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Background: To address if the long-standing association between maternal infection, depression/anxiety in pregnancy, and offspring neurodevelopmental disorder (NDD) is causal, we conducted two negative-control studies. Methods: Four primary care cohorts of UK children (pregnancy, 1 and 2 years prior to pregnancy, and siblings) born between 1 January 1990 and 31 December 2017 were constructed. NDD included autism/autism spectrum disorder, attention-deficit/hyperactivity disorder, intellectual disability, cerebral palsy, and epilepsy. Maternal exposures included depression/anxiety and/or infection. Maternal (age, smoking status, comorbidities, body mass index, NDD); child (gender, ethnicity, birth year); and area-level (region and level of deprivation) confounders were captured. The NDD incidence rate among (1) children exposed during or outside of pregnancy and (2) siblings discordant for exposure in pregnancy was compared using Cox-regression models, unadjusted and adjusted for confounders. Results: The analysis included 410 461 children of 297 426 mothers and 2 793 018 person-years of follow-up with 8900 NDD cases (incidence rate = 3.2/1000 person years). After adjustments, depression and anxiety consistently associated with NDD (pregnancy-adjusted HR = 1.58, 95% CI 1.46–1.72; 1-year adj. HR = 1.49, 95% CI 1.39–1.60; 2-year adj. HR = 1.62, 95% CI 1.50–1.74); and to a lesser extent, of infection (pregnancy adj. HR = 1.16, 95% CI 1.10–1.22; 1-year adj. HR = 1.20, 95% CI 1.14–1.27; 2-year adj. HR = 1.19, 95% CI 1.12–1.25). NDD risk did not differ among siblings discordant for pregnancy exposure to mental illness HR = 0.97, 95% CI 0.77–1.21 or infection HR = 0.99, 95% CI 0.90–1.08. Conclusions: Maternal risk appears to be unspecific to pregnancy: our study provided no evidence of a specific, and therefore causal, link between in-utero exposure to infection, common mental illness, and later development of NDD. [ABSTRACT FROM AUTHOR]

Background: Internet-based interventions produce comparable effectiveness rates as face-to-face therapy in treating depression. Still, more than half of patients do not respond to treatment. Machine learning (ML) methods could help to overcome these low response rates by predicting therapy outcomes on an individual level and tailoring treatment accordingly. Few studies implemented ML algorithms in internet-based depression treatment using baseline self-report data, but differing results hinder inferences on clinical practicability. This work compares algorithms using features gathered at baseline or early in treatment in their capability to predict non-response to a 6-week online program targeting depression. Methods: Our training and test sample encompassed 1270 and 318 individuals, respectively. We trained random forest algorithms on self-report and process features gathered at baseline and after 2 weeks of treatment. Non-responders were defined as participants not fulfilling the criteria for reliable and clinically significant change on PHQ-9 post-treatment. Our benchmark models were logistic regressions trained on baseline PHQ-9 sum or PHQ-9 early change, using 100 iterations of randomly sampled 80/20 train-test-splits. Results: Best performances were reached by our models involving early treatment characteristics (recall: 0.75–0.76; AUC: 0.71–0.77). Therapeutic alliance and early symptom change constituted the most important predictors. Models trained on baseline data were not significantly better than our benchmark. Conclusions: Fair accuracies were only attainable by involving information from early treatment stages. In-treatment adaptation, instead of a priori selection, might constitute a more feasible approach for improving response when relying on easily accessible self-report features. Implementation trials are needed to determine clinical usefulness. [ABSTRACT FROM AUTHOR]

Background: Despite increasing adoption of clinical practice guidelines in psychiatry, there is little measurement of provider implementation of these recommendations, and the resulting impact on clinical outcomes. The current study describes one effort to measure these relationships in a cohort of public sector out-patients with bipolar disorder., Method: Participants were enrolled in the algorithm intervention of the Texas Medication Algorithm Project (TMAP). Study methods and the adherence scoring algorithm have been described elsewhere. The current paper addresses the relationships between patient characteristics, provider experience with the algorithm, provider adherence, and clinical outcomes. Measurement of provider adherence includes evaluation of visit frequency, medication choice and dosing, and response to patient symptoms. An exploratory composite 'adherence by visit' score was developed for these analyses., Results: A total of 1948 visits from 141 subjects were evaluated, and utilized a two-stage declining effects model. Providers with more experience using the algorithm tended to adhere less to treatment recommendations. Few patient factors significantly impacted provider adherence. Increased adherence to algorithm recommendations was associated with larger decreases in overall psychiatric symptoms and depressive symptoms over time, but did not impact either immediate or long-term reductions in manic symptoms., Conclusions: Greater provider adherence to treatment guideline recommendations was associated with greater reductions in depressive symptoms and overall psychiatric symptoms over time. Additional research is needed to refine measurement and to further clarify these relationships.

Background: Mental disorders, including depression, obsessive compulsive disorder (OCD), and schizophrenia, share a common neuropathy of disturbed large-scale coordinated brain maturation. However, high-interindividual heterogeneity hinders the identification of shared and distinct patterns of brain network abnormalities across mental disorders. This study aimed to identify shared and distinct patterns of altered structural covariance across mental disorders. Methods: Subject-level structural covariance aberrance in patients with mental disorders was investigated using individualized differential structural covariance network. This method inferred structural covariance aberrance at the individual level by measuring the degree of structural covariance in patients deviating from matched healthy controls (HCs). T1-weighted anatomical images of 513 participants (105, 98, 190 participants with depression, OCD and schizophrenia, respectively, and 130 age- and sex-matched HCs) were acquired and analyzed. Results: Patients with mental disorders exhibited notable heterogeneity in terms of altered edges, which were otherwise obscured by group-level analysis. The three disorders shared high difference variability in edges attached to the frontal network and the subcortical-cerebellum network, and they also exhibited disease-specific variability distributions. Despite notable variability, patients with the same disorder shared disease-specific groups of altered edges. Specifically, depression was characterized by altered edges attached to the subcortical-cerebellum network; OCD, by altered edges linking the subcortical-cerebellum and motor networks; and schizophrenia, by altered edges related to the frontal network. Conclusions: These results have potential implications for understanding heterogeneity and facilitating personalized diagnosis and interventions for mental disorders. [ABSTRACT FROM AUTHOR]

Background: Resilience is defined as the ability to modify thoughts to cope with stressful events. Patients with schizophrenia (SCZ) having higher resilience (HR) levels show less severe symptoms and better real-life functioning. However, the clinical factors contributing to determine resilience levels in patients remain unclear. Thus, based on psychological, historical, clinical and environmental variables, we built a supervised machine learning algorithm to classify patients with HR or lower resilience (LR). Methods: SCZ from the Italian Network for Research on Psychoses (N = 598 in the Discovery sample, N = 298 in the Validation sample) underwent historical, clinical, psychological, environmental and resilience assessments. A Support Vector Machine algorithm (based on 85 variables extracted from the above-mentioned assessments) was built in the Discovery sample, and replicated in the Validation sample, to classify between HR and LR patients, within a nested, Leave-Site-Out Cross-Validation framework. We then investigated whether algorithm decision scores were associated with the cognitive and clinical characteristics of patients. Results: The algorithm classified patients as HR or LR with a Balanced Accuracy of 74.5% (p < 0.0001) in the Discovery sample, and 80.2% in the Validation sample. Higher self-esteem, larger social network and use of adaptive coping strategies were the variables most frequently chosen by the algorithm to generate decisions. Correlations between algorithm decision scores, socio-cognitive abilities, and symptom severity were significant (p FDR < 0.05). Conclusions: We identified an accurate, meaningful and generalizable clinical-psychological signature associated with resilience in SCZ. This study delivers relevant information regarding psychological and clinical factors that non-pharmacological interventions could target in schizophrenia. [ABSTRACT FROM AUTHOR]

Background: A transdiagnostic and contextual framework of 'clinical characterization', combining clinical, psychopathological, sociodemographic, etiological, and other personal contextual data, may add clinical value over and above categorical algorithm-based diagnosis. Methods: Prediction of need for care and health care outcomes was examined prospectively as a function of the contextual clinical characterization diagnostic framework in a prospective general population cohort (n = 6646 at baseline), interviewed four times between 2007 and 2018 (NEMESIS-2). Measures of need, service use, and use of medication were predicted as a function of any of 13 DSM-IV diagnoses, both separately and in combination with clinical characterization across multiple domains: social circumstances/demographics, symptom dimensions, physical health, clinical/etiological factors, staging, and polygenic risk scores (PRS). Effect sizes were expressed as population attributable fractions. Results: Any prediction of DSM-diagnosis in relation to need and outcome in separate models was entirely reducible to components of contextual clinical characterization in joint models, particularly the component of transdiagnostic symptom dimensions (a simple score of the number of anxiety, depression, mania, and psychosis symptoms) and staging (subthreshold, incidence, persistence), and to a lesser degree clinical factors (early adversity, family history, suicidality, slowness at interview, neuroticism, and extraversion), and sociodemographic factors. Clinical characterization components in combination predicted more than any component in isolation. PRS did not meaningfully contribute to any clinical characterization model. Conclusion: A transdiagnostic framework of contextual clinical characterization is of more value to patients than a categorical system of algorithmic ordering of psychopathology. [ABSTRACT FROM AUTHOR]

Background: Cluster analyses have become popular tools for data-driven classification in biological psychiatric research. However, these analyses are known to be sensitive to the chosen methods and/or modelling options, which may hamper generalizability and replicability of findings. To gain more insight into this problem, we used Specification-Curve Analysis (SCA) to investigate the influence of methodological variation on biomarker-based cluster-analysis results. Methods: Proteomics data (31 biomarkers) were used from patients (n = 688) and healthy controls (n = 426) in the Netherlands Study of Depression and Anxiety. In SCAs, consistency of results was evaluated across 1200 k-means and hierarchical clustering analyses, each with a unique combination of the clustering algorithm, fit-index, and distance metric. Next, SCAs were run in simulated datasets with varying cluster numbers and noise/outlier levels to evaluate the effect of data properties on SCA outcomes. Results: The real data SCA showed no robust patterns of biological clustering in either the MDD or a combined MDD/healthy dataset. The simulation results showed that the correct number of clusters could be identified quite consistently across the 1200 model specifications, but that correct cluster identification became harder when the number of clusters and noise levels increased. Conclusion: SCA can provide useful insights into the presence of clusters in biomarker data. However, SCA is likely to show inconsistent results in real-world biomarker datasets that are complex and contain considerable levels of noise. Here, the number and nature of the observed clusters may depend strongly on the chosen model-specification, precluding conclusions about the existence of biological clusters among psychiatric patients. [ABSTRACT FROM AUTHOR]

Background: Patients with obsessive-compulsive disorder (OCD) are at increased risk for suicide attempt (SA) compared to the general population. However, the significant risk factors for SA in this population remains unclear – whether these factors are associated with the disorder itself or related to extrinsic factors, such as comorbidities and sociodemographic variables. This study aimed to identify predictors of SA in OCD patients using a machine learning algorithm. Methods: A total of 959 outpatients with OCD were included. An elastic net model was performed to recognize the predictors of SA among OCD patients, using clinical and sociodemographic variables. Results: The prevalence of SA in our sample was 10.8%. Relevant predictors of SA founded by the elastic net algorithm were the following: previous suicide planning, previous suicide thoughts, lifetime depressive episode, and intermittent explosive disorder. Our elastic net model had a good performance and found an area under the curve of 0.95. Conclusions: This is the first study to evaluate risk factors for SA among OCD patients using machine learning algorithms. Our results demonstrate an accurate risk algorithm can be created using clinical and sociodemographic variables. All aspects of suicidal phenomena need to be carefully investigated by clinicians in every evaluation of OCD patients. Particular attention should be given to comorbidity with depressive symptoms. [ABSTRACT FROM AUTHOR]

Recent advances in machine learning (ML) promise far-reaching improvements across medical care, not least within psychiatry. While to date no psychiatric application of ML constitutes standard clinical practice, it seems crucial to get ahead of these developments and address their ethical challenges early on. Following a short general introduction concerning ML in psychiatry, we do so by focusing on schizophrenia as a paradigmatic case. Based on recent research employing ML to further the diagnosis, treatment, and prediction of schizophrenia, we discuss three hypothetical case studies of ML applications with view to their ethical dimensions. Throughout this discussion, we follow the principlist framework by Tom Beauchamp and James Childress to analyse potential problems in detail. In particular, we structure our analysis around their principles of beneficence, non-maleficence, respect for autonomy, and justice. We conclude with a call for cautious optimism concerning the implementation of ML in psychiatry if close attention is paid to the particular intricacies of psychiatric disorders and its success evaluated based on tangible clinical benefit for patients. [ABSTRACT FROM AUTHOR]

Background: Experiences of childhood trauma (CT) are associated with increased psychological vulnerability. Past research suggests that CT might alter stress processing with a subsequent negative impact on mental health. However, it is currently unclear how different domains of CT exert effects on specific subjective experiences of stress during adulthood. Methods: In the present study, we used network analysis to explore the complex interplay between distinct domains of CT and perceived stress in a large, general-population sample of middle-aged adults (N = 1252). We used a data-driven community-detection algorithm to identify strongly connected subgroups of items within the network. To assess the replicability of the findings, we repeated the analyses in a second sample (N = 862). Combining data from both samples, we evaluated network differences between men (n = 955) and women (n = 1159). Results: Results indicate specific associations between distinct domains of CT and perceived stress. CT domains reflecting a dimension of deprivation, i.e. experiences of neglect, were associated exclusively to a stress network community representing low perceived self-efficacy. By contrast, CT associated with threat, i.e. experiences of abuse, was specifically related to a stress community reflecting perceived helplessness. Our results replicated with high accordance in the second sample. We found no difference in network structure between men and women, but overall a stronger connected network in women. Conclusions: Our findings emphasize the unique role of distinct domains of CT in psychological stress processes in adulthood, implying opportunities for targeted interventions following distinct domains of CT. [ABSTRACT FROM AUTHOR]

Background: Psychiatric disorders, including eating disorders (EDs), have clinical outcomes that range widely in severity and chronicity. The ability to predict such outcomes is extremely limited. Machine-learning (ML) approaches that model complexity may optimize the prediction of multifaceted psychiatric behaviors. However, the investigations of many psychiatric concerns have not capitalized on ML to improve prognosis. This study conducted the first comparison of an ML approach (elastic net regularized logistic regression) to traditional regression to longitudinally predict ED outcomes. Methods: Females with heterogeneous ED diagnoses completed demographic and psychiatric assessments at baseline (n = 415) and Year 1 (n = 320) and 2 (n = 277) follow-ups. Elastic net and traditional logistic regression models comprising the same baseline variables were compared in ability to longitudinally predict ED diagnosis, binge eating, compensatory behavior, and underweight BMI at Years 1 and 2. Results: Elastic net models had higher accuracy for all outcomes at Years 1 and 2 [average Area Under the Receiving Operating Characteristics Curve (AUC) = 0.78] compared to logistic regression (average AUC = 0.67). Model performance did not deteriorate when the most important predictor was removed or an alternative ML algorithm (random forests) was applied. Baseline ED (e.g. diagnosis), psychiatric (e.g. hospitalization), and demographic (e.g. ethnicity) characteristics emerged as important predictors in exploratory predictor importance analyses. Conclusions: ML algorithms can enhance the prediction of ED symptoms for 2 years and may identify important risk markers. The superior accuracy of ML for predicting complex outcomes suggests that these approaches may ultimately aid in advancing precision medicine for serious psychiatric disorders. [ABSTRACT FROM AUTHOR]

Background: Depression even at the subclinical level is often accompanied by sleep disturbances, but little is known about the dynamics of the sleep stages in relation to depressive symptoms. We examined whether the amount, associations, and transition probabilities of various sleep stages were associated with depressive symptoms in a community sample of adolescents. Methods: The participants (N = 172, 59% girls, mean age 16.9 years) underwent overnight polysomnography and provided data on depressive symptoms (Beck Depression Inventory II). The association between depression status and total duration of each stage type was analyzed using ANOVA and survival analyses. The associations between the number of different sleep stage types were analyzed using graphical Gaussian models, mixed graphical models, and relative importance networks. A Markov chain algorithm was used to estimate the transition probabilities between each state and these probabilities were further compared between depression status groups. Results: The associations between N1 and N3 were significantly stronger in both directions of the association (p-values for interactions 0.012 and 0.006) in those with more depressive symptoms. Similarly, a stronger association was observed from N1 to wake stage in those with more depressive symptoms (p-value for interaction 0.002). In those with more depressive symptoms, it was more likely to transition from N2 to N3 and from REM to N2 compared to others. Conclusions: These findings indicate that changes in sleep architecture are not limited to clinical depression and that the transitional dynamics of sleep stages are an important marker of subclinical depression. [ABSTRACT FROM AUTHOR]

Background: Previous models suggest biological and behavioral continua among healthy individuals (HC), at-risk condition, and full-blown schizophrenia (SCZ). Part of these continua may be captured by schizotypy, which shares subclinical traits and biological phenotypes with SCZ, including thalamic structural abnormalities. In this regard, previous findings have suggested that multivariate volumetric patterns of individual thalamic nuclei discriminate HC from SCZ. These results were obtained using machine learning, which allows case–control classification at the single-subject level. However, machine learning accuracy is usually unsatisfactory possibly due to phenotype heterogeneity. Indeed, a source of misclassification may be related to thalamic structural characteristics of those HC with high schizotypy, which may resemble structural abnormalities of SCZ. We hypothesized that thalamic structural heterogeneity is related to schizotypy, such that high schizotypal burden would implicate misclassification of those HC whose thalamic patterns resemble SCZ abnormalities. Methods: Following a previous report, we used Random Forests to predict diagnosis in a case–control sample (SCZ = 131, HC = 255) based on thalamic nuclei gray matter volumes estimates. Then, we investigated whether the likelihood to be classified as SCZ (π-SCZ) was associated with schizotypy in 174 HC, evaluated with the Schizotypal Personality Questionnaire. Results: Prediction accuracy was 72.5%. Misclassified HC had higher positive schizotypy scores, which were correlated with π-SCZ. Results were specific to thalamic rather than whole-brain structural features. Conclusions: These findings strengthen the relevance of thalamic structural abnormalities to SCZ and suggest that multivariate thalamic patterns are correlates of the continuum between schizotypy in HC and the full-blown disease. [ABSTRACT FROM AUTHOR]

Background: Altered heart rate variability (HRV), an index of autonomic nervous system function, has been reported in generalized anxiety disorder (GAD), but the results have been mixed. Thus, the present study, using a large sample size and better methodology, aims to examine whether GAD is associated with impaired HRV, both at rest and in response to posture challenges. Methods: In total, 1832 participants were recruited in this study, consisting of 682 patients with GAD (including 326 drug- and comorbidity-free GAD patients) and 1150 healthy controls. Short-term HRV was measured during the supine-standing-supine test (5-min per position). Propensity score matching (PSM), a relatively novel method, was used to control for potential confounders. Results: After PSM algorithm, drug- and comorbidity-free GAD patients had reductions in resting (baseline) high-frequency power (HF), an index for parasympathetic modulation, and increases in the low-frequency/HF ratio (LF/HF), an index for sympathovagal balance as compared to matched controls. Furthermore, the responses of HF and LF/HF to posture changes were all attenuated when compared with matched controls. Effect sizes, given by Cohen's d, for resting HF and HF reactivity were 0.42 and 0.36–0.42, respectively. Conclusions: GAD is associated with altered sympathovagal balance, characterized by attenuation in both resting vagal modulation and vagal reactivity, with an almost medium effect size (Cohen's d ≈ 0.4), regardless of medication use or comorbidity status. [ABSTRACT FROM AUTHOR]

BackgroundMental health morbidity in post-conflict settings is high. Nevertheless, randomized controlled trials of psychotherapy on site are rare. Our aim was to integrate rigorous research procedures into a humanitarian programme and test the efficacy of resource activation (ROTATE) in treating post-traumatic stress disorder (PTSD), co-morbid symptoms and impaired functioning in Cambodia.MethodA total of 86 out-patients with PTSD were randomly assigned to five sessions of ROTATE (n = 53) or a 5-week waiting-list control (WLC) condition (n = 33). Treatment was provided by six Cambodian psychologists who had received extensive training in ROTATE. Masked assessments were made before and after therapy.ResultsPTSD remission rates according to the DSM-IV algorithm of the Harvard Trauma Questionnaire were 95.9% in ROTATE and 24.1% in the WLC condition. Thus, patients receiving ROTATE had a significantly higher likelihood of PTSD remission (odds ratio 0.012, 95% confidence interval 0.002–0.071, p < 0.00001). Additionally, levels of anxiety, depression and impaired functioning were significantly reduced compared with the WLC condition (p < 0.00001, between-group effect sizes d = 2.41, 2.26 and 2.54, respectively). No harms were reported.ConclusionsROTATE was efficacious in treating Cambodian patients with high symptom levels of PTSD, emotional distress and impaired functioning. ROTATE is a brief, culturally adaptable intervention focusing on stabilization and strengthening resources rather than trauma confrontation. It can be taught to local professionals and paraprofessionals and enhance access to mental health care for patients in need. [ABSTRACT FROM AUTHOR]

BackgroundTo our knowledge, there are no universal screening tools for substance dependence that (1) were developed using a population-based sample, (2) estimate total risk briefly and inexpensively by incorporating a relatively small number of well-established risk factors, and (3) aggregate risk factors using a simple algorithm. We created a universal screening tool that incorporates these features to identify adolescents at risk for persistent substance dependence in adulthood.MethodParticipants were members of a representative cohort of 1037 individuals born in Dunedin, New Zealand in 1972–1973 and followed prospectively to age 38 years, with 95% retention. We assessed a small set of childhood and adolescent risk factors: family history of substance dependence, childhood psychopathology (conduct disorder, depression), early exposure to substances, frequent substance use in adolescence, sex, and childhood socioeconomic status. We defined the outcome (persistent substance dependence in adulthood) as dependence on one or more of alcohol, tobacco, cannabis, or hard drugs at ⩾3 assessment ages: 21, 26, 32, and 38 years.ResultsA cumulative risk index, a simple sum of nine childhood and adolescent risk factors, predicted persistent substance dependence in adulthood with considerable accuracy (AUC = 0.80).ConclusionsA cumulative risk score can accurately predict which adolescents in the general population will develop persistent substance dependence in adulthood. [ABSTRACT FROM PUBLISHER]

Background.Severe health anxiety is frequent and costly, yet rarely diagnosed or treated. Earlier treatment studies show problems with recruitment, dropout and recovery. In the current study, the authors aimed to test the effect of acceptance and commitment group therapy (ACT-G) compared to waitlist in patients with severe health anxiety.Method.During March 2010 to April 2012, 126 consecutively referred patients meeting research criteria for severe health anxiety were block-randomized (1:1) to ACT-G or a 10 months’ waitlist (Clinicaltrials.gov, no. NCT01158430). Patients allocated to ACT-G were treated in seven groups of nine patients between December 2010 and October 2012 and received nine weekly 3-h group sessions and a booster session consisting of ACT techniques. The primary outcome was decided a priori as the mean change in self-reported illness worry on the Whiteley-7 Index (WI) from baseline to 10 months’ follow-up. Secondary outcomes were improvement in emotional distress and health-related quality of life at 10 months’ follow-up.Results.Intention-to-treat analysis showed a statistically significant mean difference of 20.5 points [95% confidence interval (CI) 11.7–29·4, p < 0.001] on the WI between the groups at 10 months, and the between-group effect sizes were large (Cohen's d = 0.89, 95% CI 0.50–1.29). The number needed to treat was 2.4 (95% CI 1.4–3.4, p < 0.001). Diagnosis and treatment were well accepted by the patients.Conclusions.ACT-G seems feasible, acceptable and effective in treating severe health anxiety. [ABSTRACT FROM PUBLISHER]

BackgroundThere are currently no neuroanatomical biomarkers of anorexia nervosa (AN) available to make clinical inferences at an individual subject level. We present results of a multivariate machine learning (ML) approach utilizing structural neuroanatomical scan data to differentiate AN patients from matched healthy controls at an individual subject level.MethodStructural neuroimaging scans were acquired from 15 female patients with AN (age = 20, s.d. = 4 years) and 15 demographically matched female controls (age = 22, s.d. = 3 years). Neuroanatomical volumes were extracted using the FreeSurfer software and input into the Least Absolute Shrinkage and Selection Operator (LASSO) multivariate ML algorithm. LASSO was ‘trained’ to identify ‘novel’ individual subjects as either AN patients or healthy controls. Furthermore, the model estimated the probability that an individual subject belonged to the AN group based on an individual scan.ResultsThe model correctly predicted 25 out of 30 subjects, translating into 83.3% accuracy (sensitivity 86.7%, specificity 80.0%) (p < 0.001; χ2 test). Six neuroanatomical regions (cerebellum white matter, choroid plexus, putamen, accumbens, the diencephalon and the third ventricle) were found to be relevant in distinguishing individual AN patients from healthy controls. The predicted probabilities showed a linear relationship with drive for thinness clinical scores (r = 0.52, p < 0.005) and with body mass index (BMI) (r = −0.45, p = 0.01).ConclusionsThe model achieved a good predictive accuracy and drive for thinness showed a strong neuroanatomical signature. These results indicate that neuroimaging scans coupled with ML techniques have the potential to provide information at an individual subject level that might be relevant to clinical outcomes. [ABSTRACT FROM AUTHOR]

BackgroundThe number of separable cognitive dimensions in schizophrenia has been debated. Guided by the extant factor analytic literature, the NIMH Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) initiative selected seven cognitive domains relevant to treatment studies in schizophrenia: speed of processing, attention/vigilance, working memory, verbal learning, visual learning, reasoning and problem solving, and social cognition. These domains are assessed in the MATRICS Consensus Cognitive Battery (MCCB). The aim of this study was to conduct a confirmatory factor analysis (CFA) of the beta battery of the MCCB to compare the fit of the MATRICS consensus seven-domain model to other models in the current literature on cognition in schizophrenia.MethodUsing data from 281 schizophrenia outpatients, we compared the seven correlated factors model with alternative models. Specifically, we compared the 7-factor model to (a) a single-factor model, (b) a three correlated factors model including speed of processing, working memory, and general cognition, and (c) a hierarchical model in which seven first-order factors loaded onto a second-order general cognitive factor.ResultsMultiple fit indices indicated the seven correlated factors model was the best fit for the data and provided significant improvement in model fit beyond the comparison models.ConclusionsThese results support the assessment of these seven cognitive dimensions in clinical trials of interventions to improve cognition in schizophrenia. Because these cognitive factors are separable to some degree, it is plausible that specific interventions may have differential effects on the domains. [ABSTRACT FROM AUTHOR]

BackgroundThe prevention of depression is a key public health policy priority. PredictD is the first risk algorithm for the prediction of the onset of major depression. Our aim in this study was to model the cost-effectiveness of PredictD in depression prevention in general practice (GP).MethodA decision analytical model was developed to determine the cost-effectiveness of two approaches, each of which was compared to treatment as usual (TAU) over 12 months: (1) the PredictD risk algorithm plus a low-intensity depression prevention programme; and (2) a universal prevention programme in which there was no initial identification of those at risk. The model simulates the incidence of depression and disease progression over 12 months and calculates the net monetary benefit (NMB) from the National Health Service (NHS) perspective.ResultsProviding patients with PredictD and a depression prevention programme prevented 15 (17%) cases of depression in a cohort of 1000 patients over 12 months and had the highest probability of being the optimal choice at a willingness to pay (WTP) of £20 000 for a quality-adjusted life year (QALY). Universal prevention was strongly dominated by PredictD plus a depression prevention programme in that universal prevention resulted in less QALYs than PredictD plus prevention for a greater cost.ConclusionsUsing PredictD to identify primary-care patients at high risk of depression and providing them with a low-intensity prevention programme is potentially cost-effective at a WTP of £20 000 per QALY. [ABSTRACT FROM PUBLISHER]

BackgroundAt present there are no objective, biological markers that can be used to reliably identify individuals with post-traumatic stress disorder (PTSD). This study assessed the diagnostic potential of structural magnetic resonance imaging (sMRI) for identifying trauma-exposed individuals with and without PTSD.MethodsMRI scans were acquired from 50 survivors of the Sichuan earthquake of 2008 who had developed PTSD, 50 survivors who had not developed PTSD and 40 healthy controls who had not been exposed to the earthquake. Support vector machine (SVM), a multivariate pattern recognition technique, was used to develop an algorithm that distinguished between the three groups at an individual level. The accuracy of the algorithm and its statistical significance were estimated using leave-one-out cross-validation and permutation testing.ResultsWhen survivors with PTSD were compared against healthy controls, both grey and white matter allowed discrimination with an accuracy of 91% (p < 0.001). When survivors without PTSD were compared against healthy controls, the two groups could be discriminated with accuracies of 76% (p < 0.001) and 85% (p < 0.001) based on grey and white matter, respectively. Finally, when survivors with and without PTSD were compared directly, grey matter allowed discrimination with an accuracy of 67% (p < 0.001); in contrast the two groups could not be distinguished based on white matter.ConclusionsThese results reveal patterns of neuroanatomical alterations that could be used to inform the identification of trauma survivors with and without PTSD at the individual level, and provide preliminary support to the development of SVM as a clinically useful diagnostic aid. [ABSTRACT FROM AUTHOR]

BackgroundPredictD is a risk algorithm that was developed to predict risk of onset of major depression over 12 months in general practice attendees in Europe and validated in a similar population in Chile. It was the first risk algorithm to be developed in the field of mental disorders. Our objective was to extend predictD as an algorithm to detect people at risk of major depression over 24 months.MethodParticipants were 4190 adult attendees to general practices in the UK, Spain, Slovenia and Portugal, who were not depressed at baseline and were followed up for 24 months. The original predictD risk algorithm for onset of DSM-IV major depression had already been developed in data arising from the first 12 months of follow-up. In this analysis we fitted predictD to the longer period of follow-up, first by examining only the second year (12–24 months) and then the whole period of follow-up (0–24 months).ResultsThe instrument performed well for prediction of major depression from 12 to 24 months [c-index 0.728, 95% confidence interval (CI) 0.675–0.781], or over the whole 24 months (c-index 0.783, 95% CI 0.757–0.809).ConclusionsThe predictD risk algorithm for major depression is accurate over 24 months, extending it current use of prediction over 12 months. This strengthens its use in prevention efforts in general medical settings. [ABSTRACT FROM AUTHOR]

BackgroundAutistic-like traits (ALTs), that is restrictions in intuitive social interaction, communication and flexibility of interests and behaviors, were studied in two population-based Swedish twin studies, one in children and one in adults: (1) to examine whether the variability in ALTs is a meaningful risk factor for concomitant attention deficit hyperactivity disorder (ADHD), anxiety, conduct problems, depression and substance abuse, and (2) to assess whether common genetic and environmental susceptibilities can help to explain co-existence of ALTs and traits associated with such concomitant problems.MethodTwo nationwide twin cohorts from Sweden (consisting of 11 222 children and 18 349 adults) were assessed by DSM-based symptom algorithms for autism. The twins were divided into six groups based on their degree of ALTs and the risk for concomitant mental health problems was calculated for each group. Genetic and environmental susceptibilities common to ALTs and the other problem types were examined using bivariate twin modeling.ResultsIn both cohorts, even the lowest degree of ALTs increased the risk for all other types of mental health problems, and these risk estimates increased monotonically with the number of ALTs. For all conditions, common genetic and environmental factors could be discerned. Overall, the phenotypic correlation between ALTs and the traits examined were less pronounced in adulthood than in childhood and less affected by genetic compared with environmental factors.ConclusionsEven low-grade ALTs are relevant to clinical psychiatry as they increase the risk for several heterotypical mental health problems. The association is influenced partly by common genetic and environmental susceptibilities. Attention to co-existing ALTs is warranted in research on a wide range of mental disorders. [ABSTRACT FROM PUBLISHER]

BackgroundThe different incidence rates of, and risk factors for, depression in different countries argue for the need to have a specific risk algorithm for each country or a supranational risk algorithm. We aimed to develop and validate a predictD-Spain risk algorithm (PSRA) for the onset of major depression and to compare the performance of the PSRA with the predictD-Europe risk algorithm (PERA) in Spanish primary care.MethodA prospective cohort study with evaluations at baseline, 6 and 12 months. We measured 39 known risk factors and used multi-level logistic regression and inverse probability weighting to build the PSRA. In Spain (4574), Chile (2133) and another five European countries (5184), 11 891 non-depressed adult primary care attendees formed our at-risk population. The main outcome was DSM-IV major depression (CIDI).ResultsSix variables were patient characteristics or past events (sex, age, sex×age interaction, education, physical child abuse, and lifetime depression) and six were current status [Short Form 12 (SF-12) physical score, SF-12 mental score, dissatisfaction with unpaid work, number of serious problems in very close persons, dissatisfaction with living together at home, and taking medication for stress, anxiety or depression]. The C-index of the PSRA was 0.82 [95% confidence interval (CI) 0.79–0.84]. The Integrated Discrimination Improvement (IDI) was 0.0558 [standard error (s.e.)=0.0071, Zexp=7.88, p<0.0001] mainly due to the increase in sensitivity. Both the IDI and calibration plots showed that the PSRA functioned better than the PERA in Spain.ConclusionsThe PSRA included new variables and afforded an improved performance over the PERA for predicting the onset of major depression in Spain. However, the PERA is still the best option in other European countries. [ABSTRACT FROM PUBLISHER]

BackgroundThere are no risk models for the prediction of anxiety that may help in prevention. We aimed to develop a risk algorithm for the onset of generalized anxiety and panic syndromes.MethodFamily practice attendees were recruited between April 2003 and February 2005 and followed over 24 months in the UK, Spain, Portugal and Slovenia (Europe4 countries) and over 6 months in The Netherlands, Estonia and Chile. Our main outcome was generalized anxiety and panic syndromes as measured by the Patient Health Questionnaire. We entered 38 variables into a risk model using stepwise logistic regression in Europe4 data, corrected for over-fitting and tested it in The Netherlands, Estonia and Chile.ResultsThere were 4905 attendees in Europe4, 1094 in Estonia, 1221 in The Netherlands and 2825 in Chile. In the algorithm four variables were fixed characteristics (sex, age, lifetime depression screen, family history of psychological difficulties); three current status (Short Form 12 physical health subscale and mental health subscale scores, and unsupported difficulties in paid and/or unpaid work); one concerned country; and one time of follow-up. The overall C-index in Europe4 was 0.752 [95% confidence interval (CI) 0.724–0.780]. The effect size for difference in predicted log odds between developing and not developing anxiety was 0.972 (95% CI 0.837–1.107). The validation of predictA resulted in C-indices of 0.731 (95% CI 0.654–0.809) in Estonia, 0.811 (95% CI 0.736–0.886) in The Netherlands and 0.707 (95% CI 0.671–0.742) in Chile.ConclusionsPredictA accurately predicts the risk of anxiety syndromes. The algorithm is strikingly similar to the predictD algorithm for major depression, suggesting considerable overlap in the concepts of anxiety and depression. [ABSTRACT FROM PUBLISHER]