Functioning assessment short test (FAST) : validity and reliability in adults with Autism Spectrum Disorder

Altres ajuts: PERIS, SLT006/17/00287The assessment of functional impairment is crucial both for the diagnosis and the therapeutic approach to autism spectrum disorder (ASD). The purpose of the present study was to evaluate whether the FAST is a reliable and valid tool to assess functional impairment in adults with Level 1 ASD and to study the differences in psychosocial functioning between younger and older adults with ASD. A case-control study was carried out in a sample of 150 participants, 71 adults with Level 1 ASD, and 79 adults without psychiatric history records. Results showed good psychometric properties in terms of validity and reliability. Cronbach's alpha for the total scale was.91 and the area under the curve was.98. The study also showed that adults with ASD present different profiles of functional impairment depending on their age: while younger patients present greater impairment in autonomy, older patients show more difficulties in interpersonal relationships. Our results support the use of the FAST in the evaluation of adaptive functioning in adults with Level 1 AS

Effects of atypical antipsychotics on neurocognition in euthymic bipolar patients

International audiencen.

Testing the efficacy of INtegral Cognitive REMediation (INCREM) in major depressive disorder : study protocol for a randomized clinical trial

Given the limitation of pharmacological treatments to treat cognitive symptoms in patients with Major Depressive Disorder (MDD), cognitive remediation programs has been proposed as a possible procognitive intervention but findings are not conclusive. This study investigates the efficacy of an INtegral Cognitive REMediation (INCREM) that includes a combination of a Functional Remediation (FR) strategy plus a Computerized Cognitive Training (CCT) in order to improve not only cognitive performance but also the psychosocial functioning and the quality of life. A single blind randomized controlled clinical trial in 81 patients with a diagnosis of MDD in clinical remission or in partial remission. Participants will be randomized to one of three conditions: INCREM (FR + CCT), Psychoeducation plus online games and Treatment As Usual (TAU). Intervention will consist in 12 group sessions, of approximately 110 min once a week. The primary outcome measure will be % of change in psychosocial functioning after treatment measured by the Functional Assessment Short Test (FAST); additionally, number of sick leaves and daily activities will also be recorded as pragmatic outcomes. To our knowledge, this is the first randomized controlled clinical trial using a combination of two different approaches (FR + CCT) to treat the present cognitive deficits and to promote their improvements into a better psychosocial functioning. Clinical Trials . Date registered 10th of August 2018 and last updated 24th August 2018

Affective cognition in bipolar disorder: A systematic review by the ISBD targeting cognition task force

Background: Impairments in affective cognition are part of the neurocognitive profile and possible treatment targets in bipolar disorder (BD), but the findings are heterogeneous. The International Society of Bipolar Disorder (ISBD) Targeting Cognition Task Force conducted a systematic review to (i) identify the most consistent findings in affective cognition in BD, and (ii) provide suggestions for affective cognitive domains for future study and meta‐analyses.Methods: The review included original studies reporting behavioral measures of affective cognition in BD patients vs controls following the procedures of the Preferred Reporting Items for Systematic reviews and Meta‐Analysis (PRISMA) statement. Searches were conducted on PubMed/MEDLINE, EMBASE, and PsychInfo from inception until November 2018.Results: A total of 106 articles were included (of which nine included data for several affective domains); 41 studies assessed emotional face processing; 23 studies investigated reactivity to emotional words and images; 3 investigated explicit emotion regulation; 17 assessed implicit emotion regulation; 31 assessed reward processing and affective decision making. In general, findings were inconsistent. The most consistent findings were trait‐related difficulties in facial emotion recognition and implicit emotion regulation, and impairments in reward processing and affective decision making during mood episodes. Studies using eye‐tracking and facial emotion analysis revealed subtle trait‐related abnormalities in emotional reactivity.Conclusion: The ISBD Task Force recommends facial expression recognition, implicit emotion regulation, and reward processing as domains for future research and meta‐analyses. An important step to aid comparability between studies in the field would be to reach consensus on an affective cognition test battery for BD

Diagnosis of bipolar disorders and body mass index predict clustering based on similarities in cortical thickness-ENIGMA study in 2436 individuals

AIMS: Rates of obesity have reached epidemic proportions, especially among people with psychiatric disorders. While the effects of obesity on the brain are of major interest in medicine, they remain markedly under-researched in psychiatry. METHODS: We obtained body mass index (BMI) and magnetic resonance imaging-derived regional cortical thickness, surface area from 836 bipolar disorders (BD) and 1600 control individuals from 14 sites within the ENIGMA-BD Working Group. We identified regionally specific profiles of cortical thickness using K-means clustering and studied clinical characteristics associated with individual cortical profiles. RESULTS: We detected two clusters based on similarities among participants in cortical thickness. The lower thickness cluster (46.8% of the sample) showed thinner cortex, especially in the frontal and temporal lobes and was associated with diagnosis of BD, higher BMI, and older age. BD individuals in the low thickness cluster were more likely to have the diagnosis of bipolar disorder I and less likely to be treated with lithium. In contrast, clustering based on similarities in the cortical surface area was unrelated to BD or BMI and only tracked age and sex. CONCLUSIONS: We provide evidence that both BD and obesity are associated with similar alterations in cortical thickness, but not surface area. The fact that obesity increased the chance of having low cortical thickness could explain differences in cortical measures among people with BD. The thinner cortex in individuals with higher BMI, which was additive and similar to the BD-associated alterations, may suggest that treating obesity could lower the extent of cortical thinning in BD

What we learn about bipolar disorder from large-scale neuroimaging:Findings and future directions from the ENIGMA Bipolar Disorder Working Group

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

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group

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