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Significance Major depressive disorder is a debilitating condition with diverse neuroimaging correlates, including cortical thinning in medial prefrontal cortex and altered functional connectivity of cortical association networks. However, the molecular bases of these imaging markers remain ambiguous, despite a need for treatment targets and mechanisms. Here, we advance cross-modal approaches to identify cell types and gene transcripts associated with depression-implicated cortex. Across multiple population-imaging datasets (combined N ≥ 23,723) and ex vivo patient cortical tissue, somatostatin interneurons and astrocytes emerge as replicable cell-level correlates of depression and negative affect. These data identify transcripts, cell types, and molecular processes associated with neuroimaging markers of depression and offer a roadmap for integrating in vivo clinical imaging with genetic and postmortem patient transcriptional data., Major depressive disorder emerges from the complex interactions of biological systems that span genes and molecules through cells, networks, and behavior. Establishing how neurobiological processes coalesce to contribute to depression requires a multiscale approach, encompassing measures of brain structure and function as well as genetic and cell-specific transcriptional data. Here, we examine anatomical (cortical thickness) and functional (functional variability, global brain connectivity) correlates of depression and negative affect across three population-imaging datasets: UK Biobank, Brain Genomics Superstruct Project, and Enhancing NeuroImaging through Meta Analysis (ENIGMA; combined n ≥ 23,723). Integrative analyses incorporate measures of cortical gene expression, postmortem patient transcriptional data, depression genome-wide association study (GWAS), and single-cell gene transcription. Neuroimaging correlates of depression and negative affect were consistent across three independent datasets. Linking ex vivo gene down-regulation with in vivo neuroimaging, we find that transcriptional correlates of depression imaging phenotypes track gene down-regulation in postmortem cortical samples of patients with depression. Integrated analysis of single-cell and Allen Human Brain Atlas expression data reveal somatostatin interneurons and astrocytes to be consistent cell associates of depression, through both in vivo imaging and ex vivo cortical gene dysregulation. Providing converging evidence for these observations, GWAS-derived polygenic risk for depression was enriched for genes expressed in interneurons, but not glia. Underscoring the translational potential of multiscale approaches, the transcriptional correlates of depression-linked brain function and structure were enriched for disorder-relevant molecular pathways. These findings bridge levels to connect specific genes, cell classes, and biological pathways to in vivo imaging correlates of depression.

Summary Background Better understanding of the heterogeneity of treatment responses could help to improve care for adolescents with depression. We analysed data from a clinical trial to assess whether specific symptom clusters responded differently to various treatments. Methods For this secondary analysis, we used data from the Treatment for Adolescents with Depression Study (TADS), in which 439 US adolescents aged 12–17 with a DSM-IV diagnosis of major depressive disorder and a minimum score of 45 on the Children's Depression Rating Scale-Revised (CDRS-R) were randomly assigned (1:1:1:1) to treatment with fluoxetine, cognitive behavioural therapy (CBT), fluoxetine plus CBT, or pill placebo. Our analysis focuses on the acute phase of the trial (ie, the first 12 weeks). Groups of co-occurring symptoms were established by clustering scores for each CDRS-R item at baseline with Ward's method, with Euclidean distances for hierarchical agglomerative clustering. We then used a linear mixed-effects model to investigate the relationship between symptom clusters and treatment efficacy, with the sum of symptom scores within each cluster as the dependent measure. As fixed effects, we entered cluster, time, and treatment assignment, with all two-way and three-way interactions, into the model. The random effect providing better fit was established to be a by-subject random slope for cluster based on improvement in the Schwarz-Bayesian information criterion. Outcomes We identified two symptom clusters: cluster 1 comprised depressed mood, difficulty having fun, irritability, social withdrawal, sleep disturbance, impaired schoolwork, excessive fatigue, and low self-esteem, and cluster 2 comprised increased appetite, physical complaints, excessive weeping, decreased appetite, excessive guilt, morbid ideation, and suicidal ideation. For cluster 1 symptoms, CDRS-R scores were reduced by 5·8 points (95% CI 2·8–8·9) in adolescents treated with fluoxetine plus CBT, and by 4·1 points (1·1–7·1) in those treated with fluoxetine, compared with those given placebo. For cluster 2 symptoms, no significant differences in improvements in CDRS-R scores were detected between the active treatment and placebo groups. Interpretation Response to fluoxetine and CBT among adolescents with depression is heterogeneous. Clinicians should consider clinical profile when selecting therapeutic modality. The contrast in response patterns between symptom clusters could provide opportunities to improve treatment efficacy by gearing the development of new therapies towards the resolution of specific symptoms. Funding Conselho Nacional de Desenvolvimento Cientifico e Tecnologico.

Studying individuals at increased genetic risk for schizophrenia may generate important theories regarding the emergence of the illness. In this investigation, genetic high-risk individuals (GHR, n = 37) were assessed with functional magnetic resonance imaging and compared to individuals in the first episode of schizophrenia (FESZ, n = 42) and healthy comparison subjects (HCS, n = 59). Measures of functional connectivity and the amplitude of low-frequency fluctuation (ALFF) were obtained in a global, data-driven analysis. The functional connectivity measure, termed degree centrality, assessed each voxel's connectivity with all the other voxels in the brain. GHR and FESZ displayed increased degree centrality globally and locally. On ALFF measures, GHR were indistinguishable from HCS in the majority of areas but resembled FESZ in insula, basal ganglia and hippocampus. FESZ evidenced reduced amplitude of the global neural signal as compared to HCS and GHR. Results support the hypothesis that schizophrenia diathesis involves functional connectivity and ALFF abnormalities. In addition, they further an emerging theory suggesting that increased connectivity and metabolism may be involved in schizophrenia vulnerability and early stages of the illness.

Psychiatry is a medical field concerned with the treatment of mental illness. Psychiatric disorders broadly relate to higher functions of the brain, and as such are richly intertwined with social, cultural, and experiential factors. This makes them exquisitely complex phenomena that depend on and interact with a large number of variables. Computational psychiatry provides two ways of approaching this complexity. Theory-driven computational approaches employ mechanistic models to make explicit hypotheses at multiple levels of analysis. Data-driven machine-learning approaches can make predictions from high-dimensional data and are generally agnostic as to the underlying mechanisms. Here, we review recent advances in the use of big data and machine-learning approaches toward the aim of alleviating the suffering that arises from psychiatric disorders.

Investment in workplace wellness programs is increasing despite concerns about lack of clinical benefit and return on investment (ROI). In contrast, outcomes from workplace mental health programs, which treat mental health difficulties more directly, remain mostly unknown.To determine whether participation in an employer-sponsored mental health benefit was associated with improvements in depression and anxiety, workplace productivity, and ROI as well as to examine factors associated with clinical improvement.This cohort study included participants in a US workplace mental health program implemented by 66 employers across 40 states from January 1, 2018, to January 1, 2021. Participants were employees who enrolled in the mental health benefit program and had at least moderate anxiety or depression, at least 1 appointment, and at least 2 outcome assessments.A digital platform that screened individuals for common mental health conditions and provided access to self-guided digital content, care navigation, and video and in-person psychotherapy and/or medication management.Primary outcomes were the Patient Health Questionnaire-9 for depression (range, 0-27) score and the Generalized Anxiety Disorder 7-item scale (range, 0-21) score. The ROI was calculated by comparing the cost of treatment to salary costs for time out of the workplace due to mental health symptoms, measured with the Sheehan Disability Scale. Data were collected through 6 months of follow-up and analyzed using mixed-effects regression.A total of 1132 participants (520 of 724 who reported gender [71.8%] were female; mean [SD] age, 32.9 [8.8] years) were included. Participants reported improvements from pretreatment to posttreatment in depression (b = -6.34; 95% CI, -6.76 to -5.91; Cohen d = -1.11; 95% CI, -1.18 to -1.03) and anxiety (b = -6.28; 95% CI, -6.77 to -5.91; Cohen d = -1.21; 95% CI, -1.30 to -1.13). Symptom change per log-day of treatment was similar post-COVID-19 vs pre-COVID-19 for depression (b = 0.14; 95% CI, -0.10 to 0.38) and anxiety (b = 0.08; 95% CI, -0.22 to 0.38). Workplace salary savings at 6 months at the federal median wage was US $3440 (95% CI, $2730-$4151) with positive ROI across all wage groups.Results of this cohort study suggest that an employer-sponsored workplace mental health program was associated with large clinical effect sizes for employees and positive financial ROI for employers.

Background Transcranial direct current stimulation (tDCS) presents small antidepressant efficacy at group level and considerable inter-individual variability of response. Its heterogeneous effects bring the need to investigate whether specific groups of patients submitted to tDCS could present comparable or larger improvement compared to pharmacotherapy. Aggregate measurements might be insufficient to address its effects. Objective /Hypothesis: To determine the efficacy of tDCS, compared to pharmacotherapy and placebo, in depressive symptom clusters. Methods Data from ELECT-TDCS (Escitalopram versus Electrical Direct-Current Therapy for Treating Depression Clinical Study, ClinicalTrials.gov , NCT01894815), in which antidepressant-free, depressed patients were randomized to receive 22 bifrontal tDCS (2 mA, 30 min) sessions (n = 94), escitalopram 20 mg/day (n = 91), or placebo (n = 60) over 10 weeks. Agglomerative hierarchical clustering identified “sleep/insomnia”, “core depressive”, “guilt/anxiety”, and “atypical” clusters that were the dependent measure. Trajectories were estimated using linear mixed regression models. Effect sizes are expressed in raw HAM-D units. P-values were adjusted for multiple comparisons. Results For core depressive symptoms, escitalopram was superior to tDCS (ES = −0.56; CI95% = -0.94 to −0.17, p = .009), which was superior to placebo (ES = 0.49; CI95% = 0.06 to 0.92, p = .042). TDCS but not escitalopram was superior to placebo in sleep/insomnia symptoms (ES = 0.87; CI95% = 0.22 to 1.52, p = .015). Escitalopram but not tDCS was superior to placebo in guilt/anxiety symptoms (ES = 1.66; CI95% = 0.58 to 2.75, p = .006). No active intervention was superior to placebo for atypical symptoms. Conclusions Pharmacotherapy and non-invasive brain stimulation produce distinct effects in depressive symptoms. TDCS or escitalopram could be chosen according to specific clusters of symptoms for a bigger response. Trial registration ClinicalTrials.gov, NCT01894815

Current first-line treatments for major depressive disorder (MDD) include pharmacotherapy and cognitive-behavioral therapy. However, one-third of depressed patients do not achieve remission after multiple medication trials, and psychotherapy can be costly and time-consuming. Although non-implantable neuromodulation (NIN) techniques such as transcranial magnetic stimulation, transcranial direct current stimulation, electroconvulsive therapy, and magnetic seizure therapy are gaining momentum for treating MDD, the efficacy of non-convulsive techniques is still modest, whereas use of convulsive modalities is limited by their cognitive side effects. In this context, we propose that NIN techniques could benefit from a precision-oriented approach. In this review, we discuss the challenges and opportunities in implementing such a framework, focusing on enhancing NIN effects via a combination of individualized cognitive interventions, using closed-loop approaches, identifying multimodal biomarkers, using computer electric field modeling to guide targeting and quantify dosage, and using machine learning algorithms to integrate data collected at multiple biological levels and identify clinical responders. Though promising, this framework is currently limited, as previous studies have employed small samples and did not sufficiently explore pathophysiological mechanisms associated with NIN response and side effects. Moreover, cost-effectiveness analyses have not been performed. Nevertheless, further advancements in clinical trials of NIN could shift the field toward a more "precision-oriented" practice.

Summary Background Exercise is known to be associated with reduced risk of all-cause mortality, cardiovascular disease, stroke, and diabetes, but its association with mental health remains unclear. We aimed to examine the association between exercise and mental health burden in a large sample, and to better understand the influence of exercise type, frequency, duration, and intensity. Methods In this cross-sectional study, we analysed data from 1 237 194 people aged 18 years or older in the USA from the 2011, 2013, and 2015 Centers for Disease Control and Prevention Behavioral Risk Factors Surveillance System survey. We compared the number of days of bad self-reported mental health between individuals who exercised and those who did not, using an exact non-parametric matching procedure to balance the two groups in terms of age, race, gender, marital status, income, education level, body-mass index category, self-reported physical health, and previous diagnosis of depression. We examined the effects of exercise type, duration, frequency, and intensity using regression methods adjusted for potential confounders, and did multiple sensitivity analyses. Findings Individuals who exercised had 1·49 (43·2%) fewer days of poor mental health in the past month than individuals who did not exercise but were otherwise matched for several physical and sociodemographic characteristics (W=7·42 × 1010, p Interpretation In a large US sample, physical exercise was significantly and meaningfully associated with self-reported mental health burden in the past month. More exercise was not always better. Differences as a function of exercise were large relative to other demographic variables such as education and income. Specific types, durations, and frequencies of exercise might be more effective clinical targets than others for reducing mental health burden, and merit interventional study. Funding Cloud computing resources were provided by Microsoft.

Schizophrenia research is plagued by enormous challenges in integrating and analyzing large datasets and difficulties developing formal theories related to the etiology, pathophysiology, and treatment of this disorder. Computational psychiatry provides a path to enhance analyses of these large and complex datasets and to promote the development and refinement of formal models for features of this disorder. This presentation introduces the reader to the notion of computational psychiatry and describes discovery-oriented and theory-driven applications to schizophrenia involving machine learning, reinforcement learning theory, and biophysically-informed neural circuit models.

OBJECTIVE: Develop and validate models using electronic health records to predict suicide attempt and suicide death following an outpatient visit. METHODS: Across seven health systems, 2,960,929 patients aged 13 or older (mean age 46, 62% female) made 10,275,853 specialty mental health visits and 9,685,206 primary care visits with mental health diagnoses between 1/1/2009 and 6/30/2015. Health system records and state death certificate data identified suicide attempts (n=24,133) and suicide deaths (n=1240) over 90 days following each visit. Potential predictors included 313 demographic and clinical characteristics extracted from records for up to five years prior to each visit: prior suicide attempts, mental health and substance use diagnoses, medical diagnoses, psychiatric medications dispensed, inpatient or emergency department care, and routinely administered PHQ-9 depression questionnaires. Logistic regression models predicting suicide attempt and death were developed using penalized LASSO variable selection in a random 65% sample of visits and validated in the remaining 35%. RESULTS: Mental health specialty visits with risk scores in the top 5% accounted for 43% of subsequent suicide attempts and 48% of suicide deaths. Of patients scoring in the top 5%, 5.4% attempted suicide and 0.26% died by suicide within 90 days. C-statistics (equivalent to AUC) for prediction of suicide attempt and suicide death were 0.851 (95% CI 0.848 to 0.853) and 0.861 (95% CI 0.848 to 0.875). Primary care visits with scores in the top 5% accounted for 48% of subsequent suicide attempts and 43% of suicide deaths. C-statistics for prediction of suicide attempt and suicide death were 0.853 (95% CI 0.849 to 0.857) and 0.833 (95% CI 0.813 to 0.853). CONCLUSIONS: Prediction models incorporating both health records data and responses to self-report questionnaires substantially outperform existing suicide risk prediction tools.

This work presents a novel approach to finding linkage/association between multimodal brain imaging data, such as structural MRI (sMRI) and functional MRI (fMRI). Motivated by the machine translation domain, we employ a deep learning model, and consider two different imaging views of the same brain like two different languages conveying some common facts. That analogy enables finding linkages between two modalities. The proposed translation-based fusion model contains a computing layer that learns "alignments" (or links) between dynamic connectivity features from fMRI data and static gray matter patterns from sMRI data. The approach is evaluated on a multi-site dataset consisting of eyes-closed resting state imaging data collected from 298 subjects (age- and gender matched 154 healthy controls and 144 patients with schizophrenia). Results are further confirmed on an independent dataset consisting of eyes-open resting state imaging data from 189 subjects (age- and gender matched 91 healthy controls and 98 patients with schizophrenia). We used dynamic functional connectivity (dFNC) states as the functional features and ICA-based sources from gray matter densities as the structural features. The dFNC states characterized by weakly correlated intrinsic connectivity networks (ICNs) were found to have stronger association with putamen and insular gray matter pattern, while the dFNC states of profuse strongly correlated ICNs exhibited stronger links with the gray matter pattern in precuneus, posterior cingulate cortex (PCC), and temporal cortex. Further investigation with the estimated link strength (or alignment score) showed significant group differences between healthy controls and patients with schizophrenia in several key regions including temporal lobe, and linked these to connectivity states showing less occupancy in healthy controls. Moreover, this novel approach revealed significant correlation between a cognitive score (attention/vigilance) and the function/structure alignment score that was not detected when data modalities were considered separately.

Objective: Even though safe and effective treatments for depression are available, many individuals with a diagnosis of depression do not obtain treatment. This study aimed to develop a tool to identify persons who might not initiate treatment among those who acknowledge a need.Methods: Data were aggregated from the 2008–2014 U.S. National Survey on Drug Use and Health (N=391,753), including 20,785 adults given a diagnosis of depression by a health care provider in the 12 months before the survey. Machine learning was applied to self-report survey items to develop strategies for identifying individuals who might not get needed treatment.Results: A derivation cohort aggregated between 2008 and 2013 was used to develop a model that identified the 30.6% of individuals with depression who reported needing but not getting treatment. When applied to independent responses from the 2014 cohort, the model identified 72% of those who did not initiate treatment (pConclusions: Considerable work is needed to improve follow-up and retention rates after the critical initial meeting in which a patient is given a diagnosis of depression. Routinely collected information about patients with depression could identify those at risk of not obtaining needed treatment, which may inform the development and implementation of interventions to reduce the prevalence of untreated depression.

Genetic risk variants for schizophrenia have been linked to many related clinical and biological phenotypes with the hopes of delineating how individual variation across thousands of variants corresponds to the clinical and etiologic heterogeneity within schizophrenia. This has primarily been done using risk score profiling, which aggregates effects across all variants into a single predictor. While effective, this method lacks flexibility in certain domains: risk scores cannot capture nonlinear effects and do not employ any variable selection. We used random forest, an algorithm with this flexibility designed to maximize predictive power, to predict 6 cognitive endophenotypes in a combined sample of psychiatric patients and controls (N = 739) using 77 genetic variants strongly associated with schizophrenia. Tenfold cross-validation was applied to the discovery sample and models were externally validated in an independent sample of similar ancestry (N = 336). Linear approaches, including linear regression and task-specific polygenic risk scores, were employed for comparison. Random forest models for processing speed (P = .019) and visual memory (P = .036) and risk scores developed for verbal (P = .042) and working memory (P = .037) successfully generalized to an independent sample with similar predictive strength and error. As such, we suggest that both methods may be useful for mapping a limited set of predetermined, disease-associated SNPs to related phenotypes. Incorporating random forest and other more flexible algorithms into genotype-phenotype mapping inquiries could contribute to parsing heterogeneity within schizophrenia; such algorithms can perform as well as standard methods and can capture a more comprehensive set of potential relationships.

In a recent report of the North American Prodrome Longitudinal Study (NAPLS), clinical high-risk individuals who converted to psychosis showed a steeper rate of cortical gray matter reduction compared with non-converters and healthy controls, and the rate of cortical thinning was correlated with levels of proinflammatory cytokines at baseline. These findings suggest a critical role for microglia, the resident macrophages in the brain, in perturbations of cortical maturation processes associated with onset of psychosis. Elucidating gene expression pathways promoting microglial action prior to disease onset would inform potential preventative intervention targets. Here we used a forward stepwise regression algorithm to build a classifier of baseline microRNA expression in peripheral leukocytes associated with annualized rate of cortical thinning in a subsample of the NAPLS cohort (N=74). Our cortical thinning classifier included nine microRNAs, p=3.63 × 10-08, R2=0.358, permutation-based p=0.039, the gene targets of which were enriched for intracellular signaling pathways that are important to coordinating inflammatory responses within immune cells (p

This article outlines how a core concept from theories of homeostasis and cybernetics, the inference-control loop, may be used to guide differential diagnosis in computational psychiatry and computational psychosomatics. In particular, we discuss 1) how conceptualizing perception and action as inference-control loops yields a joint computational perspective on brain-world and brain-body interactions and 2) how the concrete formulation of this loop as a hierarchical Bayesian model points to key computational quantities that inform a taxonomy of potential disease mechanisms. We consider the utility of this perspective for differential diagnosis in concrete clinical applications.

Depressive severity is typically measured according to total scores on questionnaires that include a diverse range of symptoms despite convincing evidence that depression is not a unitary construct. When evaluated according to aggregate measurements, treatment efficacy is generally modest and differences in efficacy between antidepressant therapies are small.To determine the efficacy of antidepressant treatments on empirically defined groups of symptoms and examine the replicability of these groups.Patient-reported data on patients with depression from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial (n = 4039) were used to identify clusters of symptoms in a depressive symptom checklist. The findings were then replicated using the Combining Medications to Enhance Depression Outcomes (CO-MED) trial (n = 640). Mixed-effects regression analysis was then performed to determine whether observed symptom clusters have differential response trajectories using intent-to-treat data from both trials (n = 4706) along with 7 additional placebo and active-comparator phase 3 trials of duloxetine (n = 2515). Finally, outcomes for each cluster were estimated separately using machine-learning approaches. The study was conducted from October 28, 2014, to May 19, 2016.Twelve items from the self-reported Quick Inventory of Depressive Symptomatology (QIDS-SR) scale and 14 items from the clinician-rated Hamilton Depression (HAM-D) rating scale. Higher scores on the measures indicate greater severity of the symptoms.Of the 4706 patients included in the first analysis, 1722 (36.6%) were male; mean (SD) age was 41.2 (13.3) years. Of the 2515 patients included in the second analysis, 855 (34.0%) were male; mean age was 42.65 (12.17) years. Three symptom clusters in the QIDS-SR scale were identified at baseline in STAR*D. This 3-cluster solution was replicated in CO-MED and was similar for the HAM-D scale. Antidepressants in general (8 of 9 treatments) were more effective for core emotional symptoms than for sleep or atypical symptoms. Differences in efficacy between drugs were often greater than the difference in efficacy between treatments and placebo. For example, high-dose duloxetine outperformed escitalopram in treating core emotional symptoms (effect size, 2.3 HAM-D points during 8 weeks, 95% CI, 1.6 to 3.1; P .001), but escitalopram was not significantly different from placebo (effect size, 0.03 HAM-D points; 95% CI, -0.7 to 0.8; P = .94).Two common checklists used to measure depressive severity can produce statistically reliable clusters of symptoms. These clusters differ in their responsiveness to treatment both within and across different antidepressant medications. Selecting the best drug for a given cluster may have a bigger benefit than that gained by use of an active compound vs a placebo.

Purpose The transition from medical school to residency is a critical step in the careers of physicians. Because of the standardized application process-wherein schools submit summative Medical Student Performance Evaluations (MSPE's)-it also represents a unique opportunity to assess the possible prevalence of racial and gender disparities, as shown elsewhere in medicine. Method The authors conducted textual analysis of MSPE's from 6,000 US students applying to 16 residency programs at a single institution in 2014-15. They used custom software to extract demographic data and keyword frequency from each MSPE. The main outcome measure was the proportion of applicants described using 24 pre-determined words from four thematic categories ("standout traits", "ability", "grindstone habits", and "compassion"). Results The data showed significant differences based on race and gender. White applicants were more likely to be described using "standout" or "ability" keywords (including "exceptional", "best", and "outstanding") while Black applicants were more likely to be described as "competent". These differences remained significant after controlling for United States Medical Licensing Examination Step 1 scores. Female applicants were more frequently described as "caring", "compassionate", and "empathic" or "empathetic". Women were also more frequently described as "bright" and "organized". Conclusions While the MSPE is intended to reflect an objective, summative assessment of students' qualifications, these data demonstrate for the first time systematic differences in how candidates are described based on racial/ethnic and gender group membership. Recognizing possible implicit biases and their potential impact is important for faculty who strive to create a more egalitarian medical community.

AIM: Paediatric opsoclonus-myoclonus syndrome (OMS) is a poorly understood condition with long-term cognitive, behavioural, and motor sequelae. Neuroimaging has indicated cerebellar atrophy in the chronic phase, but this alone may not explain the cognitive sequelae seen in many children with OMS. This study aimed to determine the extent of structural change throughout the brain that may underpin the range of clinical outcomes. METHOD: Nine participants with OMS (one male, eight females; mean age [SD] 14y, [6y 5mo], range 12-30y) and 10 comparison individuals (three males, seven females; mean age 12y 6mo, [4y 9mo], range 10-23y) underwent magnetic resonance imaging to acquire T1-weighted structural images, diffusion-weighted images, and magnetic resonance spectroscopy scans. Neuroblastoma had been present in four participants with OMS. Voxel-based morphometry was used to determine changes in grey matter volume, tract-based spatial statistics to analyze white matter integrity, and Freesurfer to analyze cortical thickness across visual and motor cortices. RESULTS: Whole-brain analysis indicated that cerebellar grey matter was significantly reduced in the patients with OMS, particularly in the vermis and flocculonodular lobe. A region-of-interest analysis indicated significantly lower cerebellar grey matter volume, particularly in patients with the greatest OMS scores. Diffusion-weighted images did not show effects at a whole brain level, but all major cerebellar tracts showed increased mean diffusivity when analysis was restricted to the cerebellum. Cortical thickness was reduced across the motor and visual areas in the OMS group, indicating involvement beyond the cerebellum. INTERPRETATION: Across individuals with OMS, there is considerable cerebellar atrophy, particularly in the vermis and flocculonodular lobes with atrophy severity associated with persistent symptomatology. Differences in cerebral cortical thickness indicate disease effects beyond the cerebellum.

Summary Background Understanding patterns of relapse in patients who respond to antidepressant treatment can inform strategies for prevention of relapse. We aimed to identify distinct trajectories of depression severity, assess whether similar or different trajectory classes exist for patients who continued or discontinued active treatment, and test whether clinical predictors of trajectory class membership exist using pooled data from clinical trials. Methods We analysed individual patient data from four double-blind discontinuation clinical trials of duloxetine or fluoxetine versus placebo in major depression from before 2012 (n=1462). We modelled trajectories of relapse up to 26 weeks during double-blind treatment. Trajectories of depression severity, as measured by the Hamilton Depression Rating Scale score, were identified in the entire sample, and separately in groups in which antidepressants had been continued or discontinued, using growth mixture models. Predictors of trajectory class membership were assessed with weighted logistic regression. Findings We identified similar relapse trajectories and two trajectories of stable depression scores in the normal range on active medication and on placebo. Active treatment significantly lowered the odds of membership in the relapse trajectory (odds ratio 0·47, 95% CI 0·37–0·61), whereas female sex (1·56, 1·23–2·06), shorter length of time with clinical response by 1 week (1·10, 1·06–1·15), and higher Clinical Global Impression score at baseline (1·28, 1·01–1·62) increased the odds. Overall, the protective effect of antidepressant medication relative to placebo on the risk of being classified as a relapser was about 13% (33% vs 46%). Interpretation The existence of similar relapse trajectories on active medication and on placebo suggests that there is no specific relapse signature associated with antidepressant discontinuation. Furthermore, continued treatment offers only modest protection against relapse. These data highlight the need to incorporate treatment strategies that prevent relapse as part of the treatment of depression. Funding National Institutes of Health, the US Department of Veterans Affairs Alcohol Research Center, and National Center for Post-Traumatic Stress Disorder.

Aim Opsoclonus–myoclonus syndrome (OMS) is a rare, poorly understood condition that can result in long-term cognitive, behavioural, and motor sequelae. Several studies have investigated structural brain changes associated with this condition, but little is known about changes in function. This study aimed to investigate changes in brain functional connectivity in patients with OMS. Method Seven patients with OMS and 10 age-matched comparison participants underwent 3T magnetic resonance imaging (MRI) to acquire resting-state functional MRI data (whole-brain echo-planar images; 2mm isotropic voxels; multiband factor ×2) for a cross-sectional study. A seed-based analysis identified brain regions in which signal changes over time correlated with the cerebellum. Model-free analysis was used to determine brain networks showing altered connectivity. Results In patients with OMS, the motor cortex showed significantly reduced connectivity, and the occipito-parietal region significantly increased connectivity with the cerebellum relative to the comparison group. A model-free analysis also showed extensive connectivity within a visual network, including the cerebellum and basal ganglia, not present in the comparison group. No other networks showed any differences between groups. Interpretation Patients with OMS showed reduced connectivity between the cerebellum and motor cortex, but increased connectivity with occipito-parietal regions. This pattern of change supports widespread brain involvement in OMS.

Background At present, no tools exist to estimate objectively the risk of poor treatment outcomes in patients with first-episode psychosis. Such tools could improve treatment by informing clinical decision-making before the commencement of treatment. We tested whether such a tool could be successfully built and validated using routinely available, patient-reportable information. Methods By applying machine learning to data from 334 patients in the European First Episode Schizophrenia Trial (EUFEST; International Clinical Trials Registry Platform number, ISRCTN68736636), we developed a tool to predict poor versus good treatment outcome (Global Assessment of Functioning [GAF] score >= 65 vs GAF

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.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).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]; p0·0001). 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.Building statistical models by mining existing clinical trial data can enable prospective identification of patients who are likely to respond to a specific antidepressant.Yale University.