequency band derived from REM sleep EEG after the first week of antidepressant medication could characterize the treatment response after 4 weeks of therapy in depressed patients.

METHOD

20 in-patients (15 females, 5 males) with a depressive episode and 20 healthy matched controls were recruited into 4-week, open label, case-control study. Patients were treated with various antidepressants. No significant differences in age (responders (mean ± SD): 45 ± 22) years; non-responders: 49 ± 12 years), medication or Hamilton Depression Rating Scale (HAM-D) score (responders: 23.8 ± 4.5; non-responders 24.5 ± 7.6) at inclusion into the study were found between responders and non-responders. Response to treatment was defined as a ≥50% reduction of HAM-D score at the end of four weeks of active medication. Sleep EEG of patients was recorded after the first and the fourth week of medication. Cordance was computed for prefrontal EEG channels in theta frequency band during tonic REM sleep.

RESULTS

The group of 8 responders had significantly higher prefrontal theta cordance in relation to the group of 12 non-responders after the first week of antidepressant medication. This finding was significant also when controlling for age, gender and number of previous depressive episodes (F1,15 = 6.025, P = .027). Furthermore, prefrontal cordance of all patients showed significant positive correlation (r = 0.52; P = .019) with the improvement of HAM-D score between the inclusion week and fourth week of medication.

CONCLUSIONS

The results suggest that prefrontal cordance derived from REM sleep EEG could provide a biomarker for the response to antidepressant treatment in depressed patients.

Rhythms and blues: modulation of oscillatory synchrony and the mechanism of action of antidepressant treatments

Treatments for major depressive disorder (MDD) act at different hierarchical levels of biological complexity, ranging from the individual synapse to the brain as a whole. Theories of antidepressant medication action traditionally have focused on the level of cell-to-cell interaction and synaptic neurotransmission. However, recent evidence suggests that modulation of synchronized electrical activity in neuronal networks is a common effect of antidepressant treatments, including not only medications, but also neuromodulatory treatments such as repetitive transcranial magnetic stimulation. Synchronization of oscillatory network activity in particular frequency bands has been proposed to underlie neurodevelopmental and learning processes, and also may be important in the mechanism of action of antidepressant treatments. Here, we review current research on the relationship between neuroplasticity and oscillatory synchrony, which suggests that oscillatory synchrony may help mediate neuroplastic changes related to neurodevelopment, learning, and memory, as well as medication and neuromodulatory treatment for MDD. We hypothesize that medication and neuromodulation treatments may have related effects on the rate and pattern of neuronal firing, and that these effects underlie antidepressant efficacy. Elucidating the mechanisms through which oscillatory synchrony may be related to neuroplasticity could lead to enhanced treatment strategies for MDD.

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.

Neural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordance

OBJECTIVE

The combination of repetitive transcranial magnetic stimulation (rTMS), a non-pharmacological form of therapy for treating major depressive disorder (MDD), and electroencephalogram (EEG) is a valuable tool for investigating the functional connectivity in the brain. This study aims to explore whether pre-treating frontal quantitative EEG (QEEG) cordance is associated with response to rTMS treatment among MDD patients by using an artificial intelligence approach, artificial neural network (ANN).

METHODS

The artificial neural network using pre-treatment cordance of frontal QEEG classification was carried out to identify responder or non-responder to rTMS treatment among 55 MDD subjects. The classification performance was evaluated using k-fold cross-validation.

RESULTS

The ANN classification identified responders to rTMS treatment with a sensitivity of 93.33%, and its overall accuracy reached to 89.09%. Area under Receiver Operating Characteristic (ROC) curve (AUC) value for responder detection using 6, 8 and 10 fold cross validation were 0.917, 0.823 and 0.894 respectively.

CONCLUSION

Potential utility of ANN approach method can be used as a clinical tool in administering rTMS therapy to a targeted group of subjects suffering from MDD. This methodology is more potentially useful to the clinician as prediction is possible using EEG data collected before this treatment process is initiated. It is worth using feature selection algorithms to raise the sensitivity and accuracy values.

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.

Pharmacologic treatments for esophageal disorders

The following, from the 12th OESO World Conference: Cancers of the Esophagus, includes commentaries on the role for ketamine and other alternative treatments in esophageal disorders; the use of linaclotide in the treatment of esophageal pain; the alginate test as a diagnostic criterion in gastroesophageal reflux disease (GERD); the use of baclofen in treatment of GERD; the effects of opioids on the esophagus; the use of antagonists on the receptor level in GERD; the effect of local formulation of drugs on the esophageal mucosa; and the use of electroencephalographic fingerprints to predict the effect of pharmacological treatment.

Neurophysiologic predictors of response to atomoxetine in young adults with attention deficit hyperactivity disorder: a pilot project

Atomoxetine is a non-stimulant medication with sustained benefit throughout the day, and is a useful pharmacologic treatment option for young adults with Attention-Deficit/Hyperactivity Disorder (ADHD). It is difficult to determine, however, those patients for whom atomoxetine will be both effective and advantageous. Patients may need to take the medication for several weeks before therapeutic benefit is apparent, so a biomarker that could predict atomoxetine effectiveness early in the course of treatment could be clinically useful. There has been increased interest in the study of thalamocortical oscillatory activity using quantitative electroencephalography (qEEG) as a biomarker in ADHD. In this study, we investigated qEEG absolute power, relative power, and cordance, which have been shown to predict response to reuptake inhibitor antidepressants in Major Depressive Disorder (MDD), as potential predictors of response to atomoxetine. Forty-four young adults with ADHD (ages 18-30) enrolled in a multi-site, double-blind placebo-controlled study of the effectiveness of atomoxetine and underwent serial qEEG recordings at pretreatment baseline and one week after the start of medication. qEEG measures were calculated from a subset of the sample (N = 29) that provided useable qEEG recordings. Left temporoparietal cordance in the theta frequency band after one week of treatment was associated with ADHD symptom improvement and quality of life measured at 12 weeks in atomoxetine-treated subjects, but not in those treated with placebo. Neither absolute nor relative power measures selectively predicted improvement in medication-treated subjects. Measuring theta cordance after one week of treatment could be useful in predicting atomoxetine treatment response in adult ADHD.

Telemonitoring with respect to mood disorders and information and communication technologies: overview and presentation of the PSYCHE project

This paper reviews what we know about prediction in relation to mood disorders from the perspective of clinical, biological, and physiological markers. It then also presents how information and communication technologies have developed in the field of mood disorders, from the first steps, for example, the transition from paper and pencil to more sophisticated methods, to the development of ecological momentary assessment methods and, more recently, wearable systems. These recent developments have paved the way for the use of integrative approaches capable of assessing multiple variables. The PSYCHE project stands for Personalised monitoring SYstems for Care in mental HEalth.

Do prefrontal midline electrodes provide unique neurophysiologic information in Major Depressive Disorder?

Brain oscillatory activity from the midline prefrontal region has been shown to reflect brain dysfunction in subjects with Major Depressive Disorder (MDD). It is not known, however, whether electrodes from this area provide unique information about brain function in MDD. We examined a set of midline sites and two other prefrontal locations for detecting cerebral activity differences between subjects with MDD and healthy controls. Resting awake quantitative EEG (qEEG) data were recorded from 168 subjects: 47 never-depressed adults and 121 with a current major depressive episode. Individual midline electrodes (Fpz, Fz, Cz, Pz, and Oz) and prefrontal electrodes outside the hairline (Fp1, Fp2) were examined with absolute and relative power and cordance in the theta band. We found that MDD subjects exhibited higher values of cordance (p = 0.0066) at Fpz than controls; no significant differences were found at other locations, and power measures showed trend-level differences. Depressed adults showed higher midline cordance than did never-depressed subjects at the most-anterior midline channel. Salient abnormalities in MDD may be detectable by focusing on the prefrontal midline region, and EEG metrics from focused electrode arrays may offer clinical practicality for clinical monitoring.

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.

EEG biomarkers in major depressive disorder: discriminative power and prediction of treatment response

Major depressive disorder (MDD) has high population prevalence and is associated with substantial impact on quality of life, not least due to an unsatisfactory time span of sometimes several weeks from initiation of treatment to clinical response. Therefore extensive research focused on the identification of cost-effective and widely available electroencephalogram (EEG)-based biomarkers that not only allow distinguishing between patients and healthy controls but also have predictive value for treatment response for a variety of treatments. In this comprehensive overview on EEG research on MDD, biomarkers that are either assessed at baseline or during the early course of treatment and are helpful in discriminating patients from healthy controls and assist in predicting treatment outcome are reviewed, covering recent decades up to now. Reviewed markers include quantitative EEG (QEEG) measures, connectivity measures, EEG vigilance-based measures, sleep-EEG-related measures and event-related potentials (ERPs). Further, the value and limitations of these different markers are discussed. Finally, the need for integrated models of brain function and the necessity for standardized procedures in EEG biomarker research are highlighted to enhance future research in this field.