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Introduction: Currently, major depressive disorder (MDD) treatment plans are based on trial-and-error, and remission rates remain low. A strategy to replace trial-and-error and increase remission rates could be treatment stratification. We explored the heartbeat-evoked potential (HEP) as a biomarker for treatment stratification to either antidepressant medication or rTMS treatment. Methods: Two datasets were analyzed: (1) the International Study to Predict Optimized Treatment in Depression (iSPOT-D; n = 1,008 MDD patients, randomized to escitalopram, sertraline, or venlafaxine, and n = 336 healthy controls) and (2) a multi-site, open-label rTMS study (n = 196). The primary outcome measure was remission. Cardiac field artifacts were removed from the baseline EEG using independent component analysis (ICA). The HEP-peak was detected in a bandwidth of 20 ms around 8 ms and 270 ms (N8, N270) after the R-peak of the electrocardiogram signal. Differences between remitters and non-remitters were statistically assessed by repeated-measures ANOVAs for electrodes Fp1, Cz, and Oz. Results: In the venlafaxine subgroup, remitters showed a lower HEP around the N8 peak than non-remitters on electrode site Cz (p = 0.004; d = 0.497). The rTMS group showed a non-significant difference in the opposite direction (d = −0.051). Retrospective stratification to one of the treatments based on the HEP resulted in enhanced treatment outcome prediction for venlafaxine (+22.98%) and rTMS (+10.66%). Conclusion: These data suggest that the HEP could be used as a stratification biomarker between venlafaxine and rTMS; however, future out-of-sample replication is warranted.

Contains fulltext : 247442.pdf (Publisher’s version ) (Open Access) Background: Attention-deficit/hyperactivity disorder is characterized by neurobiological heterogeneity, possibly explaining why not all patients benefit from a given treatment. As a means to select the right treatment (stratification), biomarkers may aid in personalizing treatment prescription, thereby increasing remission rates. Methods: The biomarker in this study was developed in a heterogeneous clinical sample (N=4249), and first applied to two large transfer datasets, a priori stratifying young males (

We here examine associations of a recently published polygenic risk score of antidepressant response (PRS-AR) with antidepressant treatment outcomes (remission and depression score change) in an independent clinical trial. We not only replicate the PRS-AR for escitalopram, but also find antidepressant interaction effects, suggesting drug-specificity of PRS-AR. We therefore also tested the utility of this PRS-AR to stratify between antidepressants and demonstrate a 14% increase in remission rate (from 43.6% to 49.7%), relative to the randomized remission rate.

Attention-deficit/hyperactivity disorder (ADHD) is characterized by neurobiological heterogeneity, possibly explaining why not all patients benefit from a given treatment. As a means to select the right treatment (stratification), biomarkers may aid in personalizing treatment prescription, thereby increasing remission rates.The present study introduces a clinically interpretable and actionable, age- and sex-standardized biomarker based on individual alpha peak frequency (iAPF) assessed during resting-state electroencephalography (EEG). The biomarker was developed in a heterogeneous sample (N=4249), and stratifies patients with a higher iAPF to Methylphenidate (MPH; N=336) and those with a lower iAPF to Neurofeedback (NFB; N=136), resulting in a predicted gain in normalized remission of 17-30%. Blinded out-of-sample validation studies for MPH (N=58) and NFB (N=96) corroborated these findings, yielding a predicted gain in stratified normalized remission of 36% and 29%, respectively.These findings suggest that acknowledging neurobiological heterogeneity can inform stratification of patients to their individual best treatment and enhance remission rates.

Side effects to antidepressant medications are common and can impact the prognosis of successful treatment outcome in people with major depressive disorder (MDD). However, few studies have investigated the severity of side effects over the course of treatment and their association with treatment outcome. Here we assessed the severity of side effects and the impact of treatment type and anxiety symptoms over the course of treatment, as well as whether side effects were associated with treatment outcome. Participants were N = 1008 adults with a current diagnosis of single-episode or recurrent, nonpsychotic MDD. Participants were randomised to receive escitalopram, sertraline, or venlafaxine-extended release with equal probability and reassessed at 8 weeks regarding Hamilton Rating Scale Depression (HRSD17) and Quick Inventory of Depressive Symptomatology (QIDS-SR16) remission and response. Severity of side effects were assessed using the Frequency, Intensity, and Burden of Side Effects Rating (FIBSER) scale and assessed at day 4 and weeks 2, 4, 6, and 8. Frequency, intensity, and burden of side effects were greatest at week 2, then only frequency and intensity of side effects gradually decreased up to week 6. Treatment type and anxiety symptoms did not impact the severity of side effects. A greater burden—but not frequency or intensity—of side effects was associated with poorer treatment outcome and as early as 4 days post-treatment. Together, this work provides an informative mapping of the progression of side effects throughout the treatment course and their association with treatment outcome. Importantly, the burden of side effects that are present as early as 4 days post-treatment predicts poorer treatment outcome and should be monitored closely. iSPOT-D: Registry name: ClinicalTrials.gov. Registration number: NCT00693849.

Occipital bending (OB) describes asymmetry of the occipital lobes where one lobe wraps across the midline, and has been associated with the presence of mood disorders. We evaluated the relationship between OB and major depressive disorder (MDD) in a large population of subjects from the International Study to Predict Optimized Treatment in Depression. MDD patients (n = 231) and healthy controls (n = 68) underwent MRI and neuropsychiatric evaluation, including response or remission to antidepressant medication at baseline and at 8 weeks. Cortical thickness, ventricular volumes and regional grey matter volumes were measured. OB was visually assessed and OB angle measured using a semi-automated method. Correlations with MDD diagnosis, MRI measures and clinical features were tested. Results demonstrated a greater proportion of rightwards OB in MDD compared to control subjects (p = 0.02). There was no difference in the total prevalence of OB (combined left and rightward bending) between MDD and controls. MDD subjects with right OB had greater cortical thickness in three medial occipital regions (cuneus, lingual gyrus and calcarine sulcus) on the left. Lateral ventricular size was 20% lower bilaterally in right OB MDD subjects compared to non-OB MDD subjects. OB was not associated with severity (HDRS-17). Our data suggest the presence of a strong link between greater rightward occipital bending and MDD. Rightward-OB is associated with greater left medial occipital cortical thickness, and with reduced lateral ventricular size. The cause for greater rightward bending in MDD patients is unclear, however our data suggest a developmental aetiology.

The treatment of major depressive disorder (MDD) is hampered by low chances of treatment response in each treatment step, which is partly due to a lack of firmly established outcome-predictive biomarkers. Here, we hypothesize that polygenic-informed EEG signatures may help predict differential antidepressant treatment response. Using a polygenic-informed electroencephalography (EEG) data-driven, data-reduction approach, we identify a functional brain network in a large cohort of predominantly psychiatric patients (N=1,123), and discover that this network is sex-specifically associated with polygenic liability to psychiatric illness. Subsequently, we demonstrate the utility of this network in predicting response to repetitive transcranial magnetic stimulation (rTMS) and antidepressant medication in two independent datasets (N=196 and N=1,008). A stratification model aimed at stratifying patients to rTMS or sertraline based on only this EEG component yields improved remission rates varying from 22% to 39%. Overall, our findings highlight the power and utility of a combined polygenic and neurophysiological approach in the search for clinically-relevant biomarkers in psychiatric disorders.

The treatment of major depressive disorder (MDD) is hampered by low chances of treatment response in each treatment step, which is partly due to a lack of firmly established outcome-predictive biomarkers. Here, we hypothesize that polygenic-informed EEG biomarkers may help predict differential antidepressant treatment response. Using a polygenic-informed electroencephalography (EEG) data-driven, data-reduction approach, we identify a functional brain network that is sex-specifically associated with polygenic risk for MDD in psychiatric patients (N=1,123). Subsequently, we demonstrate the utility of this network in predicting response to transcranial magnetic stimulation (TMS) and antidepressant medication in two independent datasets (N=196 and N=1,008). A simulation aimed at stratifying patients to TMS, sertraline or escitalopram/venlafaxine based on only this EEG component yields up to >30% improved remission rates. Overall, our findings highlight the power and utility of a combined polygenic and neurophysiological approach in the search for clinically-relevant biomarkers in psychiatric disorders.

Contains fulltext : 240290.pdf (Publisher’s version ) (Open Access) 1 p.

Background and Objectives: Chronically stressed individuals report deficits spanning cognitive and emotional functioning. However, limitations to clinical populations and measures of stress have impeded the generalisability and scope of results. This study investigated whether chronic stress predicted cognitive and emotional functioning, and whether these relationships differed between males and females, in a large representative sample of healthy participants. Design: Cross-sectional study. Method: 1883 healthy adults sampled from the Brain Resource International Database reported stress using the 21-item Depression Anxiety Stress Scales. Participants then completed a cognitive and emotional assessment battery (IntegNeuro), as well as questionnaires related to sleep, emotional functioning, and self-regulation. Results: In contrast to previously reported results, chronic stress did not predict cognitive functioning. However, higher stress predicted a greater negativity bias and poorer social skills, confirming previous research identifying these links. Conclusions: Cognitive deficits related to stress are absent in healthy participants when stress is measured using the 21-items Depression Anxiety Stress Scales. Identifying how chronic stress is associated with aspects of emotional functioning can lead to personalized interventions for individuals to better manage the negative outcomes resulting from stress.

Rationale. Limited research is available on electrophysiological abnormalities such as epileptiform EEG or EEG slowing in depression and its association with antidepressant treatment response. Objectives. We investigated the association between EEG abnormalities and antidepressant treatment response in the international Study to Predict Optimized Treatment in Depression (iSPOT-D). Methods. Of 1008 participants with major depressive disorder randomized to escitalopram, sertraline, or venlafaxine-XR, 622 completed 8 weeks of treatment per protocol. The study also recruited 336 healthy controls. Treatment response was established after 8 weeks using the 17-item Hamilton Rating Scale for Depression (HRSD17). The resting-state EEG was assessed at baseline with eyes closed. EEG abnormalities including epileptiform activity, EEG slowing, and alpha peak frequency (APF) were scored for all subjects, blind to treatment outcome. Results. Patients and controls did not differ in the occurrence of EEG abnormalities. Furthermore, in the per protocol sample the occurrence of epileptiform EEG and EEG slowing (as a combined marker) were associated with a reduced likelihood of responding to escitalopram ( P = .019; odds ratio [OR] = 3.56) and venlafaxine-XR ( P = .043; OR = 2.76), but not sertraline (OR = 0.73). The response rates for this “any EEG abnormality” groups versus the “no-abnormality” group were 33% and 64% for escitalopram and 41% and 66% for venlafaxine-XR, respectively. A slow APF was associated with treatment response only in the sertraline group ( P = .21; d = .027). Conclusions. EEG abnormalities are associated with nonresponse to escitalopram and venlafaxine-XR, but not sertraline, whereas a slow APF is associated to response for sertraline only.

Objective Over 50% of outpatients with nonpsychotic major depressive disorder (MDD) do not achieve remission with any single antidepressant medication (ADM). There are currently no clinically useful pretreatment measures that inform the decision to prescribe or select ADMs. This report examines whether a biomarker based on diffusion tensor imaging (DTI) measures of brain connectivity can identify a subset of nonremitting patients with a sufficiently high degree of specificity that use of a medication that is likely to fail could be avoided. Methods MDD outpatients recruited from community and primary-care settings underwent pretreatment magnetic resonance imaging as part of the international Study to Predict Optimized Treatment in Depression (conducted December 2008-June 2014). DSM-IV criteria and a 17-item Hamilton Depression Rating Scale (HDRS17) score ≥ 16 confirmed the primary diagnosis of nonpsychotic MDD. Data from the first cohort of MDD patients (n = 74) were used to calculate fractional anisotropy measures of the stria terminalis and cingulate portion of the cingulate bundle (CgC). On the basis of our previous data, we hypothesized that nonremission might be predicted using a ratio of these 2 values. Remission was defined as an HDRS17 score of ≤ 7 following 8 weeks of open-label treatment with escitalopram, sertraline, or venlafaxine extended-release, randomized across participants. The second study cohort (n = 83) was used for replication. Results Thirty-four percent of all participants achieved remission. A value > 1.0 for the ratio of the fractional anisotropy of the stria terminalis over the CgC identified 38% of the nonremitting participants with an accuracy of 88% (test cohort; odds ratio [OR] = 9.6; 95% CI, 2.0-45.9); 24% with an accuracy of 83% (replication cohort; OR = 1.8; 95% CI, 0.5-6.9) and 29% with an accuracy of 86% (pooled data; OR = 4.0; 95% CI, 1.5-11.1). Treatment moderation analysis showed greater specificity for escitalopram and sertraline (χ(2) = 8.07; P = .003). Conclusions To our knowledge, this simple DTI-derived metric represents the first brain biomarker to reliably identify nonremitting patients in MDD. The test identifies a meaningful proportion of nonremitters, has high specificity, and may assist in managing the antidepressant treatment of depression. Trial registration ClinicalTrials.gov identifier: NCT00693849.

Arousal systems are one of the recently announced NIMH Research Domain Criteria to inform future diagnostics and treatment prediction. In major depressive disorder (MDD), altered central nervous system (CNS) wakefulness regulation and an increased sympathetic autonomic nervous system (ANS) activity have been identified as biomarkers with possible discriminative value for prediction of antidepressant treatment response. Therefore, the hypothesis of a more pronounced decline of CNS and ANS-arousal being predictive for a positive treatment outcome to selective-serotonin-reuptake-inhibitor (SSRI) treatment was derived from a small, independent exploratory dataset (N = 25) and replicated using data from the randomized international Study to Predict Optimized Treatment Response in Depression (iSPOT-D). There, 1008 MDD participants were randomized to either a SSRI (escitalopram or sertraline) or a serotonin-norepinephrine-reuptake-inhibitor (SNRI-venlafaxine) arm. Treatment response was established after eight weeks using the 17-item Hamilton Rating Scale for Depression. CNS-arousal (i.e. electroencephalogram-vigilance), ANS-arousal (heart rate) and their change across time were assessed during rest. Responders and remitters to SSRI treatment were characterized by a faster decline of CNS-arousal during rest whereas SNRI responders showed a significant increase of ANS-arousal. Furthermore, SSRI responders/remitters showed an association between ANS- and CNS-arousal regulation in comparison to non-responders/non-remitters while this was not the case for SNRI treatment arm. Since positive treatment outcome to SSRI and SNRI was linked to distinct CNS and ANS-arousal profiles, these predictive markers probably are not disorder specific alterations but reflect the responsiveness of the nervous system to specific drugs. (C) 2015 Elsevier Ltd. All rights reserved.

Item does not contain fulltext Studies have shown that specific networks (default mode network [DMN] and task positive network [TPN]) activate in an anticorrelated manner when sustaining attention. Related EEG studies are scarce and often lack behavioral validation. We performed independent component analysis (ICA) across different frequencies (source-level), using eLORETA-ICA, to extract brain-network activity during resting-state and sustained attention. We applied ICA to the voxel domain, similar to functional magnetic resonance imaging methods of analyses. The obtained components were contrasted and correlated to attentional performance (omission errors) in a large sample of healthy subjects (N = 1397). We identified one component that robustly correlated with inattention and reflected an anticorrelation of delta activity in the anterior cingulate and precuneus, and delta and theta activity in the medial prefrontal cortex and with alpha and gamma activity in medial frontal regions. We then compared this component between optimal and suboptimal attentional performers. For the latter group, we observed a greater change in component loading between resting-state and sustained attention than for the optimal performers. Following the National Institute of Mental Health Research Domain Criteria (RDoC) approach, we prospectively replicated and validated these findings in subjects with attention deficit/hyperactivity disorder. Our results provide further support for the "default mode interference hypothesis". 9 p.

Supplemental material, Supplement_material for Probing the “Default Network Interference Hypothesis” With EEG: An RDoC Approach Focused on Attention by Berrie Gerrits, Madelon A. Vollebregt, Sebastian Olbrich, Hanneke van Dijk, Donna Palmer, Evian Gordon, Roberto Pascual-Marqui, Roy P. C. Kessels and Martijn Arns in Clinical EEG and Neuroscience

The harsh reality is that many medical treatments do not work as expected in a significant percentage of patients, and occasionally there are serious side effects. A new paradigm of personalized medicine is emerging, which proactively tailors treatment to each individual’s biological and psychological profile. The first proof-of-concept phase of personalized medicine has now been achieved. However, it has thus far focused on the use of genomic markers and on disorders of the body. The complexity of the brain is likely to require a shift from a single genetic marker focus to a more integrated approach in which additional brain-related information (neuromarkers) is taken into account. Codevelopment of genomic neuromarkers with new compounds in a personalized medicine approach will lead to increased drug R&D and treatment benefits. The emerging genomic neuromarker potential has begun to be incorporated into the template for the next version of the Diagnostic and Statistical Manual (DSM-V). The statistical power of large subject numbers in databases in general (and standardized databases in particular) provides an ideal source for elucidating the best genomic–neuromarker profiles (explaining most of the main-effects variance), which will empower a brain-related personalized medicine into mainstream clinical practice.

Objective: To determine whether EEG occipital alpha and frontal alpha asymmetry (FAA) distinguishes outpatients with major depression (MDD) from controls, predicts antidepressant treatment outcome, and to explore the role of gender.Methods: In the international Study to Predict Optimized Treatment in Depression (iSPOT-D), a multi-center, randomized, prospective open-label trial, 1008 MDD participants were randomized to escitalopram, sertraline or venlafaxine-extended release. The study also recruited 336 healthy controls. Treatment response was established after eight weeks and resting EEG was measured at baseline (two minutes eyes open and eyes closed).Results: No differences in EEG alpha for occipital and frontal cortex, or for FAA, were found in MDD participants compared to controls. Alpha in the occipital and frontal cortex was not associated with treatment outcome. However, a gender and drug-class interaction effect was found for FAA. Relatively greater right frontal alpha (less cortical activity) in women only was associated with a favorable response to the Selective Serotonin Reuptake Inhibitors escitalopram and sertraline. No such effect was found for venlafaxine-extended release.Conclusions: FAA does not differentiate between MDD and controls, but is associated with antidepressant treatment response and remission in a gender and drug-class specific manner.Significance: Future studies investigating EEG alpha measures in depression should a-priori stratify by gender. (C) 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

In major depressive disorder (MOD), elevated theta current density in the rostral anterior cingulate (rACC), as estimated by source localization of scalp-recorded electroencenphalogram (EEG), has been associated with response to antidepressant treatments, whereas elevated frontal theta has been linked to non-response. This study used source localization to attempt to integrate these apparently opposite results and test, whether antidepressant response is associated with elevated rACC theta and non-response with elevated frontal theta and whether theta activity is a differential predictor of response to different types of commonly used antidepressants. In the international Study to Predict Optimized Treatment in Depression (iSPOT-D), a multi-center, international, randomized, prospective practical trial, 1008 MDD participants were randomized to escitalopram, sertraline or venlafaxine-XR. The study also recruited 336 healthy controls. Treatment response and remission were established after eight weeks using the 17-item Hamilton Rating Scale for Depression (HRSD17). The resting-state EEG was assessed at baseline with eyes closed and source localization (eLORETA) was employed to extract theta from the rACC and frontal cortex. Patients with MDD had elevated theta in both frontal cortex and rACC, with small effect sizes. High frontal and rACC theta were associated with treatment non-response, but not with non-remission, and this effect was most pronounced in a subgroup with previous treatment failures. Low theta in frontal cortex and rACC are found in responders to antidepressant treatments with a small effect size. Future studies should investigate in more detail the role of previous treatment (failure) in the association between theta and treatment outcome. (C) 2015 Elsevier B.V. and ECNP. All rights reserved.

We aimed to characterize a large international cohort of outpatients with MDD within a practical trial design, in order to identify clinically useful predictors of outcomes with three common antidepressant medications in acute-phase treatment of major depressive disorder (MDD). The international Study to Predict Optimized Treatment in Depression has presently enrolled 1008 treatment-seeking outpatients (18-65 years old) at 17 sites (five countries). At pre-treatment, we characterized participants by symptoms, clinical history, functional status and comorbidity. Participants were randomized to receive escitalopram, sertraline or venlafaxine-extended release and managed by their physician following usual treatment practices. Symptoms, function, quality of life, and side-effect outcomes were assessed 8 weeks later. The relationship of anxiety to response and remission was assessed by comorbid Axis I diagnosis, presence/absence of anxiety symptoms, and dimensionally by anxiety symptom severity. The sample had moderate-to-severe symptoms, but substantial comorbidity and functional impairment. Of completers at week 8, 62.2% responded and 45.4% reached remission on the 17-item Hamilton Rating Scale for Depression; 53.3% and 37.6%, respectively on the 16-item Quick Inventory of Depressive Symptoms. Functional improvements were seen across all domains. Most participants had side effects that occurred with a frequency of 25% or less and were reported as being in the "none" to minimal/mild range for intensity and burden. Outcomes did not differ across medication groups. More severe anxiety symptoms at pre-treatment were associated with lower remission rates across all medications, independent of depressive severity, diagnostic comorbidity or side effects. Across medications, we found consistent and similar improvements in symptoms and function, and a dimensional prognostic effect of comorbid anxiety symptoms. These equivalent outcomes across treatments lay the foundation for identifying potential neurobiological and genetic predictors of treatment outcome in this sample.

This study could not confirm the association between the Catechol-O-Methyltransferase Val158Met polymorphism (COMT) and electroencephalographic (EEG) alpha peak frequency (APF) in two independent cohorts of 187 (96 depressed and 91 healthy participants) and 413 healthy participants. If COMT and APF play a role in depression or antidepressant treatment response, they do not have a shared pathway. We emphasize the importance of publishing null-findings for obtaining more accurate overall estimates of genetic effects. (C) 2014 Elsevier Ireland Ltd. All rights reserved.

Accepted August 20, 2012. Address correspondence to Stephen H. Koslow, BRAINnet, 71 Stevenson Street, Suite 400, San Francisco, CA 94105-0908, USA. Tel: 301-237-6690; E-mail: stephen.koslow@brainnet.net Technology and Innovation, Vol. 15, pp. 17–29, 2013 1949-8241/13 $90.00 + .00 Printed in the USA. All rights reserved. DOI: http://dx.doi.org/10.3727/194982413X13608676060457 Copyright  2013 Cognizant Comm. Corp. E-ISSN 1949-825X www.cognizantcommunication.com

Accepted November 5, 2012. Address correspondence to Dr. Evian Gordon, Brain Resource Inc., 1000 Sansome Street, Suite 200, San Francisco, CA 94111, USA. Tel: 415-499-7990; Fax: 415-852-5198; E-mail: eviang@brainresource.com Technology and Innovation, Vol. 15, pp. 53–62, 2013 1949-8241/13 $90.00 + .00 Printed in the USA. All rights reserved. DOI: http://dx.doi.org/10.3727/194982413X13608676060574 Copyright  2013 Cognizant Comm. Corp. E-ISSN 1949-825X www.cognizantcommunication.com