PTSD and the War of Words

Trauma-related symptoms among veterans of military engagement have been documented at least since the time of the ancient Greeks. 1 Since the third edition of the Diagnostic and Statistical Manual in 1980, this condition has been known as posttraumatic stress disorder, but the name has changed repeatedly over the past century, including shell shock, war neurosis, and soldier’s heart. Using over 14 million articles in the digital archives of the New York Times , Associated Press , and Reuters , we quantify historical changes in trauma-related terminology over the past century. These data suggest that posttraumatic stress disorder has historically peaked in public awareness after the end of US military engagements, but denoted by a different name each time—a phenomenon that could impede clinical and scientific progress

Altered functional brain connectivity in children and young people with paediatric opsoclonus-myoclonus syndrome (OMS)

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 control participants underwent 3T magnetic resonance imaging (MRI) to acquire resting state functional MRI data (whole-brain echo-planar images; 2mm isotropic voxels; multiband factor x 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 employed to determine brain networks showing altered connectivity. Results: In OMS patients the motor cortex showed significantly reduced connectivity, and the occipito-parietal region significantly increased connectivity with the cerebellum relative to the control group. A model-free analysis also showed extensive connectivity within a visual network, including the cerebellum and basal ganglia, not present in controls. No other networks showed any differences between groups. Interpretation: OMS patients showed reduced connectivity between the cerebellum and motor cortex, but increased connectivity with occipitoparietal regions. This pattern of change supports widespread brain involvement in OMS.</p

Altered functional brain connectivity in children and young people with paediatric opsoclonus-myoclonus syndrome (OMS)

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 control participants underwent 3T magnetic resonance imaging (MRI) to acquire resting state functional MRI data (whole-brain echo-planar images; 2mm isotropic voxels; multiband factor x 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 employed to determine brain networks showing altered connectivity. Results: In OMS patients the motor cortex showed significantly reduced connectivity, and the occipito-parietal region significantly increased connectivity with the cerebellum relative to the control group. A model-free analysis also showed extensive connectivity within a visual network, including the cerebellum and basal ganglia, not present in controls. No other networks showed any differences between groups. Interpretation: OMS patients showed reduced connectivity between the cerebellum and motor cortex, but increased connectivity with occipitoparietal regions. This pattern of change supports widespread brain involvement in OMS.</p

Supplemental material for PTSD and the War of Words

<p>Supplemental material for PTSD and the War of Words by Adam M. Chekroud, Hieronimus Loho, Martin Paulus and John H. Krystal in Chronic Stress</p

Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: a machine learning approach

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 <65, respectively) after 4 weeks and 52 weeks of treatment. To enable the unbiased estimation of the predictive system's generalisability to new patients, we used repeated nested cross-validation to prevent information leaking between patients used for training and validating the models. In pursuit of everyday clinical applicability, we retrained the 4-week outcome predictor with only the top ten predictors of the pooled prediction system and then tested this tool in 108 independent patients with 4-week outcome labels. Discontinuation and readmission to hospital events in patients with predicted poor versus good outcomes were assessed with Kaplan-Meier log-rank analyses, whereas generalised linear mixed-effects models were used to investigate the GAF-based predictions against several clinically meaningful outcome indicators, including treatment adherence, symptom remission, and quality of life. Findings The generalisability of our outcome predictions were estimated with cross-validation (test-fold balanced accuracy [BAC] of 75.0% for 4-week outcomes and 73.8% for and 52-week outcomes), and leave-site-out validation across 44 European sites (BAC of 72.1% for 4-week outcomes and 71.1% for 52-week outcomes). We identified a smaller group of ten predictors still providing a BAC of 71.7% in 108 patients never used for model discovery. Unemployment, poor education, functional deficits, and unmet psychosocial needs predicted both endpoints, whereas previous depressive episodes, male sex, and suicidality additionally predicted poor 1-year outcomes. 52-week predictions identified patients at risk for symptom persistence, non-adherence to treatment, readmission to hospital and poor quality of life. Specifically among these patients, amisulpride and olanzapine showed superior efficacy versus haloperidol, quetiapine, and ziprasidone. Interpretation Our results suggest that prognostic models operating on brief, patient-reportable pre-treatment data might provide useful insight into individualised outcome trajectories, optimising treatment selection, and targeted clinical trial designs. To embed these tools into real-world care, replication is needed in external first-episode samples with overlapping variables, which are not available in the field at presen

Multivariate Pattern Analysis of Genotype-Phenotype Relationships in Schizophrenia.

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