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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., This review discusses the major challenges facing neuroimaging research of bipolar disorder and highlights the major accomplishments, ongoing challenges and future goals of the ENIGMA Bipolar Disorder Working Group.

Investigators in neuroscience have turned to Big Data to address replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. These efforts unveil new questions about integrating data arising from distinct sources and instruments. We focus on the most frequently assessed cognitive domain - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated global raw data from 53 studies totaling N = 10,505 individuals. A mega-analysis was conducted using empirical bayes harmonization to remove site effects, followed by linear models adjusting for common covariates. A continuous item response theory (IRT) model estimated each individual’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance while preserving covariate effects, and our conversion tool is freely available online. This demonstrates that large-scale data sharing and harmonization initiatives can address reproducibility and integration challenges across the behavioral sciences.TeaserWe present a global effort to devise harmonization procedures necessary to meaningfully leverage big data.

Objective: To propose a method capable of disentangling the magnitude, the speed, and the duration or decay rate of the time course of response to rapid antidepressant therapies. Methods: We introduce a computational model of the time course of response to a single treatment with a rapid antidepressant. Numerical simulation is used to evaluate whether model parameters can be accurately estimated from observed data. Finally, we compare our computational modelling-based approach with linear mixed effects modelling in terms of their ability to detect changes in the magnitude and time-course of response to rapid antidepressant therapies in simulated randomized trials. Results: Simulation experiments show that the parameters of our computational model can be accurately recovered using nonlinear least squares. Parameter estimation accuracy is stable over noise levels reaching as high as 25% of the true antidepressant effect magnitude. Comparison of our approach to mixed effects modelling using simulated randomized controlled trial data demonstrates an inability of linear mixed models to disentangle effect magnitude and time course, while our computational model accurately separates these response components. Conclusions: Our modelling approach may accurately identify the (A) magnitude, (B) speed, and (C) durability or decay rate of response to rapid antidepressant therapies. Future studies should fit this model to data from real clinical trials, and use resulting parameter estimates to uncover predictors and causes of different elements of the temporal course of antidepressant response.

An improved understanding of genetic etiological heterogeneity in a psychiatric condition may help us (a) isolate a neurophysiological 'final common pathway' by identifying its upstream genetic origins and (b) facilitate characterization of the condition's phenotypic variation. This review aims to identify existing genetic heterogeneity measurements in the psychiatric literature and provides a conceptual review of their mechanisms, limitations, and assumptions. The Scopus database was searched for studies that quantified genetic heterogeneity or correlation of psychiatric phenotypes with human genetic data. Ninety studies were included. Eighty-seven reports quantified genetic correlation, five applied genomic structural equation modelling, three evaluated departure from the Hardy-Weinberg equilibrium at one or more loci, and two applied a novel approach known as MiXeR. We found no study that rigorously measured genetic etiological heterogeneity across a large number of markers. Developing such approaches may help better characterize the biological diversity of psychopathology.

Objective This study examined the feasibility, safety, and potential efficacy of lisdexamfetamine (LDX) as a treatment for adults with bulimia nervosa (BN). Method An open-label 8-week feasibility study was conducted in participants with BN. Enrollment rate, dropout rate, safety outcomes, and eating disorder symptom change were examined. Results Eighteen of 23 participants completed the study per protocol. There was no participant-initiated dropout due to adverse drug reactions and no severe and unexpected adverse drug reactions. An average increase in heart rate of 12.1 beats/min was observed. There was a mean weight reduction of 2.1 kg and one participant was withdrawn for clinically significant weight loss. In the intent-to-treat sample, there were reductions in objective binge episodes and compensatory behaviors from Baseline to Post/End-of-Treatment (mean difference = -29.83, 95% confidence interval: -43.38 to -16.27; and mean difference = -33.78, 95% confidence interval: -48.74 to -18.82, respectively). Discussion Results of this study indicate that a randomized controlled trial would be feasible with close monitoring of certain safety parameters (especially over a longer time period as long-term safety is unknown). However, the results should not be used as evidence for clinicians to prescribe LDX to individuals with BN before its efficacy and safety are properly tested. Trial registration number NCT03397446.

Background Many individuals with eating disorders remain symptomatic after a course of psychotherapy and pharmacotherapy; therefore, the development of innovative treatments is essential. Method To learn more about the current evidence for treating eating disorders with stimulants, we searched for original articles and reviews published up to April 29, 2021 in PubMed and MEDLINE using the following search terms: eating disorders, anorexia, bulimia, binge eating, stimulants, amphetamine, lisdexamfetamine, methylphenidate, and phentermine. Results We propose that stimulant medications represent a novel avenue for future research based on the following: (a) the relationship between eating disorders and attention deficit/hyperactivity disorder (ADHD); (b) a neurobiological rationale; and (c) the current (but limited) evidence for stimulants as treatments for some eating disorders. Despite the possible benefits of such medications, there are also risks to consider such as medication misuse, adverse cardiovascular events, and reduction of appetite and pathological weight loss. With those risks in mind, we propose several directions for future research including: (a) randomized controlled trials to study stimulant treatment in those with bulimia nervosa (with guidance on strategies to mitigate risk); (b) examining stimulant treatment in conjunction with psychotherapy; (c) investigating the impact of stimulants on "loss of control" eating in youth with ADHD; and (d) exploring relevant neurobiological mechanisms. We also propose specific directions for exploring mediators and moderators in future clinical trials. Discussion Although this line of investigation may be viewed as controversial by some in the field, we believe that the topic warrants careful consideration for future research.

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47–67.00, ROC-AUC = 71.49%, 95% CI = 69.39–73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70–60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen’s Kappa = 0.83, 95% CI = 0.829–0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.

Objective Promptly establishing maintenance therapy could reduce morbidity and mortality in patients with bipolar disorder. Using a machine learning approach, we sought to evaluate whether lithium responsiveness (LR) is predictable using clinical markers. Method Our data are the largest existing sample of direct interview-based clinical data from lithium-treated patients (n = 1266, 34.7% responders), collected across seven sites, internationally. We trained a random forest model to classify LR-as defined by the previously validated Alda scale-against 180 clinical predictors. Results Under appropriate cross-validation procedures, LR was predictable in the pooled sample with an area under the receiver operating characteristic curve of 0.80 (95% CI 0.78-0.82) and a Cohen kappa of 0.46 (0.4-0.51). The model demonstrated a particularly low false-positive rate (specificity 0.91 [0.88-0.92]). Features related to clinical course and the absence of rapid cycling appeared consistently informative. Conclusion Clinical data can inform out-of-sample LR prediction to a potentially clinically relevant degree. Despite the relevance of clinical course and the absence of rapid cycling, there was substantial between-site heterogeneity with respect to feature importance. Future work must focus on improving classification of true positives, better characterizing between- and within-site heterogeneity, and further testing such models on new external datasets.

Outpatient care (e.g., individual, group, or self-help therapies) and day treatment programs (DTPs) are common and effective treatments for adults with eating disorders. Compared to outpatient care, DTPs have additional expenses and could have unintended iatrogenic effects (e.g., may create an overly protective environment that undermines self-efficacy). However, these potential downsides may be offset if DTPs are shown to have advantages over outpatient care. To explore this question, our team conducted a scoping review that aimed to synthesize the existing body of adult eating disorder literature (a) comparing outcomes for DTPs to outpatient care, and (b) examining the use of DTPs as a higher level of care in a stepped care model. Only four studies met the predefined search criteria. The limited results suggest that the treatments have similar effects and that outpatient care is more cost-effective. Furthermore, no studies explored the use of DTPs as a higher level of care in a stepped care model (despite international guidelines recommending this approach). Given the clear dearth of literature on this clinically relevant topic, we have provided specific avenues for further research.

Predicting lithium response prior to treatment could both expedite therapy and avoid exposure to side effects. Since lithium responsiveness may be heritable, its predictability based on genomic data is of interest. We thus evaluate the degree to which lithium response can be predicted with a machine learning (ML) approach using genomic data. Using the largest existing genomic dataset in the lithium response literature (n = 2210 across 14 international sites; 29% responders), we evaluated the degree to which lithium response could be predicted based on 47,465 genotyped single nucleotide polymorphisms using a supervised ML approach. Under appropriate cross-validation procedures, lithium response could be predicted to above-chance levels in two constituent sites (Halifax, Cohen’s kappa 0.15, 95% confidence interval, CI [0.07, 0.24]; and Würzburg, kappa 0.2 [0.1, 0.3]). Variants with shared importance in these models showed over-representation of postsynaptic membrane related genes. Lithium response was not predictable in the pooled dataset (kappa 0.02 [− 0.01, 0.04]), although non-trivial performance was achieved within a restricted dataset including only those patients followed prospectively (kappa 0.09 [0.04, 0.14]). Genomic classification of lithium response remains a promising but difficult task. Classification performance could potentially be improved by further harmonization of data collection procedures.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of “big data” (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA’s activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material.

A system&rsquo, s heterogeneity (diversity) is the effective size of its event space, and can be quantified using the R&eacute, nyi family of indices (also known as Hill numbers in ecology or Hannah&ndash, Kay indices in economics), which are indexed by an elasticity parameter q&ge, 0. Under these indices, the heterogeneity of a composite system (the &gamma, heterogeneity) is decomposable into heterogeneity arising from variation within and between component subsystems (the &alpha, and &beta, heterogeneity, respectively). Since the average heterogeneity of a component subsystem should not be greater than that of the pooled system, we require that &gamma, &ge, &alpha, There exists a multiplicative decomposition for R&eacute, nyi heterogeneity of composite systems with discrete event spaces, but less attention has been paid to decomposition in the continuous setting. We therefore describe multiplicative decomposition of the R&eacute, nyi heterogeneity for continuous mixture distributions under parametric and non-parametric pooling assumptions. Under non-parametric pooling, the &gamma, heterogeneity must often be estimated numerically, but the multiplicative decomposition holds such that &gamma, for q&gt, 0. Conversely, under parametric pooling, &gamma, heterogeneity can be computed efficiently in closed-form, but the &gamma, condition holds reliably only at q=1. Our findings will further contribute to heterogeneity measurement in continuous systems.

Predicting lithium response (LiR) in bipolar disorder (BD) could expedite effective pharmacotherapy, but phenotypic heterogeneity of bipolar disorder has complicated the search for genomic markers. We thus sought to determine whether patients with "exemplary phenotypes"---those whose clinical features are reliably predictive of LiR and non-response (LiNR)---are more genetically separable than those with less exemplary phenotypes. We applied machine learning methods to clinical data collected from people with BD (n=1266 across 7 international centres; 34.7% responders) to compute an "exemplar score", which identified a subset of subjects whose clinical phenotypes were most robustly predictive of LiR/LiNR. For subjects whose genotypes were available (n=321), we evaluated whether responders/non-responders with exemplary phenotypes could be more accurately classified based on genetic data than those with non-exemplary phenotypes. We showed that the best LiR exemplars had later illness onset, completely episodic clinical course, absence of rapid cycling and psychosis, and few psychiatric comorbidities. The best exemplars of LiR and LiNR were genetically separable with an area under the receiver operating characteristic curve of 0.88 (IQR [0.83, 0.98]), compared to 0.66 [0.61, 0.80] (p=0.0032) among the poor exemplars. Variants in the Alzheimer's amyloid secretase pathway, along with G-protein coupled receptor, muscarinic acetylcholine, and histamine H1R signaling pathways were particularly informative predictors. In sum, the most reliably predictive clinical features of LiR and LiNR patients correspond to previously well-characterized phenotypic spectra whose genomic profiles are relatively distinct. Future work must enlarge the sample for genomic classification and include prediction of response to other mood stabilizers.

Heterogeneity is an important concept in psychiatric research and science more broadly. It negatively impacts effect size estimates under case–control paradigms, and it exposes important flaws in our existing categorical nosology. Yet, our field has no precise definition of heterogeneity proper. We tend to quantify heterogeneity by measuring associated correlates such as entropy or variance: practices which are akin to accepting the radius of a sphere as a measure of its volume. Under a definition of heterogeneity as the degree to which a system deviates from perfect conformity, this paper argues that its proper measure roughly corresponds to the size of a system’s event/sample space, and has units known as numbers equivalent. We arrive at this conclusion through focused review of more than 100 years of (re)discoveries of indices by ecologists, economists, statistical physicists, and others. In parallel, we review psychiatric approaches for quantifying heterogeneity, including but not limited to studies of symptom heterogeneity, microbiome biodiversity, cluster-counting, and time-series analyses. We argue that using numbers equivalent heterogeneity measures could improve the interpretability and synthesis of psychiatric research on heterogeneity. However, significant limitations must be overcome for these measures—largely developed for economic and ecological research—to be useful in modern translational psychiatric science.

A discrete system&rsquo, s heterogeneity is measured by the R&eacute, nyi heterogeneity family of indices (also known as Hill numbers or Hannah&ndash, Kay indices), whose units are the numbers equivalent. Unfortunately, numbers equivalent heterogeneity measures for non-categorical data require a priori (A) categorical partitioning and (B) pairwise distance measurement on the observable data space, thereby precluding application to problems with ill-defined categories or where semantically relevant features must be learned as abstractions from some data. We thus introduce representational R&eacute, nyi heterogeneity (RRH), which transforms an observable domain onto a latent space upon which the R&eacute, nyi heterogeneity is both tractable and semantically relevant. This method requires neither a priori binning nor definition of a distance function on the observable space. We show that RRH can generalize existing biodiversity and economic equality indices. Compared with existing indices on a beta-mixture distribution, we show that RRH responds more appropriately to changes in mixture component separation and weighting. Finally, we demonstrate the measurement of RRH in a set of natural images, with respect to abstract representations learned by a deep neural network. The RRH approach will further enable heterogeneity measurement in disciplines whose data do not easily conform to the assumptions of existing indices.

Background\ud Depressed patients with chronic and complex health issues commonly relapse; therefore, examining longer-term outcomes is an important consideration. For treatment resistant depression (TRD), the post-treatment efficacy of time-limited Intensive Short-Term Dynamic Psychotherapy (ISTDP) has been demonstrated but longer-term outcomes and cost-effectiveness are unclear.\ud \ud Method\ud In this superiority trial, 60 patients referred to Community Mental Health Teams (CMHT) were randomised to 2 groups (ISTDP=30 and CMHT=30). The primary outcome was Hamilton Depression Rating scale (HAM-D) scores at 18 months. Secondary outcomes included Patient Health Questionnaire (PHQ-9) depression scores and dichotomous measure remission. A health economic evaluation examined mental health costs with quality-adjusted life years (QALYs).\ud \ud Results\ud Statistically significant treatment differences in depression previously found at 6 months favouring ISTDP were maintained at 18-month follow-up. Group differences in depression were in the moderate to large range on both the observer rated (Cohen's d = .64) and self-report measures (Cohen's d = .70). At 18 months follow-up the remission rate in ISTDP patients was 40.0%, and 23.4% had discontinued antidepressants. Health economic analysis suggests that ISTDP was more cost-effective than CMHT at 18 months. Probabilistic analysis suggests that there is a 64.5% probability of ISTDP being cost-effective at a willingness to pay for a QALY of $25,000 compared to CMHT at 18 months.\ud \ud Limitations\ud Replication of these findings is necessary in larger samples and future cost analyses should also consider indirect costs.\ud \ud Conclusions\ud ISTDP demonstrates long-term efficacy and cost-effectiveness in TRD.

Predicting lithium response (LiR) in bipolar disorder (BD) may inform treatment planning, but phenotypic heterogeneity complicates discovery of genomic markers. We hypothesized that patients with “exemplary phenotypes”—those whose clinical features are reliably associated with LiR and non-response (LiNR)—are more genetically separable than those with less exemplary phenotypes. Using clinical data collected from people with BD (n = 1266 across 7 centers; 34.7% responders), we computed a “clinical exemplar score,” which measures the degree to which a subject’s clinical phenotype is reliably predictive of LiR/LiNR. For patients whose genotypes were available (n = 321), we evaluated whether a subgroup of responders/non-responders with the top 25% of clinical exemplar scores (the “best clinical exemplars”) were more accurately classified based on genetic data, compared to a subgroup with the lowest 25% of clinical exemplar scores (the “poor clinical exemplars”). On average, the best clinical exemplars of LiR had a later illness onset, completely episodic clinical course, absence of rapid cycling and psychosis, and few psychiatric comorbidities. The best clinical exemplars of LiR and LiNR were genetically separable with an area under the receiver operating characteristic curve of 0.88 (IQR [0.83, 0.98]), compared to 0.66 [0.61, 0.80] (p = 0.0032) among poor clinical exemplars. Variants in the Alzheimer’s amyloid–secretase pathway, along with G-protein-coupled receptor, muscarinic acetylcholine, and histamine H1R signaling pathways were informative predictors. This study must be replicated on larger samples and extended to predict response to other mood stabilizers.

Despite advancements in research methods and the growth of large international data sharing initiatives,[1][1] our understanding of the biological underpinnings of psychiatric disorders remains limited. An often cited reason for this stagnation is the presence of “heterogeneity,” whether