A posterior-alpha ageing network is differentially associated with antidepressant effects of venlafaxine and rTMS.

Major depressive disorder (MDD) is a highly prevalent psychiatric disorder, but chances for remission largely decrease with each failed treatment attempt. It is therefore desirable to assign a given patient to the most promising individual treatment option as early as possible. We used a polygenic score (PGS) informed electroencephalography (EEG) data-driven approach to identify potential predictors for MDD treatment outcome. Post-hoc we conducted exploratory analyses in order to understand the results in depth. First, an EEG independent component analysis produced 54 functional brain networks in a large heterogeneous cohort of psychiatric patients (n = 4,045; 5-84 yrs.). Next, the network that was associated to PGS for antidepressant-response (PRS-AR) in an independent sample (n = 722) was selected: an age-related posterior alpha network that explained >60 % of EEG variance, and was highly stable over recording time. Translational analyses were performed in two other independent datasets to examine if the network was predictive of psychopharmacotherapy (n = 535) and/or repetitive transcranial magnetic stimulation (rTMS) and concomitant psychotherapy (PT; n = 186) outcome. The network predicted remission to venlafaxine (p = 0.015), resulting in a normalized positive predicted value (nPPV) of 138 %, and rTMS + PT - but in opposite direction for women (p = 0.002) relative to men (p = 0.018) - yielding a nPPV of 131 %. Blinded out-of-sample validations for venlafaxine (n = 29) and rTMS + PT (n = 36) confirmed the findings for venlafaxine, while results for rTMS + PT could not be replicated. These data suggest the existence of a relatively stable EEG posterior alpha aging network related to PGS-AR that has potential as MDD treatment predictor.

Heartbeat-Evoked Potential in Major Depressive Disorder: A Biomarker for Differential Treatment Prediction between Venlafaxine and rTMS?

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.

Brainmarker-I Differentially Predicts Remission to Various Attention-Deficit/Hyperactivity Disorder Treatments: A Discovery, Transfer, and Blinded Validation Study.

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 (<18 years) with a higher individual alpha peak frequency (iAPF) to methylphenidate (N = 336) and those with a lower iAPF to multimodal neurofeedback complemented with sleep coaching (N = 136). Blinded, out-of-sample validations were conducted in two independent samples. In addition, the association between iAPF and response to guanfacine and atomoxetine was explored. RESULTS: Retrospective stratification in the transfer datasets resulted in a predicted gain in normalized remission of 17% to 30%. Blinded out-of-sample validations for methylphenidate (n = 41) and multimodal neurofeedback (n = 71) corroborated these findings, yielding a predicted gain in stratified normalized remission of 36% and 29%, respectively. CONCLUSIONS: This study introduces a clinically interpretable and actionable biomarker based on the iAPF assessed during resting-state electroencephalography. Our findings suggest that acknowledging neurobiological heterogeneity can inform stratification of patients to their individual best treatment and enhance remission rates.

Antidepressant side effects and their impact on treatment outcome in people with major depressive disorder: an iSPOT-D report.

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.

Annual variation in attentional response after methylphenidate treatment.

Prevalence rates of attention-deficit/hyperactivity disorder (ADHD) differ with geographical areas varying in sunlight intensity. Sun- or daylight reaching the retina establishes entrainment of the circadian clock to daylight. Changes herein, hence, alterations in clock alignment, could be reflected indirectly in inattention via sleep duration. We here studied (1) annual variation in inattention at treatment initiation; (2) annual variation in response to ADHD treatment [methylphenidate (MPH)] by day of treatment initiation; and (3) dose dependence. We predicted least baseline inattention during a period of high sunlight intensity implying more room for improvement (i.e., a better treatment response) when sunlight intensity is low. These hypotheses were not confirmed. High-dose treated patients, however, had significantly better attention after treatment than low-dosed treated patients, only when treated in the period from winter to summer solstice. Change in solar irradiance (SI) during low-dosed treatment period was negatively related to attentional improvement. The above described findings were primarily found in inattention ratings and replicated in omission errors on a continuous performance task. Daylight and inattention have been proposed to be related via mediation of the circadian system. One mechanism of MPH may be to enhance sensitivity to the diurnal entrainment to sunlight and the question can be raised whether appropriate lighting could potentiate the effects of stimulants.

Probing the "Default Network Interference Hypothesis" With EEG: An RDoC Approach Focused on Attention.

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."

Profound and reproducible patterns of reduced regional gray matter characterize major depressive disorder.

Reduced gray matter (GM) volume may represent a hallmark of major depressive disorder (MDD) neuropathology, typified by wide-ranging distribution of structural alteration. In the study, we aimed to replicate and extend our previous finding of profound and widespread GM loss in MDD, and evaluate the diagnostic accuracy of a structural biomarker derived from GM volume in an interconnected pattern across the brain. In a sub-study of the International Study to Predict Optimized Treatment in Depression (iSPOT-D), two cohorts of clinically defined MDD participants "Test" (n = 98) and "Replication" (n = 131) were assessed alongside healthy controls (n = 66). Using 3T MRI T1-weighted volumes, GM volume differences were evaluated using voxel-based morphometry. Sensitivity, specificity, and area under the receiver operating characteristic curve were used to evaluate an MDD diagnostic biomarker based on a precise spatial pattern of GM loss constructed using principal component analysis. We demonstrated a highly conserved symmetric widespread pattern of reduced GM volume in MDD, replicating our previous findings. Three bilateral dominant clusters were observed: Cluster 1: midline/cingulate (GM reduction: Test: 6.4%, Replication: 5.3%), Cluster 2: medial temporal lobe (GM reduction: Test: 8.2%, Replication: 11.9%), Cluster 3: prefrontal cortex (GM reduction: Test: 12.1%, Replication: 23.2%). We developed a biomarker reflecting the global pattern of GM reduction, achieving good diagnostic classification performance (AUC: Test = 0.75, Replication = 0.84). This study establishes that a highly specific pattern of reduced GM volume is a feature of MDD, suggestive of a structural basis for this disease. We introduce and validate a novel diagnostic biomarker based on this pattern.

Increased chronic stress predicts greater emotional negativity bias and poorer social skills but not cognitive functioning in healthy adults.

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.

Is occipital bending a structural biomarker of risk for depression and sensitivity to treatment?

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.

Gender-specific structural abnormalities in major depressive disorder revealed by fixel-based analysis.

BACKGROUND: Major depressive disorder (MDD) is a chronic disease with a large global impact. There are currently no clinically useful predictors of treatment outcome, and the development of biomarkers to inform clinical treatment decisions is highly desirable. METHODS: In this exploratory study we performed fixel-based analysis of diffusion MRI data from the International Study to Predict Optimized Treatment in Depression with the aim of identifying novel biomarkers at baseline that may relate to diagnosis and outcome to treatment with antidepressant medications. Analyses used MR data from individuals with MDD (n = 221) and healthy controls (n = 67). RESULTS: We show focal, gender-specific differences in the anterior limb of the internal capsule (males) and bilaterally in the genu of the corpus callosum (females) associated with diagnosis. Lower fibre cross-section in the tapetum, the conduit between the right and left hippocampi, were also associated with a decreased probability of remission. Analysis of conventional fractional anisotropy showed scattered abnormalities in the corona radiata, cerebral peduncles and mid-brain which were much lower in total volume compared to fixel-based analysis. CONCLUSIONS: Fixel-based analysis appeared to identify different underlying abnormalities than conventional tensor-based metrics, with almost no overlap between significant regions. We show that MDD is associated with gender specific abnormalities in the genu of the corpus callosum (females) and in the anterior limb of the internal capsule (males), as well as gender-independent differences in the tapetum that predict remission. Diffusion MRI may play a key role in future guidance of clinical decision-making for MDD.

Electroencephalographic biomarkers as predictors of methylphenidate response in attention-deficit/hyperactivity disorder.

EEG biomarkers have shown promise in predicting non-response to stimulant medication in ADHD and could serve as translational biomarkers. This study aimed to replicate and extend previous EEG biomarkers. The international Study to Predict Optimized Treatment for ADHD (iSPOT-A), a multi-center, international, prospective open-label trial, enrolled 336 children and adolescents with ADHD (11.9 yrs; 245 males; prescribed methylphenidate) and 158 healthy children. Treatment response was established after six weeks using the clinician rated ADHD-Rating Scale-IV. Theta/Beta ratio (TBR) and alpha peak frequency (APF) were assessed at baseline as predictors for treatment outcome. No differences between ADHD and controls were found for TBR and APF. 62% of the ADHD group was classified as a responder. Responders did not differ from non-responders in age, medication dosage, and baseline severity of ADHD symptoms. Male-adolescent non-responders exhibited a low frontal APF (Fz: R = 9.2 Hz vs. NR = 8.1 Hz; ES = 0.83), whereas no effects were found for TBR. A low APF in male adolescents was associated with non-response to methylphenidate, replicating earlier work. Our data suggest that the typical maturational EEG changes observed in ADHD responders and controls are absent in non-responders to methylphenidate and these typical changes start emerging in adolescence. CLINICAL TRIALS REGISTRATION: www.clinicaltrials.gov; NCT00863499 (https://clinicaltrials.gov/ct2/show/NCT00863499).

EEG Abnormalities Are Associated With Poorer Depressive Symptom Outcomes With Escitalopram and Venlafaxine-XR, but Not Sertraline: Results From the Multicenter Randomized iSPOT-D Study.

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.

Cognitive Testing to Identify Children With ADHD Who Do and Do Not Respond to Methylphenidate.

OBJECTIVE: To explore the utility of cognitive measures for predicting response of children and adolescents to methylphenidate (MPH). METHOD: Participants from the International Study to Predict Optimized Treatment-in ADHD (iSPOT-A) completed a cognitive test battery prior to receiving 6 weeks of MPH. The responder criterion was a 25% reduction in ADHD-Rating Scale-IV scores. Receiver Operator Characteristics (ROC) classified non-responders from responders with maximal sensitivity and specificity. RESULTS: Overall, 62% of participants responded to MPH. Response rates for ROC-identified groups ranged from 18% to 85%. Non-responders showed compromised cognition related to switching of attention, sustained attention, planning, and impulsivity. One group of responders were 10 years of age or older and had impaired switching of attention and impulsivity; a second group had enhanced switching of attention, normal or higher Continuous Performance Task (CPT) scores, and above average scores on digit span. CONCLUSION: Cognitive tests may provide a simple, low-cost tool for treatment planning for children and adolescents with ADHD.

Prediction of nonremission to antidepressant therapy using diffusion tensor imaging.

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.

EEG alpha asymmetry as a gender-specific predictor of outcome to acute treatment with different antidepressant medications in the randomized iSPOT-D study.

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.

CNS- and ANS-arousal predict response to antidepressant medication: Findings from the randomized iSPOT-D study.

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.

Utility of event-related potentials in predicting antidepressant treatment response: An iSPOT-D report.

It is essential to improve antidepressant treatment of major depressive disorder (MDD) and one way this could be achieved is by reducing the number of treatment steps by employing biomarkers that can predict treatment outcome. This study investigated differences between MDD patients and healthy controls in the P3 and N1 component from the event-related potential (ERP) generated in a standard two-tone oddball paradigm. Furthermore, the P3 and N1 are investigated as predictors for treatment outcome to three different 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. P3 and N1 latencies and amplitudes were analyzed using a peak-picking approach and further replicated by using exact low resolution tomography (eLORETA). A reduced P3 was found in MDD patients compared to controls by a peak-picking analysis. This was validated in a temporal global field power analysis. Source density analysis revealed that the difference in cortical activity originated from the posterior cingulate and parahippocampal gyrus. Male non-responders to venlafaxine-XR had significantly smaller N1 amplitudes than responders. This was demonstrated by both analytical methods. Male non-responders to venlafaxine-XR had less activity originating from the left insular cortex. The observed results are discussed from a neural network viewpoint.

Magnetic Resonance Imaging Measures of Brain Structure to Predict Antidepressant Treatment Outcome in Major Depressive Disorder.

BACKGROUND: Less than 50% of patients with Major Depressive Disorder (MDD) reach symptomatic remission with their initial antidepressant medication (ADM). There are currently no objective measures with which to reliably predict which individuals will achieve remission to ADMs. METHODS: 157 participants with MDD from the International Study to Predict Optimized Treatment in Depression (iSPOT-D) underwent baseline MRIs and completed eight weeks of treatment with escitalopram, sertraline or venlafaxine-ER. A score at week 8 of 7 or less on the 17 item Hamilton Rating Scale for Depression defined remission. Receiver Operator Characteristics (ROC) analysis using the first 50% participants was performed to define decision trees of baseline MRI volumetric and connectivity (fractional anisotropy) measures that differentiated non-remitters from remitters with maximal sensitivity and specificity. These decision trees were tested for replication in the remaining participants. FINDINGS: Overall, 35% of all participants achieved remission. ROC analyses identified two decision trees that predicted a high probability of non-remission and that were replicated: 1. Left middle frontal volume < 14 · 8 mL & right angular gyrus volume > 6 · 3 mL identified 55% of non-remitters with 85% accuracy; and 2. Fractional anisotropy values in the left cingulum bundle < 0 · 63, right superior fronto-occipital fasciculus < 0 · 54 and right superior longitudinal fasciculus < 0 · 50 identified 15% of the non-remitters with 84% accuracy. All participants who met criteria for both decision trees were correctly identified as non-remitters. INTERPRETATION: Pretreatment MRI measures seem to reliably identify a subset of patients who do not remit with a first step medication that includes one of these commonly used medications. Findings are consistent with a neuroanatomical basis for non-remission in depressed patients. FUNDING: Brain Resource Ltd is the sponsor for the iSPOT-D study (NCT00693849).

Frontal and rostral anterior cingulate (rACC) theta EEG in depression: implications for treatment outcome?

In major depressive disorder (MDD), 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.

Toward an online cognitive and emotional battery to predict treatment remission in depression.

PURPOSE: To evaluate the performance of a cognitive and emotional test battery in a representative sample of depressed outpatients to inform likelihood of remission over 8 weeks of treatment with each of three common antidepressant medications. PATIENTS AND METHODS: Outpatients 18-65 years old with nonpsychotic major depressive disorder (17 sites) were randomized to escitalopram, sertraline or venlafaxine-XR (extended release). Participants scored ≥12 on the baseline 16-item Quick Inventory of Depressive Symptomatology - Self-Report and completed 8 weeks of treatment. The baseline test battery measured cognitive and emotional status. Exploratory multivariate logistic regression models predicting remission (16-item Quick Inventory of Depressive Symptomatology - Self-Report score ≤5 at 8 weeks) were developed independently for each medication in subgroups stratified by age, sex, or cognitive and emotional test performance. The model with the highest cross-validated accuracy determined the participant proportion in each arm for whom remission could be predicted with an accuracy ≥10% above chance. The proportion for whom a prediction could be made with very high certainty (positive predictive value and negative predictive value exceeding 80%) was calculated by incrementally increasing test battery thresholds to predict remission/non-remission. RESULTS: The test battery, individually developed for each medication, improved identification of remitting and non-remitting participants by ≥10% beyond chance for 243 of 467 participants. The overall remission rates were escitalopram: 40.8%, sertraline: 30.3%, and venlafaxine-XR: 31.1%. Within this subset for whom prediction exceeded chance, test battery thresholds established a negative predictive value of ≥80%, which identified 40.9% of participants not remitting on escitalopram, 77.1% of participants not remitting on sertraline, and 38.7% of participants not remitting on venlafaxine-XR (all including 20% false negatives). CONCLUSION: The test battery identified about 50% of each medication group as being ≥10% more or less likely to remit than by chance, and identified about 38% of individuals who did not remit with ≥80% certainty. Clinicians might choose to avoid this specific medication in these particular patients.