Discontinuation of psychotropic medication: a synthesis of evidence across medication classes.

Pharmacotherapy is an effective treatment modality across psychiatric disorders. Nevertheless, many patients discontinue their medication at some point. Evidence-based guidance for patients, clinicians, and policymakers on rational discontinuation strategies is vital to enable the best, personalized treatment for any given patient. Nonetheless, there is a scarcity of guidelines on discontinuation strategies. In this perspective, we therefore summarize and critically appraise the evidence on discontinuation of six major psychotropic medication classes: antidepressants, antipsychotics, benzodiazepines, mood stabilizers, opioids, and stimulants. For each medication class, a wide range of topics pertaining to each of the following questions are discussed: (1) Who can discontinue (e.g., what are risk factors for relapse?); (2) When to discontinue (e.g., after 1 year or several years of antidepressant use?); and (3) How to discontinue (e.g., what's the efficacy of dose reduction compared to full cessation and interventions to mitigate relapse risk?). We thus highlight how comparing the evidence across medication classes can identify knowledge gaps, which may pave the way for more integrated research on discontinuation.

Treatment Response Prediction in Major Depressive Disorder Using Multimodal MRI and Clinical Data: Secondary Analysis of a Randomized Clinical Trial.

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.

From smartphone data to clinically relevant predictions: A systematic review of digital phenotyping methods in depression.

BACKGROUND: Smartphone-based digital phenotyping enables potentially clinically relevant information to be collected as individuals go about their day. This could improve monitoring and interventions for people with Major Depressive Disorder (MDD). The aim of this systematic review was to investigate current digital phenotyping features and methods used in MDD. METHODS: We searched PubMed, PsycINFO, Embase, Scopus and Web of Science (10/11/2023) for articles including: (1) MDD population, (2) smartphone-based features, (3) validated ratings. Risk of bias was assessed using several sources. Studies were compared within analysis goals (correlating features with depression, predicting symptom severity, diagnosis, mood state/episode, other). Twenty-four studies (9801 participants) were included. RESULTS: Studies achieved moderate performance. Common themes included challenges from complex and missing data (leading to a risk of bias), and a lack of external validation. DISCUSSION: Studies made progress towards relating digital phenotypes to clinical variables, often focusing on time-averaged features. Methods investigating temporal dynamics more directly may be beneficial for patient monitoring. European Research Council consolidator grant: 101001118, Prospero: CRD42022346264, Open Science Framework: https://osf.io/s7ay4.

Functional MRI correlates of emotion regulation in major depressive disorder related to depressive disease load measured over nine years.

Major Depressive Disorder (MDD) often is a recurrent and chronic disorder. We investigated the neurocognitive underpinnings of the incremental risk for poor disease course by exploring relations between enduring depression and brain functioning during regulation of negative and positive emotions using cognitive reappraisal. We used fMRI-data from the longitudinal Netherlands Study of Depression and Anxiety acquired during an emotion regulation task in 77 individuals with MDD. Task-related brain activity was related to disease load, calculated from presence and severity of depression in the preceding nine years. Additionally, we explored task related brain-connectivity. Brain functioning in individuals with MDD was further compared to 35 controls to explore overlap between load-effects and general effects related to MDD history/presence. Disease load was not associated with changes in affect or with brain activity, but with connectivity between areas essential for processing, integrating and regulating emotional information during downregulation of negative emotions. Results did not overlap with general MDD-effects. Instead, MDD was generally associated with lower parietal activity during downregulation of negative emotions. During upregulation of positive emotions, disease load was related to connectivity between limbic regions (although driven by symptomatic state), and connectivity between frontal, insular and thalamic regions was lower in MDD (vs controls). Results suggest that previous depressive load relates to brain connectivity in relevant networks during downregulation of negative emotions. These abnormalities do not overlap with disease-general abnormalities and could foster an incremental vulnerability to recurrence or chronicity of MDD. Therefore, optimizing emotion regulation is a promising therapeutic target for improving long-term MDD course.

Neurometabolite changes in response to antidepressant medication: A systematic review of 1H-MRS findings.

Selective serotonin reuptake inhibitors (SSRIs), serotonin and noradrenaline reuptake inhibitors (SNRIs), and (es)ketamine are used to treat major depressive disorder (MDD). These different types of medication may involve common neural pathways related to glutamatergic and GABAergic neurotransmitter systems, both of which have been implicated in MDD pathology. We conducted a systematic review of pharmacological proton Magnetic Resonance Spectroscopy (1H-MRS) studies in healthy volunteers and individuals with MDD to explore the potential impact of these medications on glutamatergic and GABAergic systems. We searched PubMed, Web of Science and Embase and included randomized controlled trials or cohort studies, which assessed the effects of SSRIs, SNRIs, or (es)ketamine on glutamate, glutamine, Glx or GABA using single-voxel 1H-MRS or Magnetic Resonance Spectroscopic Imaging (MRSI). Additionally, studies were included when they used a field strength > 1.5 T, and when a comparison of metabolite levels between antidepressant treatment and placebo or baseline with post-medication metabolite levels was done. We excluded animal studies, duplicate publications, or articles with 1H-MRS data already described in another included article. Twenty-nine studies were included in this review. Fifteen studies investigated the effect of administration or treatment with SSRIs or SNRIs, and fourteen studies investigated the effect of (es)ketamine on glutamatergic and GABAergic metabolite levels. Studies on SSRIs and SNRIs were highly variable, generally underpowered, and yielded no consistent findings across brain regions or specific populations. Although studies on (es)ketamine were also highly variable, some demonstrated an increase in glutamate levels in the anterior cingulate cortex in a time-dependent manner after administration. Our findings highlight the need for standardized study and acquisition protocols. Additionally, measuring metabolites dynamically over time or combining 1H-MRS with whole brain functional imaging techniques could provide valuable insights into the effects of these medications on glutamate and GABAergic neurometabolism.

Depression recurrence is accompanied by longer periods in default mode and more frequent attentional and reward processing dynamic brain-states during resting-state activity.

Recurrence in major depressive disorder (MDD) is common, but neurobiological models capturing vulnerability for recurrences are scarce. Disturbances in multiple resting-state networks have been linked to MDD, but most approaches focus on stable (vs. dynamic) network characteristics. We investigated how the brain's dynamical repertoire changes after patients transition from remission to recurrence of a new depressive episode. Sixty two drug-free, MDD-patients with ≥2 episodes underwent a baseline resting-state fMRI scan when in remission. Over 30-months follow-up, 11 patients with a recurrence and 17 matched-remitted MDD-patients without a recurrence underwent a second fMRI scan. Recurrent patterns of functional connectivity were characterized by applying Leading Eigenvector Dynamics Analysis (LEiDA). Differences between baseline and follow-up were identified for the 11 non-remitted patients, while data from the 17 matched-remitted patients was used as a validation dataset. After the transition into a depressive state, basal ganglia-anterior cingulate cortex (ACC) and visuo-attentional networks were detected significantly more often, whereas default mode network activity was found to have a longer duration. Additionally, the fMRI signal in the basal ganglia-ACC areas underlying the reward network, were significantly less synchronized with the rest of the brain after recurrence (compared to a state of remission). No significant changes were observed in the matched-remitted patients who were scanned twice while in remission. These findings characterize changes that may be associated with the transition from remission to recurrence and provide initial evidence of altered dynamical exploration of the brain's repertoire of functional networks when a recurrent depressive episode occurs.

On the Origins of MAOI Misconceptions: Reaffirming their Role in Melancholic Depression.

The first monoamine oxidase inhibitors (MAOIs) used for the treatment of depression in the 1950-60s were credited with treating severe melancholic depression (MeD) successfully and greatly reducing the need for electroconvulsive therapy (ECT). Following the hiatus caused by the then ill-understood cheese reaction, MAOI use was relegated to atypical and treatment-resistant depressions only, based on data from insufficiently probing research studies suggesting their comparatively lesser effectiveness in MeD. The siren attraction of new 'better' drugs with different mechanisms amplified this trend. Following a re-evaluation of the data, we suggest that MAOIs are effective in MeD. Additionally, the broad unitary conceptualisation of major depressive disorder (MDD) in the DSM model diminished the chance of demonstrating distinctive responses to different antidepressant drugs (ADs) such as SSRIs, TCAs, and MAOIs, thereby further reducing the interest in MAOIs. More reliable categorical distinction of MeD, disentangling it from MDD, may be possible if more sensitive measuring instruments (CORE, SMPI) are used. We suggest these issues will benefit from re-appraisement via an inductive reasoning process within a binary (rather than a unitary) model for defining the different depressive disorders, allowing for the use of more reliable diagnostic criteria for MeD in particular. We conclude that MAOIs remain essential for, inter alia, TCA-resistant MeD, and should typically be used prior to ECT; additionally, they have a role in maintaining remission in cases treated with ECT (and ketamine/esketamine). We suggest that MAOIs should be utilized earlier in treatment algorithms and with greater regularity than is presently the case.

[Consider (es)ketamine for treatment-resistant depression]. / Overweeg (es)ketamine bij therapieresistente depressie.

Major depressive disorder has a high prevalence globally. Although pharmacotherapy and psychotherapy are effective for most patients, about one third is treatment resistant. Ketamine, known as an anesthetic, is a new treatment option that can be effective in patients with treatment-resistant depression. (es)ketamine works relatively fast. However, the long-term effects are still relatively unknown. In the Netherlands, S-Ketamine is currently administered in various forms, of which only the nasal spray is registered for treatment-resistant depression. Currently, many studies have been conducted on the use of (es)ketamine. In this article we describe the latest state of affairs regarding its effectiveness and safety.

Efficacy and safety of selegiline across different psychiatric disorders: A systematic review and meta-analysis of oral and transdermal formulations.

Selegiline is an irreversible, selective type-B monoamine oxidase inhibitor (MAOI) approved for Parkison's disease-oral and major depressive disorder-transdermal formulation) resulting in non-selective MAOI activity at oral doses≥20 mg/day. The present systematic review and meta-analysis appraises the evidence of different formulations/dosages of selegiline across different psychiatric conditions. We inquired PubMed/MEDLINE/Cochrane-Central/WHO-ICTRP/Clarivate-WebOfScience and the Chinese-Electronic-Journal Database from inception to 10/26/2022 for selegiline trials involving psychiatric patients. Random-effects meta-analyses assessed heterogeneity, publication/risk biases, and confidence in the evidence, followed by sensitivity, subgroup, and meta-regression analyses. Co-primary outcomes were: changes in symptom score (standardized mean difference=SMD) and author-defined response (risk ratios=RRs). RRs of adverse events and all-cause discontinuation were secondary and acceptability outcomes, respectively. Systematic-review included 42 studies; meta-analysis, 23. Selegiline outperformed placebo in depressive symptom reduction (SMD=-0.96, 95%C.I.=-1.78, -0.14, k = 10, n = 1,308), depression (RR=1.61, 95%C.I.=1.20, 2.15, k = 9, n = 1,238) and atypical-depression response (RR=2.23, 95%C.I.=1.35, 3.68, k = 3, n = 136). Selegiline failed to outperform the placebo in negative (k = 4) or positive symptoms of schizophrenia (k = 4), attention-deficit-hyperactivity disorder (ADHD) symptoms reduction (k = 2), and smoking abstinence rate (k = 4). Selegiline did not differ from methylphenidate and ADHD scores (k = 2). No significant difference emerged in acceptability, incident diarrhea, headache, dizziness, and nausea RRs, in contrast to xerostomia (RR=1.58, 95%C.I. =1.03, 2.43, k = 6, n = 1,134), insomnia (RR=1.61, 95%C.I.=1.19, 2.17, k = 10, n = 1,768), and application-site reaction for transdermal formulation (RR=1.81, 95%C.I.=1.40, 2.33, k = 6, n = 1,662). Confidence in findings was low/very-low for most outcomes; moderate for depressive symptoms reduction (transdermal). Selegiline proved effective, safe, and well-tolerated for depressive disorders, yet further evidence is warranted about specific psychiatric disorders.

Evidence for embracing normative modeling.

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.

Decreased reward circuit connectivity during reward anticipation in major depression.

An important symptom of major depressive disorder (MDD) is the inability to experience pleasure, possibly due to a dysfunction of the reward system. Despite promising insights regarding impaired reward-related processing in MDD, circuit-level abnormalities remain largely unexplored. Furthermore, whereas studies contrasting experimental conditions from incentive tasks have revealed important information about reward processing, temporal difference modeling of reward-related prediction error (PE) signals might give a more accurate representation of the reward system. We used a monetary incentive delay task during functional MRI scanning to explore PE-related striatal and ventral tegmental area (VTA) activation in response to anticipation and delivery of monetary rewards in 24 individuals with MDD versus 24 healthy controls (HCs). Furthermore, we investigated group differences in temporal difference related connectivity with a generalized psychophysiological interaction (gPPI) analysis with the VTA, ventral striatum (VS) and dorsal striatum (DS) as seeds during reward versus neutral, both in anticipation and delivery. Relative to HCs, MDD patients displayed a trend-level (p = 0.052) decrease in temporal difference-related activation in the VS during reward anticipation and delivery combined. Moreover, gPPI analyses revealed that during reward anticipation, MDD patients exhibited decreased functional connectivity between the VS and anterior cingulate cortex / medial prefrontal cortex, anterior cingulate gyrus, angular/middle orbital gyrus, left insula, superior/middle frontal gyrus (SFG/MFG) and precuneus/superior occipital gyrus/cerebellum compared to HC. Moreover, MDD patients showed decreased functional connectivity between the VTA and left insula compared to HC during reward anticipation. Exploratory analysis separating medication free patients from patients using antidepressant revealed that these decreased functional connectivity patterns were mainly apparent in the MDD group that used antidepressants. These results suggest that MDD is characterized by alterations in reward circuit connectivity rather than isolated activation impairments. These findings represent an important extension of the existing literature since improved understanding of neural pathways underlying depression-related reward dysfunctions, may help currently unmet diagnostic and therapeutic efforts.

The association between sample and treatment characteristics and the efficacy of repetitive transcranial magnetic stimulation in depression: A meta-analysis and meta-regression of sham-controlled trials.

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is a form of non-invasive neuromodulation that is increasingly used to treat major depressive disorder (MDD). However, treatment with rTMS could be optimized by identifying optimal treatment parameters or characteristics of patients that are most likely to benefit. This meta-analysis and meta-regression aims to identify sample and treatment characteristics that are associated with change in depressive symptom level, treatment response and remission. METHODS: The databases PubMed, Embase, Web of Science and Cochrane library were searched for randomized controlled trials (RCTs) reporting on the therapeutic efficacy of high-frequent, low-frequent, or bilateral rTMS for MDD compared to sham. Study and sample characteristics as well as rTMS parameters and outcome variables were extracted. Effect sizes were calculated for change in depression score and risk ratios for response and remission. RESULTS: Sixty-five RCTs with a total of 2982 subjects were included in this meta-analysis. Active rTMS resulted in a larger depressive symptom reduction than sham protocol (Hedges' g = -0.791 95% CI -0.977; -0.605). Risk ratios for response and remission were 2.378 (95% CI 1.882; 3.005) and 2.450 (95% CI 1.779; 3.375), respectively. We found no significant association between sample and treatment parameters and rTMS efficacy. CONCLUSIONS: rTMS is an efficacious treatment for MDD. No associations between sample or treatment characteristics and efficacy were found, for which we caution that publication bias, heterogeneity and lack of consistency in the definition of remission might bias these latter null findings. Our results are clinically relevant and support the use of rTMS as a non-invasive and effective treatment option for depression.

The prescriber's guide to classic MAO inhibitors (phenelzine, tranylcypromine, isocarboxazid) for treatment-resistant depression.

This article is a clinical guide which discusses the "state-of-the-art" usage of the classic monoamine oxidase inhibitor (MAOI) antidepressants (phenelzine, tranylcypromine, and isocarboxazid) in modern psychiatric practice. The guide is for all clinicians, including those who may not be experienced MAOI prescribers. It discusses indications, drug-drug interactions, side-effect management, and the safety of various augmentation strategies. There is a clear and broad consensus (more than 70 international expert endorsers), based on 6 decades of experience, for the recommendations herein exposited. They are based on empirical evidence and expert opinion-this guide is presented as a new specialist-consensus standard. The guide provides practical clinical advice, and is the basis for the rational use of these drugs, particularly because it improves and updates knowledge, and corrects the various misconceptions that have hitherto been prominent in the literature, partly due to insufficient knowledge of pharmacology. The guide suggests that MAOIs should always be considered in cases of treatment-resistant depression (including those melancholic in nature), and prior to electroconvulsive therapy-while taking into account of patient preference. In selected cases, they may be considered earlier in the treatment algorithm than has previously been customary, and should not be regarded as drugs of last resort; they may prove decisively effective when many other treatments have failed. The guide clarifies key points on the concomitant use of incorrectly proscribed drugs such as methylphenidate and some tricyclic antidepressants. It also illustrates the straightforward "bridging" methods that may be used to transition simply and safely from other antidepressants to MAOIs.

The normative modeling framework for computational psychiatry.

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.

Negative cognitive schema modification as mediator of symptom improvement after electroconvulsive therapy in major depressive disorder.

BACKGROUND: Electroconvulsive therapy (ECT) is a potent option for treatment-resistant major depressive disorder (MDD). Cognitive models of depression posit that negative cognitions and underlying all-or-nothing negative schemas contribute to and perpetuate depressed mood. This study investigates whether ECT can modify negative schemas, potentially via memory reactivation, and whether such changes are related to MDD symptom improvement. METHOD: Seventy-two patients were randomized to either an emotional memory reactivation electroconvulsive therapy (EMR-ECT) or control memory reactivation electroconvulsive therapy (CMR-ECT) intervention prior to ECT-sessions in a randomized controlled trail. Emotional memories associated with patients' depression were reactivated before ECT-sessions. At baseline and after the ECT-course, negative schemas and depression severity were assessed using the Dysfunctional Attitude Scale (DAS) and Hamilton Depression Rating Scale HDRS. Mediation analyses were used to examine whether the effects of ECT on HDRS-scores were mediated by changes in DAS-scores or vice versa. RESULTS: Post-ECT DAS-scores were significantly lower compared to baseline. Post-ECT, the mean HDRS-score of the whole sample (15.10 ± 8.65 [SD]; n = 59) was lower compared to baseline (24.83 ± 5.91 [SD]). Multiple regression analysis showed no significant influence of memory reactivation on schema improvement. Path analysis showed that depression improvement was mediated by improvement of negative cognitive schemas. CONCLUSION: ECT is associated with improvement of negative schemas, which appears to mediate the improvement of depressive symptoms. An emotional memory intervention aimed to modify negative schemas showed no additional effect.

Suicidal ideation in remitted major depressive disorder predicts recurrence.

INTRODUCTION: Each year almost 800.000 people die from suicide, of which up to 87% are affected by major depressive disorder (MDD). Despite the strong association between suicidality and MDD, it remains unknown if suicidal symptoms during remission put remitted recurrent MDD patients (rrMDD) at risk for recurrence. METHODS: At baseline we compared sociodemographic characteristics and suicidal symptoms in un-medicated rrMDD participants to matched never-depressed controls. We used the HDRS17 and IDS-SR30 to assess suicidal symptoms and depressive symptomatology. Next, we studied the longitudinal association between baseline suicidal symptoms and time to recurrence(s) in rrMDD during a 2.5-year follow-up period using cox regression analyses. Further, we studied with longitudinal data whether suicidal symptoms and depressive symptomatology were cross-sectionally associated using mixed model analysis. RESULTS: At baseline, rrMDD participants (N = 73) had higher self-reported suicidal symptoms than matched never-depressed controls (N = 45) (χ2 = 12.09 p < .002). Self-reported suicidal symptoms were almost four times higher (27.9% versus 6.9%) compared to clinician-rated suicidal symptoms in rrMDD at baseline. Self-reported baseline suicidal symptoms, but not clinician-rated symptoms, predicted earlier MDD-recurrence during follow-up, independent of other residual depressive symptoms (χ2 = 7.26, p < .026). Higher suicidal symptoms were longitudinally related to higher depressive symptoms (HDRS17; F = 49.87, p < .001), IDS-SR30; (F = 22.36, p < .001). CONCLUSION: This study showed that self-reported - but not clinician-rated - suicidal symptoms persist during remission in rrMDD and predict recurrence, independent from residual symptoms. We recommend to monitor both suicidal and depressive symptomatology during remission in rrMDD, preferably also including self-reported questionnaires apart from clinician-rated. It would be beneficial for future research to assess suicidality using questionnaires primarily designed for measuring suicidal ideation.

Efficacy and acceptability of next step treatment strategies in adults with treatment-resistant major depressive disorder: protocol for systematic review and network meta-analysis.

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.

Charting brain growth and aging at high spatial precision.

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.

Factors associated with relapse and recurrence of major depressive disorder in patients starting mindfulness-based cognitive therapy.

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.