Search

Despite decades of research, the pathophysiology of bipolar disorder (BD) is still not well understood. Structural brain differences have been associated with BD, but results from neuroimaging studies have been inconsistent. To address this, we performed the largest study to date of cortical gray matter thickness and surface area measures from brain magnetic resonance imaging scans of 6503 individuals including 1837 unrelated adults with BD and 2582 unrelated healthy controls for group differences while also examining the effects of commonly prescribed medications, age of illness onset, history of psychosis, mood state, age and sex differences on cortical regions. In BD, cortical gray matter was thinner in frontal, temporal and parietal regions of both brain hemispheres. BD had the strongest effects on left pars opercularis (Cohen's d=-0.293; P=1.71 × 10-21), left fusiform gyrus (d=-0.288; P=8.25 × 10-21) and left rostral middle frontal cortex (d=-0.276; P=2.99 × 10-19). Longer duration of illness (after accounting for age at the time of scanning) was associated with reduced cortical thickness in frontal, medial parietal and occipital regions. We found that several commonly prescribed medications, including lithium, antiepileptic and antipsychotic treatment showed significant associations with cortical thickness and surface area, even after accounting for patients who received multiple medications. We found evidence of reduced cortical surface area associated with a history of psychosis but no associations with mood state at the time of scanning. Our analysis revealed previously undetected associations and provides an extensive analysis of potential confounding variables in neuroimaging studies of BD.

Background: For psychotic disorders (i.e. schizophrenia), pharmacotherapy plays a key role in controlling acute and long-term symptoms. To find the optimal individual dose and dosage strategy, specialised tools are used. Three tools have been proven useful to personalise drug treatments: therapeutic drug monitoring (TDM) of drug levels, pharmacogenetic testing (PG), and molecular neuroimaging., Methods: In these Guidelines, we provide an in-depth review of pharmacokinetics, pharmacodynamics, and pharmacogenetics for 45 antipsychotics. Over 30 international experts in psychiatry selected studies that have measured drug concentrations in the blood (TDM), gene polymorphisms of enzymes involved in drug metabolism, or receptor/transporter occupancies in the brain (positron emission tomography (PET))., Results: Study results strongly support the use of TDM and the cytochrome P450 (CYP) genotyping and/or phenotyping to guide drug therapies. Evidence-based target ranges are available for titrating drug doses that are often supported by PET findings., Conclusion: All three tools discussed in these Guidelines are essential for drug treatment. TDM goes well beyond typical indications such as unclear compliance and polypharmacy. Despite its enormous potential to optimise treatment effects, minimise side effects and ultimately reduce the global burden of diseases, personalised drug treatment has not yet become the standard of care in psychiatry.

Objective: Response to antidepressant treatment in major depressive disorder varies substantially between individuals, which lengthens the process of finding effective treatment. The authors sought to determine whether a multimodal machine learning approach could predict early sertraline response in patients with major depressive disorder. They assessed the predictive contribution of MR neuroimaging and clinical assessments at baseline and after 1 week of treatment., Methods: This was a preregistered secondary analysis of data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a multisite double-blind, placebo-controlled randomized clinical trial that included 296 adult outpatients with unmedicated recurrent or chronic major depressive disorder. MR neuroimaging and clinical data were collected before and after 1 week of treatment. Performance in predicting response and remission, collected after 8 weeks, was quantified using balanced accuracy (bAcc) and area under the receiver operating characteristic curve (AUROC) scores., Results: A total of 229 patients were included in the analyses (mean age, 38 years [SD=13]; 66% female). Internal cross-validation performance in predicting response to sertraline (bAcc=68% [SD=10], AUROC=0.73 [SD=0.03]) was significantly better than chance. External cross-validation on data from placebo nonresponders (bAcc=62%, AUROC=0.66) and placebo nonresponders who were switched to sertraline (bAcc=65%, AUROC=0.68) resulted in differences that suggest specificity for sertraline treatment compared with placebo treatment. Finally, multimodal models outperformed unimodal models., Conclusions: The study results confirm that early sertraline treatment response can be predicted; that the models are sertraline specific compared with placebo; that prediction benefits from integrating multimodal MRI data with clinical data; and that perfusion imaging contributes most to these predictions. Using this approach, a lean and effective protocol could individualize sertraline treatment planning to improve psychiatric care.

In this work, we expand the normative model repository introduced in Rutherford et al., 2022a to include normative models charting lifespan trajectories of structural surface area and brain functional connectivity, measured using two unique resting-state network atlases (Yeo-17 and Smith-10), and an updated online platform for transferring these models to new data sources. We showcase the value of these models with a head-to-head comparison between the features output by normative modeling and raw data features in several benchmarking tasks: mass univariate group difference testing (schizophrenia versus control), classification (schizophrenia versus control), and regression (predicting general cognitive ability). Across all benchmarks, we show the advantage of using normative modeling features, with the strongest statistically significant results demonstrated in the group difference testing and classification tasks. We intend for these accessible resources to facilitate the wider adoption of normative modeling across the neuroimaging community., Competing Interests: SR, PB, IT, CS, AM No competing interests declared, CB is director and shareholder of SBGNeuro Ltd, HR received speaker's honorarium from Lundbeck and Janssen, (© 2023, Rutherford et al.)

Radiomics in neuroimaging uses fully automatic segmentation to delineate the anatomical areas for which radiomic features are computed. However, differences among these segmentation methods affect radiomic features to an unknown extent. A scan-rescan dataset (n = 46) of T1-weighted and diffusion tensor images was used. Subjects were split into a sleep-deprivation and a control group. Scans were segmented using four segmentation methods from which radiomic features were computed. First, we measured segmentation agreement using the Dice-coefficient. Second, robustness and reproducibility of radiomic features were measured using the intraclass correlation coefficient (ICC). Last, difference in predictive power was assessed using the Friedman-test on performance in a radiomics-based sleep deprivation classification application. Segmentation agreement was generally high (interquartile range = 0.77-0.90) and median feature robustness to segmentation method variation was higher (ICC > 0.7) than scan-rescan reproducibility (ICC 0.3-0.8). However, classification performance differed significantly among segmentation methods (p < 0.001) ranging from 77 to 84%. Accuracy was higher for more recent deep learning-based segmentation methods. Despite high agreement among segmentation methods, subtle differences significantly affected radiomic features and their predictive power. Consequently, the effect of differences in segmentation methods should be taken into account when designing and evaluating radiomics-based research methods., (© 2022. The Author(s).)

Normative modeling is an emerging and innovative framework for mapping individual differences at the level of a single subject or observation in relation to a reference model. It involves charting centiles of variation across a population in terms of mappings between biology and behavior, which can then be used to make statistical inferences at the level of the individual. The fields of computational psychiatry and clinical neuroscience have been slow to transition away from patient versus 'healthy' control analytic approaches, probably owing to a lack of tools designed to properly model biological heterogeneity of mental disorders. Normative modeling provides a solution to address this issue and moves analysis away from case-control comparisons that rely on potentially noisy clinical labels. Here we define a standardized protocol to guide users through, from start to finish, normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit). We describe the input data selection process, provide intuition behind the various modeling choices and conclude by demonstrating several examples of downstream analyses that the normative model may facilitate, such as stratification of high-risk individuals, subtyping and behavioral predictive modeling. The protocol takes ~1-3 h to complete., (© 2022. Springer Nature Limited.)

Introduction: For major depression, a one-size-fits-all treatment does not exist. Patients enter a 'trial-and-change' algorithm in which effective therapies are subsequently applied. Unfortunately, an empirically based order of treatments has not yet been determined. There is a magnitude of different treatment strategies while clinical trials only compare a small number of these. Network meta-analyses (NMA) might offer a solution, but so far have been limited in scope and did not account for possible differences in population characteristics that arise with increasing levels of treatment-resistance, potentially violating the transitivity assumption. We; therefore, present a protocol for a systematic review and NMA aiming at summarising and ranking treatments for treatment-resistant depression (TRD) while covering a broad range of therapeutic options and accounting for possible differences in population characteristics at increasing levels of treatment-resistance., Methods and Analysis: Randomised controlled trials will be included that compared next-step pharmacological, neuromodulation or psychological treatments for treatment-resistant depression (TRD; ie, failure to respond to ≥1 adequate antidepressant drug trial(s) in the current episode) to each other or to a control condition. Primary outcomes will be the proportion of patients who responded to (efficacy) and dropped out of (acceptability) the allocated treatment. A random effects NMA will be conducted, synthesising the evidence for each outcome and determining the differential efficacy of treatments. Heterogeneity in treatment nodes will be reduced by considering alternative geometries of the network structure and by conducting a meta-regression examining different levels of TRD. Local and global methods will be applied to evaluate consistency. The Cochrane Risk of Bias 2 tool, Confidence in Network Meta-Analysis and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework will be used to assess risk of bias and certainty., Ethics and Dissemination: This review does not require ethical approval., Competing Interests: Competing interests: JJM: None to declare. PFPvE reports grants from Zon. MW and speaking fees from Janssen outside of the submitted work. AC is supported by the National Institute for Health Research (NIHR) Oxford Cognitive Health Clinical Research Facility, by an NIHR Research Professorship (grant RP-2017-08-ST2-006), by the NIHR Oxford and Thames Valley Applied Research Collaboration and by the NIHR Oxford Health Biomedical Research Centre (grant BRC-1215-20005). He has received research and consultancy fees from INCiPiT (Italian Network for Paediatric Trials), CARIPLO Foundation and Angelini Pharma. ID: None to declare. SvB: None to declare. TAF reports grants and personal fees from Mitsubishi-Tanabe, personal fees from MSD, grants and personal fees from Shionogi, outside the submitted work; In addition, TAF has a patent 2020-548587 concerning smartphone CBT apps pending, and intellectual properties for Kokoro-app licensed to Mitsubishi-Tanabe. HGR reports grants from ZonMW, Hersenstichting, EU Horizon 2020 and speaking fees from Lundbeck and Janssen outside of the submitted work., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Mindfulness-based cognitive therapy (MBCT) is effective for relapse prevention in major depressive disorder (MDD). It reduces cognitive reactivity (CR) and rumination, and enhances self-compassion and mindfulness. Although rumination and mindfulness after MBCT are associated with relapse, the association of CR, rumination, self-compassion, and mindfulness with relapse before initiation of MBCT has never been investigated., Methods: Data were drawn from two randomized controlled trials, including a total of 282 remitted MDD participants (≥3 depressive episodes) who had been using maintenance antidepressant medication (mADM) for at least 6 months before baseline. All participants were offered MBCT while either their mADM was maintained or discontinued after MBCT. CR, rumination, self-compassion, and mindfulness were assessed at baseline by self-rated questionnaires and were used in Cox proportional hazards regression models to investigate their association with relapse., Results: CR and mindfulness were associated with relapse, independent of residual symptoms, previous depressive episodes, and mADM-use. Higher CR and lower mindfulness increased the risk of relapse. Self-compassion was not associated with relapse. For rumination, a significant interaction with mADM-use was found. Rumination was associated with relapse in patients who discontinued their mADM, while this effect was absent if patients continued mADM., Conclusions: These results show that CR, rumination, and mindfulness are associated with relapse in remitted MDD-patients before initiation of MBCT, independent of residual symptoms and previous depressive episodes. This information could improve decisions in treatment planning in remitted individuals with a history of depression., (© 2021 The Authors. Depression and Anxiety published by Wiley Periodicals LLC.)

Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2-100) and used normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision-making., Competing Interests: SR, CF, RD, SK, TW, MZ, PB, AW, SV, DA, LH, JB, PD, PM, RM, AS, CS, IT, ED, SC, BP, MH, SB, LH, DA, CW, LW, RZ, AM No competing interests declared, OA is a consultant for HealthLytix and received speaker's honorarium from Lundbeck and Sunovion, HR received speaker's honorarium from Lundbeck and Janssen, CB is director and shareholder of SBGNeuro Ltd, (© 2022, Rutherford et al.)

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Background/objective : To describe the design of 'DepMod,' a health-economic Markov model for assessing cost-effectiveness and budget impact of user-defined preventive interventions and treatments in depressive disorders. Methods : DepMod has an epidemiological layer describing how a cohort of people can transition between health states (sub-threshold depression, first episode of mild, moderate or severe depression (partial) remission, recurrence, death). Superimposed on the epidemiological layer, DepMod has an intervention layer consisting of a reference scenario and alternative scenario comparing the effectiveness and cost-effectiveness of a user-defined package of preventive interventions and psychological and pharmacological treatments of depression. Results are presented in terms of quality-adjusted life years (QALYs) gained and healthcare expenditure. Costs and effects can be modeled over 5 years and are subjected to probabilistic sensitivity analysis. Results : DepMod was used to assess the cost-effectiveness of scaling up preventive interventions for treating people with subclinical depression, which showed that there is an 82% probability that scaling up prevention is cost-effective given a willingness-to-pay threshold of €20,000 per QALY. Conclusion : DepMod is a Markov model that assesses the cost-utility and budget impact of different healthcare packages aimed at preventing and treating depression and is freely available for academic purposes upon request at the authors.

Objectives: More than 40 drugs are available to treat affective disorders. Individual selection of the optimal drug and dose is required to attain the highest possible efficacy and acceptable tolerability for every patient. Methods: This review, which includes more than 500 articles selected by 30 experts, combines relevant knowledge on studies investigating the pharmacokinetics, pharmacodynamics and pharmacogenetics of 33 antidepressant drugs and of 4 drugs approved for augmentation in cases of insufficient response to antidepressant monotherapy. Such studies typically measure drug concentrations in blood (i.e. therapeutic drug monitoring) and genotype relevant genetic polymorphisms of enzymes, transporters or receptors involved in drug metabolism or mechanism of action. Imaging studies, primarily positron emission tomography that relates drug concentrations in blood and radioligand binding, are considered to quantify target structure occupancy by the antidepressant drugs in vivo. Results: Evidence is given that in vivo imaging, therapeutic drug monitoring and genotyping and/or phenotyping of drug metabolising enzymes should be an integral part in the development of any new antidepressant drug. Conclusions: To guide antidepressant drug therapy in everyday practice, there are multiple indications such as uncertain adherence, polypharmacy, nonresponse and/or adverse reactions under therapeutically recommended doses, where therapeutic drug monitoring and cytochrome P450 genotyping and/or phenotyping should be applied as valid tools of precision medicine.

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates., (© 2020. The Author(s).)

Depressed patients often do not respond to the first antidepressant prescribed, resulting in sequential trials of different medications. Personalised medicine offers a means of reducing this delay; however, the clinical effectiveness of personalised approaches to antidepressant treatment has not previously been tested. We assessed the clinical effectiveness of using a predictive algorithm, based on behavioural tests of affective cognition and subjective symptoms, to guide antidepressant treatment. We conducted a multicentre, open-label, randomised controlled trial in 913 medication-free depressed patients. Patients were randomly assigned to have their antidepressant treatment guided by a predictive algorithm or treatment as usual (TaU). The primary outcome was the response of depression symptoms, defined as a 50% or greater reduction in baseline score of the QIDS-SR-16 scale, at week 8. Additional prespecified outcomes included symptoms of anxiety at week 8, and symptoms of depression and functional outcome at weeks 8, 24 and 48. The response rate of depressive symptoms at week 8 in the PReDicT (55.9%) and TaU (51.8%) arms did not differ significantly (odds ratio: 1.18 (95% CI: 0.89-1.56), P = 0.25). However, there was a significantly greater reduction of anxiety in week 8 and a greater improvement in functional outcome at week 24 in the PReDicT arm. Use of the PReDicT test did not increase the rate of response to antidepressant treatment estimated by depressive symptoms but did improve symptoms of anxiety at week 8 and functional outcome at week 24. Our findings indicate that personalisation of antidepressant treatment may improve outcomes in depressed patients.

The COVID-19 pandemic has led to unprecedented societal changes limiting us in our mobility and our ability to connect with others in person. These unusual but widespread changes provide a unique opportunity for studies using digital phenotyping tools. Digital phenotyping tools, such as mobile passive monitoring platforms (MPM), provide a new perspective on human behavior and hold promise to improve human behavioral research. However, there is currently little evidence that these tools can reliably detect changes in behavior. Considering the Considering the COVID-19 pandemic as a high impact common environmental factor we studied potential impact on behavior of participants using our mobile passive monitoring platform BEHAPP that was ambulatory tracking them during the COVID-19 pandemic. We pooled data from three MPM studies involving Schizophrenia (SZ), Major Depressive Disorder (MDD) and Bipolar Disorder (BD) patients (N = 12). We compared the data collected on weekdays during three weeks prior and three weeks subsequent to the start of the quarantine. We hypothesized an increase in communication and a decrease in mobility. We observed a significant increase in the total time spent on communication applications (median 179 and 243 min per week respectively, p = 0.005), and a significant decrease in the number of unique places visited (median 6 and 3 visits per week respectively, p = 0.007), while the total time spent at home did not change significantly (median 64 and 77 h per week, respectively, p = 0.594). The data provides a proof of principle that digital phenotyping tools can identify changes in human behavior incited by a common external environmental factor., Competing Interests: Conflict of Interest Authors declare no conflicts of interest., (Copyright © 2020. Published by Elsevier B.V.)

Despite major expansions of evidence-based treatments of common mental disorders in recent decades, especially antidepressant medication, the point prevalence of depression has not decreased; instead it probably increased in young adults. We question whether antidepressants (AD)-monotherapy and low-fidelity-to-guideline psychological treatment (PT) might have no effect or even adverse effects in some patients and contexts that dilute the benefits of treatment at the population level, making it harder for population-based studies to detect treatment-driven prevalence reductions. Randomized Clinical Trial (RCT)s have not identified these effects because AD-monotherapy and low-fidelity PT are uncommon in RCTs where treatment protocols are specified and carefully monitored, unlike treatment in real-world settings. Second, RCTs may have missed the bigger picture of ultimate outcomes due to too short follow-ups. We elaborate two mechanisms through which AD-monotherapy and low-fidelity PT could produce adverse effects on long-term illness course. Both mechanisms are speculative and we outline how to test.

Interruption or abrupt discontinuation of the use of antidepressants may lead to withdrawal symptoms. These are most common with selective serotonin reuptake inhibitors (SSRIs) and serotonin-noradrenaline reuptake inhibitors (SNRIs).There is insufficient scientific evidence about the prevalence of antidepressant withdrawal symptoms and how to optimally discontinue antidepressants. The multidisciplinary document 'Discontinuation of SSRIs & SNRIs' offers a rationale and suggestions for the gradual tapering of these antidepressants. The following factors are consistently named as risk factors for the occurrence of withdrawal symptoms: (a) the patient experiences withdrawal symptoms in case of non-compliance or skipped doses; (b) a previous attempt to stop was unsuccessful; and (c) the patient is being treated with higher doses than the smallest effective dose of SSRIs or SNRIs. In patients with one or more risk factors, a tapering schedule with non-linear dose-reduction steps should be considered. The speed at which these steps are taken, should be adjusted depending on occurrence of withdrawal symptoms. Shared decision-making by patient and physician is the best way to select a tapering schedule.

Introduction: Pharmacotherapy plays an important role in the treatment of major depression. At the initiation of antidepressant treatment, both improvement of symptoms in the short term and relapse prevention in the long term should be taken into account. However, there is insufficient evidence regarding the efficacy and the acceptability of continuation/maintenance treatments and the relative efficacy/acceptability of antidepressants., Objective: We will conduct a pairwise meta-analysis and a network meta-analysis (NMA) to examine the relative efficacy, tolerability and acceptability of antidepressants in the long-term treatment of major depression., Methods and Analysis: We will include double-blind randomised controlled trials comparing any of the following antidepressants, which we included in our previous NMA of the acute treatment for major depression, with placebo or with another active drug for long-term treatment of major depression: agomelatine, amitriptyline, bupropion, citalopram, clomipramine, desvenlafaxine, duloxetine, escitalopram, fluoxetine, fluvoxamine, levomilnacipran, milnacipran, mirtazapine, nefazodone, paroxetine, reboxetine, sertraline, trazodone, venlafaxine, vilazodone and vortioxetine. Our primary outcomes will be sustained response and all-cause dropouts. We will include four types of designs that are used to investigate long-term treatment. We will conduct two main analyses. First, we will conduct a pairwise meta-analysis comparing all antidepressants versus placebo to investigate whether continuing antidepressants after achieving a positive response in the acute-phase treatment is beneficial and/or safe. Second, we will conduct an NMA to examine the comparative efficacy and acceptability of the drugs. We will use a novel approach that will combine the results of acute-phase treatment NMA with long-term treatment studies to include all related designs in the NMA. We will ensure the validity of combining different designs and our new approach by checking the distribution of important effect modifiers and consistency of network., Ethics and Dissemination: This study did not require ethical approval. We will disseminate our findings by publishing results in a peer-reviewed journal., Prospero Registration Number: CRD42018114561; Pre-results., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

An interesting factor explaining recurrence risk in Major Depressive Disorder (MDD) may be neuropsychological functioning, i.e., processing of emotional stimuli/information. Negatively biased processing of emotional stimuli/information has been found in both acute and (inconclusively) remitted states of MDD, and may be causally related to recurrence of depression. We aimed to investigate self-referent, memory and interpretation biases in recurrently depressed patients in remission and relate these biases to recurrence. We included 69 remitted recurrent MDD-patients (rrMDD-patients), 35-65 years, with ≥2 episodes, voluntarily free of antidepressant maintenance therapy for at least 4 weeks. We tested self-referent biases with an emotional categorization task, bias in emotional memory by free recall of the emotion categorization task 15 min after completing it, and interpretation bias with a facial expression recognition task. We compared these participants with 43 never-depressed controls matched for age, sex and intelligence. We followed the rrMDD-patients for 2.5 years and assessed recurrent depressive episodes by structured interview. The rrMDD-patients showed biases toward emotionally negative stimuli, faster responses to negative self-relevant characteristics in the emotional categorization, better recognition of sad faces, worse recognition of neutral faces with more misclassifications as angry or disgusting faces and less misclassifications as neutral faces (0.001 < p < 0.05). Of these, the number of misclassifications as angry and the overall performance in the emotional memory task were significantly associated with the time to recurrence ( p ≤ 0.04), independent of residual symptoms and number of previous episodes. In a support vector machine data-driven model, prediction of recurrence-status could best be achieved (relative to observed recurrence-rate) with demographic and childhood adversity parameters (accuracy 78.1%; 1-sided p = 0.002); neuropsychological tests could not improve this prediction. Our data suggests a persisting (mood-incongruent) emotional bias when patients with recurrent depression are in remission. Moreover, these persisting biases might be mechanistically important for recurrence and prevention thereof.

Despite substantial advances in treatment and management strategies for major depression, less than 50% of patients respond to first-line antidepressant treatment or psychotherapy. Given the growing number of controlled studies of psychotherapy for treatment-resistant depression (TRD) and the preference for psychotherapy of depressed subjects as a treatment option, we conducted a meta-analysis and meta-regression analysis to investigate the effectiveness of psychotherapy for TRD. Seven different psychotherapies were studied in 21 trials that included a total of 25 comparisons. In three comparisons of psychotherapy v. treatment as usual (TAU) we found no evidence to conclude that there is a significant benefit of psychotherapy as compared with TAU. In 22 comparisons of add-on psychotherapy plus TAU v. TAU only, we found a moderate general effect size of 0.42 (95% CI 0.29-0.54) in favor of psychotherapy plus TAU. The meta-regression provided evidence for a positive association between baseline severity as well as group v. individual therapy format with the treatment effect. There was no evidence for publication bias. Most frequent investigated treatments were cognitive behavior therapy, interpersonal psychotherapy, mindfulness-based cognitive therapy, and cognitive behavioral analysis system of psychotherapy. Our meta-analysis provides evidence that, in addition to pharmacological and neurostimulatory treatments, the inclusion of add-on of psychotherapy to TAU in guidelines for the treatment of TRD is justified and will provide better outcomes for this difficult-to-treat population.

Objective: To present up-to-date information and recommendations on the management of body weight changes during the use of antiepileptic mood stabilizers in bipolar disorder to help clinicians and patients make well-informed, practical decisions., Data Sources: Umbrella review. Systematic reviews and meta-analyses on the prevention, treatment, and monitoring of body weight changes as a side effect of the mood stabilizers valproate, lamotrigine, topiramate, and carbamazepine were identified in Embase (2010-2015, no language restrictions)., Study Selection: The search yielded 18 relevant publications on antiepileptic mood stabilizers and weight changes in bipolar disorder., Data Extraction: Relevant scientific evidence was abstracted and put into a clinical perspective by a multidisciplinary expert panel of clinicians with expertise in the treatment of bipolar disorders across all age groups and a patient representative., Results: Valproate has been proven to be associated with weight gain in up to 50% of its users, and can be detected 2-3 months after initiation. Carbamazepine has been proven to have a low risk of weight gain. Lamotrigine and topiramate are associated with weight loss. Other option for this sentence = Weigth gain has been proven to be associated with valproate use in up to 50% of its users, and can be detected within 2-3 months after initiation., Conclusion: Each antiepileptic mood stabilizer has specific effects on body weight and accordingly requires a discrete education, prevention, monitoring, and treatment strategy. Clinicians are recommended to adopt an active, anticipatory approach, educating patients about weight change as an important side effect in order to come to informed shared decisions about the most suitable mood stabilizer.

In the original version of this article unfortunately two tables have been missing. By mistake they have been published as Supplementary Material. We apologize for any inconvenience caused. The original article has been corrected.

(Reprinted with permission from Lancet 2018; 391:1357-66)., (Copyright © 2018 by the American Psychiatric Association.)

Objectives: Cognitive reactivity (CR) to sad mood is a risk factor for major depressive disorder (MDD). CR is usually measured by assessing change on the Dysfunctional Attitudes Scale (DAS-change) after sad mood-induction. It has, however, been suggested that the versions of the DAS (A/B) are not interchangeable, impacting the reliability and validity of the change score. The Leiden Index of Depression Sensitivity-Revised (LEIDS-R) is an alternative self-report measure of CR. Studies examining the relationship between LEIDS-R and DAS-change have shown mixed results. We examined whether scores of these CR measures differed between remitted MDD and controls, the relationship between these CR measures, and the effect of order of DAS administration on DAS-change., Design: Cross-sectional design with two groups (remitted MDD and controls)., Methods: Sixty-eight MDD patients remitted from ≥2 previous episodes, not taking antidepressants, and 43 never-depressed controls participated in a mood-induction and filled in the DAS-A/B in randomized order before and after mood-induction, and LEIDS-R separately., Results: LEIDS-R scores and pre-mood-induction DAS scores were significantly higher in remitted MDD than controls (p < .001, Cohen's d = 1.48; p = .001, Cohen's d = 0.66, respectively). DAS-change did not differ between these groups (p = .67, Cohen's d = 0.08). LEIDS-R correlated with DAS-change (r = .30, p = .042), but only in the group that filled in DAS-B before DAS-A. In remitted MDD, DAS-change was dependent on the order of DAS versions before and after mood-induction (10.6 ± 19.0 vs. -1.2 ± 10.5, for order B-A and A-B, respectively), with a significant group × order interaction (p = .012)., Conclusions: Existing DAS versions are not interchangeable, which compromises the usefulness of mood-inductions in clinical practice. The LEIDS-R seems a valid measure of cognitive vulnerability to depression., Practitioner Points: Clinical implications: Cognitive reactivity (CR) is a risk factor of depressive recurrence. The current measurement of CR, by assessing change on the Dysfunctional Attitudes Scale (DAS) after mood-induction, is not reliable. The Leiden Index Depression Sensitivity-Revised (LEIDS-R) is an alternative CR measure. In contrast to mood-induction, it reliably assesses depression vulnerability. The use of mood-inductions for clinical/research purposes is unnecessary., Limitations of the Study: We were not able to examine the effect of previous treatment, which could have affected results as psychological treatments probably have differential effects on CR. Examining un-medicated patients may have led to selection of a sample not completely representative for the general MDD population. We did not administer both parallel versions of the DAS (A/B) before and after mood-induction. This might have provided better understanding of their differential sensitivity to change., (© 2018 The Authors. British Journal of Clinical Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.)

Objective: We investigated if the degree of treatment resistance of depression, as measured by the Maudsley Staging Method (MSM), is predictive of a worse depression outcome by using a large naturalistic cohort of depressed patients., Methods: 643 subjects from the general population, primary care, and secondary care who suffered from current depressive disorder were included from the Netherlands Study of Depression and Anxiety baseline assessment. The diagnostic criterion was major depressive disorder (MDD) in the last month, based on the Composite Interview Diagnostic Instrument (CIDI), or a CIDI diagnosis of MDD in the past 6 months with an Inventory of Depressive Symptomatology Self-Report score > 24 at baseline. In these subjects, composite scores of the MSM, based on duration, severity, and treatment history of current episode, were determined retrospectively. We then determined if the MSM score prospectively predicted the 2-year course of depression after baseline. The primary outcomes were percentage of follow-up time spent in a depressive episode and being "mostly depressed" (≥ 50% of the follow-up) between baseline and 2-year follow-up., Results: The MSM predicted "percentage of follow-up time with depression" (P < .001) and was associated with being "mostly depressed" (OR = 1.40; 95% CI, 1.23-1.60; P < .001). These effects were not modified by having received treatment., Conclusions: The current study shows that the MSM is a promising tool to predict worse depression outcomes in depressed patients. In this study that adds to previous work, we show the applicability of MSM in a wider range of primary and secondary care patients with depression., (© Copyright 2018 Physicians Postgraduate Press, Inc.)

Background and Objectives: In the proposed symptom network approach to psychopathology, psychiatric disorders are assumed to result from the (causal) interplay between symptoms. By implementing this approach we explored whether individual feedback on symptom dynamics complements current categorical classification and treatment. The aim of this proof-of-principle case-study was to explore the feasibility, acceptability and usability of this transdiagnostic approach., Methods: A female patient, aged 67, suffering from treatment resistant anxious and depressive symptoms was treated in our tertiary outpatient clinic for old age psychiatry. She participated in ecological momentary assessments (EMA), which involved intensive repeated measurements of mood and context-related items during two weeks. Visualizations of the interplay between the items were provided by network graphs and were discussed with the patient., Results: Network graphs were discussed with the patient. For example, it was hypothesized and discussed with the patient that feeling relaxed increased physical activity, causing physical discomfort in the following hours. Physical discomfort caused stress as its symptoms resembled her feared somatic anxiety symptoms. This increased the patient's insight that stress, expressed as somatic symptoms, played a central role in her panic disorder. This started a dialogue on how to cope with stress caused by somatic (anxiety) symptoms and provided a rationale for the patient to start an interoceptive exposure intervention she had repeatedly refused before., Limitations: The observed symptom dynamics may not be generalizable to any other random two weeks., Conclusions: Personalized diagnosis of psychopathology incorporating complex symptom dynamics is feasible and a promising addition to current categorical diagnostic systems and could guide intervention selection. This merits further exploration., (© Person-Oriented Research.)

Background: Treatment resistant depression (TRD) is common in daily practice. An empirical, widely accepted and applicable measure to quantify TRD is lacking. Previously, the Maudsley Staging Method (MSM) showed good validity. We aimed to improve the MSM by refining and extending its items resulting in the Dutch Measure for quantification of TRD (DM-TRD)., Methods: In addition to duration, severity and failed treatments in the current depressive episode, we added items for functional impairment, comorbid anxiety, personality disorders and psychosocial stressors. We extended the augmentation section and added items for failed psychotherapy and intensified treatment. We examined psychometric properties of the DM-TRD and tested prediction of future depressive symptoms and remission after 16 weeks in 274 (DSM-IV) depressed in- and outpatients entering naturalistic treatment., Results: The DM-TRD showed excellent inter-/intra-rater reliability. Higher scores were associated with more symptoms and less remission during follow-up. The DM-TRD outperformed the MSM in prediction of future depressive symptomatology. Remission was predicted equally well by both measures. Longer duration of the current episode, larger functional impairment and larger baseline symptom severity were the strongest predictors of symptomatology at follow-up. Longer duration and larger functional impairment were negatively associated with remission., Limitations: Longer follow-up could have increased predictive power. Addition of items for somatic co-morbidity, childhood adversity and psychotic features must be investigated further., Conclusion: The DM-TRD has excellent psychometric properties and better predictive validity for clinical outcome than other sophisticated measure published to date. Its use in clinical practice and research will improve treatment planning in TRD-patients., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Introduction: Many antidepressants are indicated for the treatment of major depression. Two network meta-analyses have provided the most comprehensive assessments to date, accounting for both direct and indirect comparisons; however, these reported conflicting interpretation of results. Here, we present a protocol for a systematic review and network meta-analysis aimed at updating the evidence base and comparing all second-generation as well as selected first-generation antidepressants in terms of efficacy and acceptability in the acute treatment of major depression., Methods and Analysis: We will include all randomised controlled trials reported as double-blind and comparing one active drug with another or with placebo in the acute phase treatment of major depression in adults. We are interested in comparing the following active agents: agomelatine, amitriptyline, bupropion, citalopram, clomipramine, desvenlafaxine, duloxetine, escitalopram, fluoxetine, fluvoxamine, levomilnacipran, milnacipran, mirtazapine, nefazodone, paroxetine, reboxetine, sertraline, trazodone, venlafaxine, vilazodone and vortioxetine. The main outcomes will be the proportion of patients who responded to or dropped out of the allocated treatment. Published and unpublished studies will be sought through relevant database searches, trial registries and websites; all reference selection and data extraction will be conducted by at least two independent reviewers. We will conduct a random effects network meta-analysis to synthesise all evidence for each outcome and obtain a comprehensive ranking of all treatments. To rank the various treatments for each outcome, we will use the surface under the cumulative ranking curve and the mean ranks. We will employ local as well as global methods to evaluate consistency. We will fit our model in a Bayesian framework using OpenBUGS, and produce results and various checks in Stata and R. We will also assess the quality of evidence contributing to network estimates of the main outcomes with the GRADE framework., Ethics and Dissemination: This review does not require ethical approval., Prospero Registration Number: CRD42012002291., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/)

D-amphetamine (dAMPH) and methylphenidate (MPH) are stimulants used in the treatment of Attention Deficit Hyperactivity Disorder (ADHD). Preclinical studies have shown that in healthy animals, dAMPH induces dopamine (DA) dysfunction, as evidenced for instance by loss of DA levels and its transporters. It has also been suggested that DA plays an important role in emotional processing, and that altered DA-ergic intervention may modulate amygdala function. To explore the role of the DA system in emotional processing we examined emotional processing using functional magnetic resonance imaging (fMRI) in eight male recreational users of dAMPH and eight male healthy controls. We compared brain activation between both groups during an emotional face-processing task with and without an oral MPH challenge. All subjects were abstinent for at least 2 weeks during the baseline scan. The second scan was performed on the same day 1½ hours after receiving an oral dose of 35 mg MPH. A significant Valence*Group interaction (p = .037) indicated amygdala hyperreactivity to fearful facial expressions in dAMPH users that was robust against adjustment for age (p = .015). Furthermore, duration of amphetamine use in years was positively correlated with amygdala reactivity in dAMPH users (r = .76; p = .029). These exploratory findings are in line with previous findings suggesting that DA plays a role in emotional processing.

Inhibitors of serotonin (5-HT) and/or noradrenaline (NA) reuptake have been developed for pharmacological treatment of major depressive disorder. Insights in the role of 5-HT and NA in the neurological control of the lower urinary tract have also led to their application in common urological conditions such as stress urinary incontinence (SUI), nocturnal enuresis and ejaculatory disorders. The European approval of the 5-HT and NA reuptake inhibitor (SNRI) duloxetine for treatment of SUI underlines the importance of a new approach in SUI, but has also given rise to questions about the safety of antidepressants in urology. This paper reviews the safety of 5-HT and NA reuptake inhibitors in their on- and off-label use in urology. A systematic Medline search was performed for randomised controlled trials, meta-analyses and practice guidelines dealing with antidepressants in urology. The safety profiles of the drugs in the urological population were compared with data from psychiatric populations. Tricyclic antidepressants are associated with serious cardiovascular side effects. In addition, anticholinergic and antihistaminic side effects are common. Although recently questions have been raised regarding the cardiovascular safety profile of venlafaxine, most selective 5-HT reuptake inhibitors and SNRI have not been associated with serious cardiovascular effects. Their most common side effect is nausea. However, nausea tends to be mild and, importantly, transient. Patient counselling about side effects and up-titrating doses may be useful strategies for minimising discomfort and withdrawals.