Comparative effectiveness of biomarkers and clinical indicators for predicting outcomes of SSRI treatment in Major Depressive Disorder: results of the BRITE-MD study

Escitalopram but not placebo modulates brain rhythmic oscillatory activity in the first week of treatment of Major Depressive Disorder

Serotonin modulates brain oscillatory activity, and serotonergic projections to the thalamus and cortex modulate the frequency of prefrontal rhythmic oscillations. Changes in serotonergic tone have been reported to shift oscillations between the combined delta-theta (2.5-8 Hz) and the alpha (8-12 Hz) frequency ranges. Such frequency shifts may constitute a useful biomarker for the effects of selective serotonin reuptake inhibitor (SSRI) medications in Major Depressive Disorder (MDD). We utilized quantitative electroencephalography (qEEG) to measure shifts in prefrontal rhythmic oscillations early in treatment with either the SSRI escitalopram or placebo, and examined the relationship between these changes and remission of depressive symptoms. Prefrontal delta-theta and alpha power were calculated for 194 subjects with moderate MDD prior to and one week after start of treatment. Changes at one week in delta-theta and alpha power, as well as the delta-theta/alpha ratio, were examined in three cohorts: initial (N = 70) and replication (N = 76) cohorts treated with escitalopram, and a cohort treated with placebo (N = 48). Mean delta-theta power significantly increased and alpha power decreased after one week of escitalopram treatment, but did not significantly change with placebo treatment. The delta-theta/alpha ratio change was a specific predictor of the likelihood of remission after seven weeks of medication treatment: a large increase in this ratio was associated with non-remission in escitalopram-treated subjects, but not placebo-treated subjects. Escitalopram and placebo treatment have differential effects on delta-theta and alpha frequency oscillations. Early increase in delta-theta/alpha may constitute a replicable biomarker for non-remission during SSRI treatment of MDD.

Using Electroencephalography for Treatment Guidance in Major Depressive Disorder

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

The quantified EEG characteristics of responders and non-responders to long-term treatment with atomoxetine in children with attention deficit hyperactivity disorders

OBJECTIVE

The aim of our study is to examine quantitative Electroencephalogram (QEEG) differences between ADHD patients that are responders and non-responders to long-term treatment with Atomoxetine at baseline and after 6 and 12months of treatment. Patients with attention deficit hyperactivity disorder (ADHD) received atomoxetine titrated, over 7days, from 0.5 to 1.2mg/kg/day. QEEG and Swanson, Nolan, and Pelham-IV Questionnaire (SNAP-IV) scores were recorded before treatment and after therapy.

METHODS

Twenty minutes of eyes closed resting EEG was recorded from 19 electrodes referenced to linked earlobes. Full frequency and narrow band spectra of two minutes of artifact-free EEG were computed as well as source localization using Variable Resolution Electrical Tomography (VARETA). Abnormalities were identified using Z-spectra relative to normative values.

RESULTS

Patients were classified as responders, non-responders and partial responders based upon the SNAP-IV findings. At baseline, the responders showed increased absolute power in alpha and delta in frontal and temporal regions, whereas, non-responders showed increased absolute power in all frequency bands that was widely distributed. With treatment responders' absolute power values moved toward normal values, whereas, non-responders remained at baseline values.

CONCLUSIONS

Patients with increased power in the alpha band with no evidence of alterations in the beta or theta range, might be responders to treatment with atomoxetine. Increased power in the beta band coupled with increased alpha seems to be related to non-responders and one should consider atomoxetine withdrawal, especially if there is persistence of increased alpha and beta accompanied by an increase of theta.

Cross-trial prediction of treatment outcome in depression: a machine learning approach

BACKGROUND

Antidepressant treatment efficacy is low, but might be improved by matching patients to interventions. At present, clinicians have no empirically validated mechanisms to assess whether a patient with depression will respond to a specific antidepressant. We aimed to develop an algorithm to assess whether patients will achieve symptomatic remission from a 12-week course of citalopram.

METHODS

We used patient-reported data from patients with depression (n=4041, with 1949 completers) from level 1 of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D; ClinicalTrials.gov, number NCT00021528) to identify variables that were most predictive of treatment outcome, and used these variables to train a machine-learning model to predict clinical remission. We externally validated the model in the escitalopram treatment group (n=151) of an independent clinical trial (Combining Medications to Enhance Depression Outcomes [COMED]; ClinicalTrials.gov, number NCT00590863).

FINDINGS

We identified 25 variables that were most predictive of treatment outcome from 164 patient-reportable variables, and used these to train the model. The model was internally cross-validated, and predicted outcomes in the STAR*D cohort with accuracy significantly above chance (64·6% [SD 3·2]; p<0·0001). The model was externally validated in the escitalopram treatment group (N=151) of COMED (accuracy 59·6%, p=0.043). The model also performed significantly above chance in a combined escitalopram-buproprion treatment group in COMED (n=134; accuracy 59·7%, p=0·023), but not in a combined venlafaxine-mirtazapine group (n=140; accuracy 51·4%, p=0·53), suggesting specificity of the model to underlying mechanisms.

INTERPRETATION

Building statistical models by mining existing clinical trial data can enable prospective identification of patients who are likely to respond to a specific antidepressant.

FUNDING

Yale University.

Combining Antidepressants in Acute Treatment of Depression: A Meta-Analysis of 38 Studies Including 4511 Patients

OBJECTIVE

Combining antidepressants (ADs) for therapy of acute depression is frequently employed, but randomized studies have yielded conflicting results. We conducted a systematic review and meta-analysis aimed at determining efficacy and tolerability of combination therapy.

METHODS

MEDLINE, Embase, PsycINFO, and CENTRAL databases were systematically searched through March 2014 for controlled studies comparing combinations of ADs with AD monotherapy in adult patients suffering from acute depression. The prespecified primary outcome was standardized mean difference (SMD), secondary outcomes were response, remission, and dropouts.

RESULTS

Among 8688 articles screened, 38 studies were eligible, including 4511 patients. Combination treatment was statistically, significantly superior to monotherapy (SMD 0.29; 95% CI 0.16 to 0.42). During monotherapy, slightly fewer patients dropped out due to adverse events (OR 0.90; 95% CI 0.53 to 1.53). Studies were heterogeneous (I(2) = 63%), and there was indication of moderate publication bias (fail-safe N for an effect of 0.1:44), but results remained robust across prespecified secondary outcomes and subgroups, including analyses restricted to randomized controlled trials and low risk of bias studies. Meta-regression revealed an association of SMD with difference in imipramine-equivalent dose. Combining a reuptake inhibitor with an antagonist of presynaptic α2-autoreceptors was superior to other combinations.

CONCLUSION

Combining ADs seems to be superior to monotherapy with only slightly more patients dropping out. Combining a reuptake inhibitor with an antagonist of presynaptic α2-autoreceptors seems to be significantly more effective than other combinations. Overall, our search revealed a dearth of well-designed studies.

Targeting drug sensitivity predictors: New potential strategies to improve pharmacotherapy of human brain disorders

One of the main challenges in medicine is the lack of efficient drug therapies for common human disorders. For example, although depressed patients receive powerful antidepressants, many often remain resistant to psychopharmacotherapy. The growing recognition of complex interplay between the drug targets and the predictors of drug sensitivity requires an improved understanding of these two key aspects of drug action and their potentially shared molecular networks. Here, we apply the concept of endophenotypes and their interplay to drug action and sensitivity. Based on these analyses, we postulate that novel drugs may be developed by targeting specific molecular pathways that integrate drug targets with drug sensitivity predictors.

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.

Neuroimaging-based biomarkers for treatment selection in major depressive disorder

The use of neuroimaging approaches to identify likely treatment outcomes in patients with major depressive disorder is developing rapidly. Emerging work suggests that resting state pretreatment metabolic activity in the fronto-insular cortex may distinguish between patients likely to respond to psychotherapy or medication and may function as a treatment-selection biomarker. In contrast, high metabolic activity in the subgenual anterior cingulate cortex may be predictive of poor outcomes to both medication and psychotherapy, suggesting that nonstandard treatments may be pursued earlier in the treatment course. Although these findings will require replication before clinical adoption, they provide preliminary support for the concept that brain states can be measured and applied to the selection of a specific treatment most likely to be beneficial for an individual patient.

Prediction of treatment outcomes in psychiatry--where do we stand ?

Psychiatric treatment relies on a solid armamentarium of pharmacologic and nonpharmacologic treatment modalities that perform reasonably well for many patients but leave others in a state of chronic disability or troubled by problematic side effects. Treatment planning in psychiatry remains an art that depends on considerable trial and error. Thus, there is an urgent need for better tools that will provide a means for matching individual patients with the most effective treatments while minimizing the risk of adverse events. This review will consider the current state of the science in predicting treatment outcomes in psychiatry. Genetic and other biomarkers will be considered alongside clinical diagnostic, and family history data. Problems inherent in prediction medicine will also be discussed, along with recent developments that support the hope that psychiatry can do a better job in quickly identifying the best treatments for each patient.

Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach

Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an optimized classification method was tested in 147 patients with major depressive disorder (MDD) treated with repetitive transcranial magnetic stimulation (rTMS). The performance of the combination of a genetic algorithm (GA) and a back-propagation (BP) neural network (BPNN) was evaluated using 6-channel pre-rTMS electroencephalographic (EEG) patterns of theta and delta frequency bands. The GA was first used to eliminate the redundant and less discriminant features to maximize classification performance. The BPNN was then applied to test the performance of the feature subset. Finally, classification performance using the subset was evaluated using 6-fold cross-validation. Although the slow bands of the frontal electrodes are widely used to collect EEG data for patients with MDD and provide quite satisfactory classification results, the outcomes of the proposed approach indicate noticeably increased overall accuracy of 89.12% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.904 using the reduced feature set.

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.

Chronobiological hypothalamic-pituitary-thyroid axis status and antidepressant outcome in major depression

BACKGROUND

We previously demonstrated that the difference between 2300h and 0800h TSH response to protirelin (TRH) tests on the same day (ΔΔTSH test) is an improved measure in detecting hypothalamic-pituitary-thyroid (HPT) axis dysregulation in depression. This chronobiological index (1) is reduced in about three quarters of major depressed inpatients, and (2) is normalized after successful antidepressant treatment. In the present study, we examined whether early changes in HPT axis activity during the first 2 weeks of antidepressant treatment could be associated with subsequent outcome.

METHODS

The ΔΔTSH test was performed in 50 drug-free DSM-IV euthyroid major depressed inpatients and 50 hospitalized controls. After 2 weeks of antidepressant treatment the ΔΔTSH test was repeated in all inpatients. Antidepressant response was evaluated after 6 weeks of treatment.

RESULTS

At baseline, ΔΔTSH values were significantly lower in patients compared to controls and 38 patients (76%) showed reduced ΔΔTSH values (i.e., <2.5mU/L). After 2 weeks of antidepressant treatment, 20 patients showed ΔΔTSH normalization (among them 18 were subsequent remitters), while 18 patients did not normalize their ΔΔTSH (among them 15 were non-remitters) (p<0.00001). Among the 12 patients who had normal ΔΔTSH values at baseline, 8 out 9 who had still normal values after 2 weeks of treatment were remitters, while the 3 with worsening HPT axis function (i.e., reduced ΔΔTSH value after 2 weeks of treatment) were non-remitters (p<0.02). A logistic regression analysis revealed that ΔΔTSH levels after 2 weeks of treatment could predict the probability of remission (odds ratio [OR]=2.11, 95% confidence interval [CI]=1.31-3.41).

CONCLUSIONS

Our results suggest that after 2 weeks of antidepressant treatment: (1) chronobiological restoration of the HPT axis activity precedes clinical remission, and (2) alteration of the HPT axis is associated with treatment resistance.

Early identification of treatment non-response utilizing the Patient Health Questionnaire (PHQ-9)

BACKGROUND

Treatment non-response among high-risk, psychiatric patients exposes those suffering to suicidal risk as well as persistent social and occupational difficulties. Strategies for identification of treatment non-response are limited.

AIMS

Diagnostic efficiency of a self-report, cross-cutting symptom measure was assessed as a marker of treatment non-response.

METHOD

835 inpatients at a specialist psychiatric hospital completed the Patient Health Questionnaire - Depression (PHQ-9) at admission and every two weeks during hospitalization.

RESULTS

For patients admitted with severe depression (PHQ-9 ≥ 20), results indicated good accuracy of 2-week PHQ-9 change score in identifying treatment non-response (AUC = 0.80, SE = 0.04, p < .0001; sensitivity = 85%; specificity = 73%; OR = 14.91).

CONCLUSIONS

The search for predictors of non-response to psychiatric treatment has a long and generally unfulfilled history. The PHQ-9 change score holds promise as a cost-effective test with comparable diagnostic characteristics to other medical tests.

Analysis of potentially predictive factors of efficacy of adjunct extended-release quetiapine fumarate in patients with major depressive disorder

Identification of predictors of treatment response in patients with major depressive disorder (MDD) may facilitate improved disease management. Data were pooled from two 6-week, double-blind, placebo-controlled studies of extended-release quetiapine (quetiapine XR; 150 or 300 mg/day) as adjunct to ongoing antidepressant therapy. Effects of psychiatric history and baseline demographic and disease characteristics on efficacy outcomes (Week 6 Montgomery Åsberg Depression Rating Scale [MADRS] total score reduction) were evaluated in population subgroups (quetiapine XR both doses pooled, n = 616; placebo, n = 303). Baseline Clinical Global Impressions-Severity (CGI-S) score and previous depressive episodes on Week 6 MADRS total score change, and baseline MADRS individual item scores on Week 6 change in CGI-Improvement score, were also evaluated. No major differences between responders and non-responders to quetiapine XR were observed for patient characteristics or demographic and disease characteristics. No suggestion of a predictive association was found between baseline CGI-S score, number of depressive episodes, and baseline MADRS item scores and efficacy outcomes. These analyses showed no major differences between responders and non-responders, and no predictive association between the parameters assessed and efficacy outcomes for adjunct quetiapine XR in patients with MDD and an inadequate response to prior antidepressant therapy.

Blood transcriptomic markers for major depression: from animal models to clinical settings

Depression is a heterogeneous disorder and, similar to other spectrum disorders, its manifestation varies by age of onset, severity, comorbidity, treatment responsiveness, and other factors. A laboratory blood test based on specific biomarkers for major depressive disorder (MDD) and its subgroups could increase diagnostic accuracy and expedite the initiation of treatment. We identified candidate blood biomarkers by examining genome-wide expression differences in the blood of animal models representing both the genetic and environmental/stress etiologies of depression. Human orthologs of the resulting transcript panel were tested in pilot studies. Transcript abundance of 11 blood markers differentiated adolescent subjects with early-onset MDD from adolescents with no disorder (ND). A set of partly overlapping transcripts distinguished adolescent patients who had comorbid anxiety disorders from those with only MDD. In adults, blood levels of nine transcripts discerned subjects with MDD from ND controls. Even though cognitive behavioral therapy (CBT) resulted in remission of some patients, the levels of three transcripts consistently signaled prior MDD status. A coexpression network of transcripts seems to predict responsiveness to CBT. Thus, our approach can be developed into clinically valid diagnostic panels of blood transcripts for different manifestations of MDD, potentially reducing diagnostic heterogeneity and advancing individualized treatment strategies.

Integrating Different Aspects of Resting Brain Activity: A Review of Electroencephalographic Signatures in Resting State Networks Derived from Functional Magnetic Resonance Imaging

Electroencephalography (EEG) is an established measure in the field of brain resting state with a range of quantitative methods (qEEG) that yield unique information about neuronal activation and synchronization. Meanwhile, in the last decade, functional magnetic resonance imaging (fMRI) studies have revealed the existence of more than a dozen resting state networks (RSNs), and combined qEEG and fMRI have allowed us to gain understanding about the relationship of qEEG and fMRI-RSNs. However, the overall picture is less clear because there is no a priori hypothesis about which EEG features correspond well to fMRI-RSNs. We reviewed the associations of several types of qEEG features to four RSNs considered as neurocognitive systems central for higher brain processes: the default mode network, dorsal and ventral frontoparietal networks, and the salience network. We could identify 12 papers correlating qEEG and RSNs in adult human subjects and employing a simultaneous design under a no-task resting state condition. A systematic overview investigates which qEEG features replicably relate to the chosen RSNs. This review article leads to the conclusion that spatially delimited θ and whole/local α may be the most promising measures, but the time domain methods add important additional information. © 2015 S. Karger AG, Basel.

Physiologic artifacts in resting state oscillations in young children: methodological considerations for noisy data

We quantified the potential effects of physiologic artifact on the estimation of EEG band power in a cohort of typically developing children in order to guide artifact rejection methods in quantitative EEG data analysis in developmental populations. High density EEG was recorded for 2 min while children, ages 2-6, watched a video of bubbles. Segments of data were categorized as blinks, saccades, EMG or artifact-free, and both absolute and relative power in the theta (4-7 Hz), alpha (8-12 Hz), beta (13-30 Hz) and gamma (35-45 Hz) bands were calculated in 9 regions for each category. Using a linear mixed model approach with artifact type, region and their interaction as predictors, we compared mean band power between clean data and each type of artifact. We found significant differences in mean relative and absolute power between artifacts and artifact-free segments in all frequency bands. The magnitude and direction of the differences varied based on power type, region, and frequency band. The most significant differences in mean band power were found in the gamma band for EMG artifact and the theta band for ocular artifacts. Artifact detection strategies need to be sensitive to the oscillations of interest for a given analysis, with the most conservative approach being the removal of all EMG and ocular artifact from EEG data. Quantitative EEG holds considerable promise as a clinical biomarker of both typical and atypical development. However, there needs to be transparency in the choice of power type, regions of interest, and frequency band, as each of these variables are differentially vulnerable to noise, and therefore, their interpretation depends on the methods used to identify and remove artifacts.

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.

Pretreatment brain states identify likely nonresponse to standard treatments for depression

BACKGROUND

Treatment approaches for major depressive disorder (MDD) result in approximately one third of patients achieving remission after a first treatment. Added treatment generally improves remission rates, but approximately one third of all patients fail to respond after several treatments (sequential monotherapies or combined treatment). A pretreatment biomarker could help identify these patients. Overactivity of the subcallosal cingulate has been associated with failure of response to treatment in MDD, and it is a potential candidate for such a biomarker.

METHODS

Investigators enrolled 82 patients with MDD currently not receiving treatment in a two-phase treatment study. Patients underwent a fluorodeoxyglucose positron emission tomography scan. After scanning, patients were randomly assigned to 12 weeks of treatment with either escitalopram or cognitive-behavioral therapy (CBT). Patients not achieving remission after 12 weeks of initial treatment were treated with an additional 12 weeks of escitalopram plus CBT. Subcallosal cingulate metabolism was compared between patients who failed to achieve a response and patients who achieved remission as a result of either phase one or phase two treatment. This analysis was followed by a whole-brain analysis making the same comparison.

RESULTS

After two phases of treatment (24 weeks), 36 patients were identified as remitters, 6 patients were responders, and 9 patients were nonresponders. Subcallosal cingulate metabolism was significantly higher in nonresponders than remitters. In the follow-up whole-brain analysis, increased superior temporal sulcus activity was also associated with nonresponse to two treatments.

CONCLUSIONS

Patients with MDD who fail to achieve remission as a result of CBT or escitalopram, either alone or in combination, have a distinct brain metabolic pattern compared with patients who achieve remission as a result of CBT, escitalopram, or their combination.

Blood transcriptomic biomarkers in adult primary care patients with major depressive disorder undergoing cognitive behavioral therapy

An objective, laboratory-based diagnostic tool could increase the diagnostic accuracy of major depressive disorders (MDDs), identify factors that characterize patients and promote individualized therapy. The goal of this study was to assess a blood-based biomarker panel, which showed promise in adolescents with MDD, in adult primary care patients with MDD and age-, gender- and race-matched nondepressed (ND) controls. Patients with MDD received cognitive behavioral therapy (CBT) and clinical assessment using self-reported depression with the Patient Health Questionnaire-9 (PHQ-9). The measures, including blood RNA collection, were obtained before and after 18 weeks of CBT. Blood transcript levels of nine markers of ADCY3, DGKA, FAM46A, IGSF4A/CADM1, KIAA1539, MARCKS, PSME1, RAPH1 and TLR7, differed significantly between participants with MDD (N=32) and ND controls (N=32) at baseline (q< 0.05). Abundance of the DGKA, KIAA1539 and RAPH1 transcripts remained significantly different between subjects with MDD and ND controls even after post-CBT remission (defined as PHQ-9 <5). The ROC area under the curve for these transcripts demonstrated high discriminative ability between MDD and ND participants, regardless of their current clinical status. Before CBT, significant co-expression network of specific transcripts existed in MDD subjects who subsequently remitted in response to CBT, but not in those who remained depressed. Thus, blood levels of different transcript panels may identify the depressed from the nondepressed among primary care patients, during a depressive episode or in remission, or follow and predict response to CBT in depressed individuals.

Neuroimaging and psychiatry: the long road from bench to bedside

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

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.

Frontal alpha EEG asymmetry before and after behavioral activation treatment for depression

BACKGROUND

Mid-frontal and mid-lateral (F3/F4 and F7/F8) EEG asymmetry has been associated with motivation and affect. We examined alpha EEG asymmetry in depressed and healthy participants before and after Behavioral Activation treatment for depression; examined the association between alpha EEG asymmetry and motivational systems and affect; and evaluated the utility of alpha EEG asymmetry in predicting remission.

METHODS

Depressed (n=37) and healthy participants (n=35) were assessed before and after treatment using a clinical interview, a task to measure baseline EEG, and questionnaires of behavioral activation and inhibition, avoidance, and affect.

RESULTS

Alpha EEG asymmetry was significantly higher in depressed than healthy participants at pre-treatment, positively correlated with negative affect and behavioral inhibition, and inversely correlated with lower behavioral activation sensitivity.

CONCLUSIONS

Heightened alpha EEG asymmetry in depressed participants was significantly associated with increased behavioral inhibition and negative emotion and was independent of clinical remission.

Are there meaningful biomarkers of treatment response for depression?

During the past decades, the prevalence of affective disorders has been on the rise globally, with only one out of three patients achieving remission in acute treatment with antidepressants. The identification of physiological markers that predict treatment course proves useful in increasing therapeutic success. On the basis of well-documented, recent findings in depression research, we highlight and discuss the most promising biomarkers for antidepressant therapy response. These include genetic variants and gene expression profiles, proteomic and metabolomic markers, neuroendocrine function tests, electrophysiology and imaging techniques. Ultimately, this review proposes an integrative use of biomarkers for antidepressant treatment outcome.

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.

Predicting response to cognitive therapy and interpersonal therapy, with or without antidepressant medication, for major depression: a pragmatic trial in routine practice

BACKGROUND

Identifying patient characteristics that predict response within treatments (prognostic) or between treatments (prescriptive) can inform clinical decision-making. In this study, we sought to identify predictors of response to evidence-based treatments in a sample of depressed patients seeking help in routine practice.

METHODS

Data come from a pragmatic trial of 174 patients with major depression who received an evidence-based treatment of their own choice: cognitive therapy (CT), interpersonal therapy (IPT), antidepressant medication (ADM) alone or in combination with either of the two psychotherapies. Patient characteristics measured at baseline were examined to see if they predicted subsequent response as measured with the Beck Depression Inventory (BDI) over the course of 26 weeks of treatment, using mixed regression modeling.

RESULTS

Higher agoraphobia scores at baseline predicted more change in depression scores across treatments, irrespective of the treatment received. Physical functioning moderated the response to treatment: patients with high physical functioning fared better in combined treatment than patients with low physical functioning, whereas physical functioning did not predict a differential response in the psychotherapy group. Moreover, the lowest levels of physical functioning predicted an increase of depressive symptoms in combined treatment.

LIMITATIONS

A relatively small sample size, and selection of several predictors that were less theory-driven, which hampers the translation to clinical practice.

CONCLUSIONS

If replicated, the prognostic and prescriptive indices identified in this study could guide decision-making in routine practice. Development of more uniform requirements for the analysis and reporting of prediction studies is recommended.

Electrocortical features of depression and their clinical utility in assessing antidepressant treatment outcome

Major depressive disorder (MDD) is primarily characterized by decreased affect and accompanying behavioural consequences, but it is also associated with cognitive dysfunction. Assessment of electroencephalographic (EEG) activity and associated event-related potentials (ERPs; derived from averaged EEG activity in response to a stimulus) in the context of MDD has provided insights into the electrocortical abnormalities associated with the disorder. Importantly, EEG and ERPs also have emerged as candidates for predicting and optimizing antidepressant (AD) treatment outcome. This is critical in light of relatively low remission rates or a limited response to initial AD interventions. In contrast to other neuroimaging approaches, EEG and ERPs may be superior for predicting and monitoring AD response, as electrocortical measures are relatively inexpensive, easy to use, and have excellent temporal (that is, millisecond) resolution, enabling fine-grained assessment of basic cognitive and emotive processes. This review aims to highlight the most consistently noted EEG and ERP features in MDD, which may one day assist with diagnostic confirmation, as well as the potential clinical utility of specific electrocortical measures in aiding with response prediction.

Can pharmaco-electroencephalography help improve survival of central nervous system drugs in early clinical development?

Pharmaco-electroencephalography has significant yet unrealised promise as a translatable intermediate biomarker of central pharmacodynamic activity that could help reduce Phase 2 attrition in the development of central nervous system drugs. In an effort to understand its true potential, a framework for decision-making was proposed and the utility of pharmaco-electroencephalography was assessed through several case studies. A key finding was that lack of standardisation reduces the value of data pooling and meta-analyses and renders assessment of translatability difficult, limiting utility in all but simple cases. Pre-competitive collaboration is essential both to improving understanding of translation and developing modern signal processing techniques.

Toward a neuroimaging treatment selection biomarker for major depressive disorder

IMPORTANCE

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

OBJECTIVE

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

DESIGN

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

SETTING

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

PARTICIPANTS

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

INTERVENTION

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

MAIN OUTCOME AND MEASURE

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

RESULTS

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

CONCLUSIONS AND RELEVANCE

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

TRIAL REGISTRATION

Registered at clinicaltrials.gov (NCT00367341).

Baseline and treatment-emergent EEG biomarkers of antidepressant medication response do not predict response to repetitive transcranial magnetic stimulation

There has been a surge of interest in biomarkers that can rapidly predict or assess response to psychiatric treatment, as the current standard practice of extended therapeutic trials is often dissatisfying to both clinicians and patients. Electroencephalographic (EEG) biomarkers in particular have been proposed as an inexpensive yet rapid way of determining whether a patient is responding to an intervention, usually before subjective mood improvement occurs. However, even the most well-reported EEG algorithms have not been subjected to independent replication, limiting their clinical generalizability. It is also unclear whether those biomarkers can generalize beyond their original study population, e.g. to patients undergoing somatic treatments for depression. We report here analysis of EEG data from the pivotal OPT-TMS study of transcranial magnetic stimulation (rTMS) for major depressive disorder. In this dataset, previously reported biomarkers of medication response showed no significant correlation with eventual response to rTMS treatment. Furthermore, EEG power in multiple bands measured at baseline and throughout the treatment course did not correlate with or predict either binary (response/nonresponse) or continuous (Hamilton Rating Scale for Depression) outcome measures. While somewhat limited by technical difficulties in data collection, these analyses are adequately powered to detect clinically relevant biomarkers. We believe this highlights a need for wider-scale independent replication of previous EEG biomarkers, both in pharmacotherapy and neuromodulation.

Oscillatory serotonin function in depression

Oscillations in brain activities with periods of minutes to hours may be critical for normal mood behaviors. Ultradian (faster than circadian) rhythms of mood behaviors and associated central nervous system activities are altered in depression. Recent data suggest that ultradian rhythms in serotonin (5HT) function also change in depression. In two separate studies, 5HT metabolites in cerebrospinal fluid (CSF) were measured every 10 min for 24 h before and after chronic antidepressant treatment. Antidepressant treatments were associated with enhanced ultradian amplitudes of CSF metabolite levels. Another study used resting-state functional magnetic resonance imaging (fMRI) to measure amplitudes of dorsal raphé activation cycles following sham or active dietary depletions of the 5HT precursor (tryptophan). During depletion, amplitudes of dorsal raphé activation cycles increased with rapid 6 s periods (about 0.18 Hz) while functional connectivity weakened between dorsal raphé and thalamus at slower periods of 20 s (0.05 Hz). A third approach studied MDMA (ecstasy, 3,4-methylenedioxy-N-methylamphetamine) users because of their chronically diminished 5HT function compared with non-MDMA polysubstance users (Karageorgiou et al., 2009). Compared with a non-MDMA using cohort, MDMA users showed diminished fMRI intra-regional coherence in motor regions along with altered functional connectivity, again suggesting effects of altered 5HT oscillatory function. These data support a hypothesis that qualities of ultradian oscillations in 5HT function may critically influence moods and behaviors. Dysfunctional 5HT rhythms in depression may be a common endpoint and biomarker for depression, linking dysfunction of slow brain network oscillators to 5HT mechanisms affected by commonly available treatments. 5HT oscillatory dysfunction may define illness subtypes and predict responses to serotonergic agents. Further studies of 5HT oscillations in depression are indicated.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

SIGNIFICANCE

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

Serotonin uptake is largely mediated by platelets versus lymphocytes in peripheral blood cells

The serotonin transporter (SERT), a primary target for many antidepressants, is expressed in the brain and also in peripheral blood cells. Although platelet SERT function is well accepted, lymphocyte SERT function has not been definitively characterized. Due to their small size, platelets often are found in peripheral blood mononuclear cell preparations aimed at isolating lymphocytes, monocytes, and macrophages. The presence of different cells makes it difficult to assign SERT expression and function to specific cell types. Here, we use flow cytometry and IDT307, a monoamine transporter substrate that fluoresces after uptake into cells, to investigate SERT function in lymphocyte and platelet populations independently, as well as simultaneously without prior isolation. We find that murine lymphocytes exhibit temperature-dependent IDT307 transport but uptake is independent of SERT. Lack of measurable SERT function in lymphocytes was corroborated by chronoamperometry using serotonin as a substrate. When we examined rhesus and human mixed blood cell populations, we found that platelets, and not lymphocytes, were primary contributors to SERT function. Overall, these findings indicate that lymphocyte SERT function is minimal. Moreover, flow cytometry, in conjunction with the fluorescent transporter substrate IDT307, can be widely applied to investigate SERT in platelets from populations of clinical significance.

Validity and reliability of electroencephalographic frontal alpha asymmetry and frontal midline theta as biomarkers for depression

Electroencephalographic (EEG) frontal alpha asymmetry (FAA) and frontal midline (FM) theta have been suggested as biomarkers for depression and anxiety, but have mostly been assessed in small and non-clinical studies. In a clinical sample of 79 adults with depression (ICD-10: F32), resting EEG and scales of depression (MADRS) and anxiety (HADS-A) were measured at intake and after 3 months. FAA and FM theta values were referenced to a normative population database. Internal consistency, test-retest reliability, and correlations with psychiatric tests were examined. Reliability was sufficient. However, FAA and FM theta values were close to the general population, and correlations with psychiatric tests were mostly small and non-significant, with the exception of FAA on F7-F8 z-scores and HADS-A. We conclude that the validity of FAA and FM theta and therefore their potential as biomarkers for depression and anxiety remain unclear.

Examination of Org 26576, an AMPA receptor positive allosteric modulator, in patients diagnosed with major depressive disorder: an exploratory, randomized, double-blind, placebo-controlled trial

Org 26576 acts by modulating ionotropic AMPA-type glutamate receptors to enhance glutamatergic neurotransmission. The aim of this Phase 1b study (N=54) was to explore safety, tolerability, pharmacokinetics, and pharmacodynamics of Org 26576 in depressed patients. Part I (N=24) evaluated the maximum tolerated dose (MTD) and optimal titration schedule in a multiple rising dose paradigm (range 100 mg BID to 600 mg BID); Part II (N=30) utilized a parallel groups design (100 mg BID, 400 mg BID, placebo) to examine all endpoints over a 28-day dosing period. Based on the number of moderate intensity adverse events reported at the 600 mg BID dose level, the MTD established in Part I was 450 mg BID. Symptomatic improvement as measured by the Montgomery-Asberg Depression Rating Scale was numerically greater in the Org 26576 groups than in the placebo group in both study parts. In Part II, the 400 mg BID dose was associated with improvements in executive functioning and speed of processing cognitive tests. Org 26576 was also associated with growth hormone increases and cortisol decreases at the end of treatment but did not influence prolactin or brain-derived neurotrophic factor. The quantitative electroencephalogram index Antidepressant Treatment Response at Week 1 was able to significantly predict symptomatic response at endpoint in the active treatment group, as was early improvement in social acuity. Overall, Org 26576 demonstrated good tolerability and pharmacokinetic properties in depressed patients, and pharmacodynamic endpoints suggested that it may show promise in future well-controlled, adequately powered proof of concept trials.

The drugs don't work? antidepressants and the current and future pharmacological management of depression

Depression is a potentially life-threatening disorder affecting millions of people across the globe. It is a huge burden to both the individual and society, costing over £9 billion in 2000 alone: the World Health Organisation (WHO) cited it as the third leading cause of global disability in 2004 (first in the developed world), and project it will be the leading cause by 2030. The serendipitous discovery of antidepressants has revolutionized both our understanding and management of depression: however, their efficacy in the treatment of depression has long been debated and recently been brought very much into the public limelight by a controversial publication by Kirsch, in which the role of placebo response in antidepressant efficacy trials is highlighted. Whilst antidepressants offer benefits in both the short and long term, important problems persist such as intolerability, delayed therapeutic onset, limited efficacy in milder depression and the existence of treatment-resistant depression.

Citalopram versus other anti-depressive agents for depression

BACKGROUND

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

OBJECTIVES

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

SEARCH METHODS

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

SELECTION CRITERIA

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

DATA COLLECTION AND ANALYSIS

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

MAIN RESULTS

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

AUTHORS' CONCLUSIONS

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

Predictors of remission in depression to individual and combined treatments (PReDICT): study protocol for a randomized controlled trial

BACKGROUND

Limited controlled data exist to guide treatment choices for clinicians caring for patients with major depressive disorder (MDD). Although many putative predictors of treatment response have been reported, most were identified through retrospective analyses of existing datasets and very few have been replicated in a manner that can impact clinical practice. One major confound in previous studies examining predictors of treatment response is the patient's treatment history, which may affect both the predictor of interest and treatment outcomes. Moreover, prior treatment history provides an important source of selection bias, thereby limiting generalizability. Consequently, we initiated a randomized clinical trial designed to identify factors that moderate response to three treatments for MDD among patients never treated previously for the condition.

METHODS/DESIGN

Treatment-naïve adults aged 18 to 65 years with moderate-to-severe, non-psychotic MDD are randomized equally to one of three 12-week treatment arms: (1) cognitive behavior therapy (CBT, 16 sessions); (2) duloxetine (30-60 mg/d); or (3) escitalopram (10-20 mg/d). Prior to randomization, patients undergo multiple assessments, including resting state functional magnetic resonance imaging (fMRI), immune markers, DNA and gene expression products, and dexamethasone-corticotropin-releasing hormone (Dex/CRH) testing. Prior to or shortly after randomization, patients also complete a comprehensive personality assessment. Repeat assessment of the biological measures (fMRI, immune markers, and gene expression products) occurs at an early time-point in treatment, and upon completion of 12-week treatment, when a second Dex/CRH test is also conducted. Patients remitting by the end of this acute treatment phase are then eligible to enter a 21-month follow-up phase, with quarterly visits to monitor for recurrence. Non-remitters are offered augmentation treatment for a second 12-week course of treatment, during which they receive a combination of CBT and antidepressant medication. Predictors of the primary outcome, remission, will be identified for overall and treatment-specific effects, and a statistical model incorporating multiple predictors will be developed to predict outcomes.

DISCUSSION

The PReDICT study's evaluation of biological, psychological, and clinical factors that may differentially impact treatment outcomes represents a sizeable step toward developing personalized treatments for MDD. Identified predictors should help guide the selection of initial treatments, and identify those patients most vulnerable to recurrence, who thus warrant maintenance or combination treatments to achieve and maintain wellness.

The neurobiology of the EEG biomarker as a predictor of treatment response in depression

The management of depression remains a constant challenge in clinical practice. This is largely due to the fact that initial treatments frequently do not lead to remission and recovery. The current treatment approach involves lengthy trial-and-error periods. It would be beneficial to have early reliable predictors to determine whether patients will respond to treatment or not. Electroencephalography (EEG) derived biomarkers namely change in the activity of EEG frequency bands, hemispheric alpha asymmetry, theta cordance, the antidepressant treatment response index (ATR) and evoked potentials have all been shown to predict response to a variety of antidepressant medications. However, the neurobiology in support of this association has been largely unexplored. In this review, we discuss biological mechanisms for each EEG derived biomarker predictive of treatment response. Validating such biomarkers will not only greatly aid clinicians in selecting antidepressant treatment for individual patients but will also provide a critical step in drug discovery.

How biomarkers will change psychiatry: from clinical trials to practice. Part I: introduction

Part I of this series defines biomarkers and discusses their research utility and potential application in clinical practice. To provide a frame of reference, biomarkers commonly used in general medicine are reviewed, with a focus on low density lipoprotein as a biomarker for risk of developing atherosclerosis. The use of biomarkers in research on psychiatric illnesses is then reviewed. While biomarkers currently have only a limited role in psychiatric care, their use has improved our ability to assess potential efficacy and safety of investigational new drugs. For example, positron emission tomography can be used to measure dopamine D2 receptor occupancy (relevant for antipsychotic effects) or serotonin transporter occupancy (relevant for antidepressant effects), to establish whether an agent reaches and to what extent it affects a specific target in the brain. In the future, biomarkers are likely to become an integral component of psychiatric treatment, providing information concerning a patient's odds of developing an illness, diagnosis, severity of illness, and level of response to therapeutic interventions. The second part of this series will discuss research on derivatives of the inflammatory biomarker thromboxane and depression.