ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

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

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

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

Machine Learning Can be Used to Predict Function but Not Pain After Surgery for Thumb Carpometacarpal Osteoarthritis

BACKGROUND: Surgery for thumb carpometacarpal osteoarthritis is offered to patients who do not benefit from nonoperative treatment. Although surgery is generally successful in reducing symptoms, not all patients benefit. Predicting clinical improvement after surgery could provide decision support and enhance preoperative patient selection. QUESTIONS/PURPOSES: This study aimed to develop and validate prediction models for clinically important improvement in (1) pain and (2) hand function 12 months after surgery for thumb carpometacarpal osteoarthritis. METHODS: Between November 2011 and June 2020, 2653 patients were surgically treated for thumb carpometacarpal osteoarthritis. Patient-reported outcome measures were used to preoperatively assess pain, hand function, and satisfaction with hand function, as well as the general mental health of patients and mindset toward their condition. Patient characteristics, medical history, patient-reported symptom severity, and patient-reported mindset were considered as possible predictors. Patients who had incomplete Michigan Hand outcomes Questionnaires at baseline or 12 months postsurgery were excluded, as these scores were used to determine clinical improvement. The Michigan Hand outcomes Questionnaire provides subscores for pain and hand function. Scores range from 0 to 100, with higher scores indicating less pain and better hand function. An improvement of at least the minimum clinically important difference (MCID) of 14.4 for the pain score and 11.7 for the function score were considered "clinically relevant." These values were derived from previous reports that provided triangulated estimates of two anchor-based and one distribution-based MCID. Data collection resulted in a dataset of 1489 patients for the pain model and 1469 patients for the hand function model. The data were split into training (60%), validation (20%), and test (20%) dataset. The training dataset was used to select the predictive variables and to train our models. The performance of all models was evaluated in the validation dataset, after which one model was selected for further evaluation. Performance of this final model was evaluated on the test dataset. We trained the models using logistic regression, random forest, and gradient boosting machines and compared their performance. We chose these algorithms because of their relative simplicity, which makes them easier to implement and interpret. Model performance was assessed using discriminative ability and qualitative visual inspection of calibration curves. Discrimination was measured using area under the curve (AUC) and is a measure of how well the model can differentiate between the outcomes (improvement or no improvement), with an AUC of 0.5 being equal to chance. Calibration is a measure of the agreement between the predicted probabilities and the observed frequencies and was assessed by visual inspection of calibration curves. We selected the model with the most promising performance for clinical implementation (that is, good model performance and a low number of predictors) for further evaluation in the test dataset. RESULTS: For pain, the random forest model showed the most promising results based on discrimination, calibration, and number of predictors in the validation dataset. In the test dataset, this pain model had a poor AUC (0.59) and poor calibration. For function, the gradient boosting machine showed the most promising results in the validation dataset. This model had a good AUC (0.74) and good calibration in the test dataset. The baseline Michigan Hand outcomes Questionnaire hand function score was the only predictor in the model. For the hand function model, we made a web application that can be accessed via https://analyse.equipezorgbedrijven.nl/shiny/cmc1-prediction-model-Eng/. CONCLUSION: We developed a promising model that may allow clinicians to predict the chance of functional improvement in an individual patient undergoing surgery for thumb carpometacarpal osteoarthritis, which would thereby help in the decision-making process. However, caution is warranted because our model has not been externally validated. Unfortunately, the performance of the prediction model for pain is insufficient for application in clinical practice. LEVEL OF EVIDENCE: Level III, therapeutic study

Reply to the Letter to the Editor: Machine Learning Can be Used to Predict Function but Not Pain After Surgery for Thumb Carpometacarpal Osteoarthritis

To the Editor,We would like to thank Kabuli et al. for their interest in our study and for sharing their perspectives on the importance of anxiety on the outcomes of surgery. [...] We fully agree with the letter authors on the importance of considering the psychological profiles of patients when predicting outcomes after surgical treatment of the first carpometacarpal with osteoarthritis (CMC-1 OA).[...] We therefore included several psychological variables as possible predictors for our models, including the PHQ-4 for depression and anxiety. [...

Patients With Higher Treatment Outcome Expectations Are More Satisfied With the Results of Nonoperative Treatment for Thumb Base Osteoarthritis

Objective: To investigate how satisfaction with treatment outcome is associated with patient mindset and Michigan Hand Outcome Questionnaire (MHQ) scores at baseline and 3 months in patients receiving nonoperative treatment for first carpometacarpal joint (CMC-1) osteoarthritis (OA). Design: Cohort study Setting: A total of 20 outpatient locations of a clinic for hand surgery and hand therapy in the Netherlands. Participants: Patients (N=308) receiving nonoperative treatment for CMC-1 OA, including exercise therapy, an orthosis, or both, between September 2017 and February 2019. Interventions: Nonoperative treatment (ie, exercise therapy, an orthosis, or both) Main Outcome Measures: Satisfaction with treatment outcomes was measured after 3 months of treatment. We measured total MHQ score at baseline and at 3 months. As baseline mindset factors, patients completed questionnaires on treatment outcome expectations, illness perceptions, pain catastrophizing, and psychological distress. We used multivariable logistic regression analysis and mediation analysis to identify factors associated with satisfaction with treatment outcomes. Results: More positive pretreatment outcome expectations were associated with a higher probability of being satisfied with treatment outcomes at 3 months (odds ratio, 1.15; 95% confidence interval, 1.07-1.25). Only a relatively small part (33%) of this association was because of a higher total MHQ score at 3 months. None of the other mindset and hand function variables at baseline were associated with satisfaction with treatment outcomes. Conclusions: This study demonstrates that patients with higher pretreatment outcome expectations are more likely to be satisfied with treatment outcomes after 3 months of nonoperative treatment for CMC-1 OA. This association could only partially be explained by a better functional outcome at 3 months for patients who were satisfied. Health care providers treating patients nonoperatively for CMC-1 OA should be aware of the importance of expectations and may take this into account in pretreatment counseling.</p

Prognostic Factors in Open Triangular Fibrocartilage Complex (TFCC) Repair

Purpose: Patients with triangular fibrocartilage complex (TFCC) injury report ulnar-sided wrist pain and impaired function. Open TFCC repair aims to improve the condition of these patients. Patients have shown reduction in pain and improvement in function at 12 months after surgery; however, results are highly variable. The purpose of this study was to relate patient (eg, age and sex), disease (eg, trauma history and arthroscopic findings), and surgery factors (type of bone anchor) associated with pain and functional outcomes at 12 months after surgery. Methods: This study included patients who underwent an open TFCC repair between December 2011 and December 2018 in various Xpert Clinics in the Netherlands. All patients were asked to complete Patient-Rated Wrist Evaluation (PRWE) questionnaires at baseline as well as at 12 months after surgery. Patient, disease, and surgery factors were extracted from digital patient records. All factors were analyzed by performing a multivariable hierarchical linear regression. Results: We included 274 patients who had received open TFCC repair and completed PRWE questionnaires. Every extra month of symptoms before surgery was correlated with an increase of 0.14 points on the PRWE total score at 12 months after surgery. In addition, an increase of 0.28 points in the PRWE total score at 12 months was seen per extra point of PRWE total score at baseline. Conclusions: Increased preoperative pain, less preoperative function, and a longer duration of complaints are factors that were associated with more pain and less function at 12 months after open surgery for TFCC. This study arms surgeons with data to predict outcomes for patients undergoing open TFCC repair. Type of study/level of evidence: Prognostic II.</p

Factors associated with return to work after open reinsertion of the triangular fibrocartilage

The aim of this study was to assess return to work (RTW) after open Triangular Fibrocartilage Complex (TFCC) reinsertion. RTW after open surgery for TFCC injury was assessed by questionnaires at 6 weeks, 3 months, 6 months, and 12 months post-operatively. Median RTW time was assessed on inverted Kaplan–Meier curves and hazard ratios were calculated with Cox regression models. 310 patients with a mean age of 38 years were included. By 1 year, 91% of the patients had returned to work, at a median 12 weeks (25%–75%: 6–20 weeks). Light physical labor (HR 3.74) was associated with RTW within the first 15 weeks; this association altered from 23 weeks onward: light (HR 0.59) or moderate physical labor (HR 0.25) was associated with lower RTW rates. Patients with poorer preoperative Patient-Rated Wrist Evaluation (PRWE) total score returned to work later (HR 0.91 per 10 points). Overall cost of loss of productivity per patient was €13,588. In the first year after open TFCC reinsertion, 91% of the patients returned to work, including 50% within 12 weeks. Factors associated with RTW were age, gender, work intensity, and PRWE score at baseline

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

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

Volume of subcortical brain regions in social anxiety disorder: mega-analytic results from 37 samples in the ENIGMA-Anxiety Working Group

There is limited convergence in neuroimaging investigations into volumes of subcortical brain regions in social anxiety disorder (SAD). The inconsistent findings may arise from variations in methodological approaches across studies, including sample selection based on age and clinical characteristics. The ENIGMA-Anxiety Working Group initiated a global mega-analysis to determine whether differences in subcortical volumes can be detected in adults and adolescents with SAD relative to healthy controls. Volumetric data from 37 international samples with 1115 SAD patients and 2775 controls were obtained from ENIGMA-standardized protocols for image segmentation and quality assurance. Linear mixed-effects analyses were adjusted for comparisons across seven subcortical regions in each hemisphere using family-wise error (FWE)-correction. Mixed-effects d effect sizes were calculated. In the full sample, SAD patients showed smaller bilateral putamen volume than controls (left: d = −0.077, pFWE = 0.037; right: d = −0.104, pFWE = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = −0.034, pFWE = 0.045). Smaller bilateral putamen volumes (left: d = −0.141, pFWE < 0.001; right: d = −0.158, pFWE < 0.001) and larger bilateral pallidum volumes (left: d = 0.129, pFWE = 0.006; right: d = 0.099, pFWE = 0.046) were detected in adult SAD patients relative to controls, but no volumetric differences were apparent in adolescent SAD patients relative to controls. Comorbid anxiety disorders and age of SAD onset were additional determinants of SAD-related volumetric differences in subcortical regions. To conclude, subtle volumetric alterations in subcortical regions in SAD were detected. Heterogeneity in age and clinical characteristics may partly explain inconsistencies in previous findings. The association between alterations in subcortical volumes and SAD illness progression deserves further investigation, especially from adolescence into adulthood