The Walsh family resilience questionnaire: The Italian version

Background: Resilience focuses on strength under stress, in the context of adversity. Walsh\u2019s theoretical model identifies relational processes that allow families to tackle and overcome critical situations, dividing them into three domains of family function. The aim of this study was to assess resilience in families of patients with a chronic disease by adapting and validating the Italian version of the Walsh Family Resilience Questionnaire (Walsh-IT). Patients and methods: An Italian adult sample of 421 participants (patients and relatives) was collected with the aim to assess the reliability and validity of the Walsh-IT. Concurrent validity was carried out by comparing this instrument with the Family Adaptability and Cohesion Evaluation Scale III (FACES III) administered at the same time as the Walsh-IT. Results: Reliability showed high correlation between repeated measurements. The alpha coefficient was 0.946. Both parallel analysis and minimum average partial criteria suggested that the best number of domains is equal to 3, explaining 50.4% of the total variance. Based on the results obtained from the Rasch analysis, items 10, 11, 16, 22, and 23 have been removed resulting in a short-form questionnaire (Walsh-IT-R) of 26 items with three domains: shared beliefs and support (SBS, \u3b1=0.928); family organization and interaction (FOI, \u3b1=0.863); and utilization of social resources (USR, \u3b1=0.567). The total score of the Walsh-IT-R was strongly correlated with the total score of FACES III Real Family Scale (r=0.68; p,0.0001). Conclusion: Results support that the Walsh-IT-R is a valid instrument for the assessment of family resilience in Italy when contending with the challenges of chronic disease. It could be used in pre- and post-assessment in practice effectiveness research, offering a profile of family resilience processes at the start and end of interventions and follow-up

Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum.

Autism is a common developmental condition with a wide, variable range of co-occurring neuropsychiatric symptoms. Contrasting with most extant studies, we explored whole-brain functional organization at multiple levels simultaneously in a large subject group reflecting autism's clinical diversity, and present the first network-based analysis of transient brain states, or dynamic connectivity, in autism. Disruption to inter-network and inter-system connectivity, rather than within individual networks, predominated. We identified coupling disruption in the anterior-posterior default mode axis, and among specific control networks specialized for task start cues and the maintenance of domain-independent task positive status, specifically between the right fronto-parietal and cingulo-opercular networks and default mode network subsystems. These appear to propagate downstream in autism, with significantly dampened subject oscillations between brain states, and dynamic connectivity configuration differences. Our account proposes specific motifs that may provide candidates for neuroimaging biomarkers within heterogeneous clinical populations in this diverse condition

Validity, reliability, and diagnostic cut-off of the Kinyarwandan version of the Hamilton depression rating scale in Rwanda

Introduction: In Rwanda, major depressive disorder affects 11.9% of the population and up to 35% of genocide survivors. Mental health services remain underutilized due to stigma and lack of awareness. Increasing the ability and capacity to diagnose and treat mental disorders is considered important to close this gap. We describe the translation, validity, and reliability assessment of the Hamilton Depression Rating Scale (HDRS) as a diagnostic tool for moderate to severe depression in Rwanda. Methods: The HDRS-21 was translated by a multi-group taskforce. We validated the translation against expert assessment in a comparative study on a sample of patients living with depression and of healthy volunteers. Psychometric properties, namely internal structure, reliability, and external validity were assessed using confirmatory factor analysis, three reliability calculations, and correlation analysis, respectively. Maximized Youden's index was used for determining diagnostic cut-off. Results: The translated version demonstrated a kappa of 0.93. We enrolled 105 healthy volunteers and 105 patients with confirmed mild to severe depression. In the confirmatory factor analysis, HDRS had good factor loadings of 0.32-0.80. Reliability coefficients above 0.92 indicated strong internal consistency. External validity was shown by good sensitivity (0.95) and specificity (0.94) to differentiate depression from absence of depression. At a cut-off point of 17 for the diagnosis of depression, sensitivity and specificity were both 0.95 relative to gold standard. Conclusion: The validated HDRS in Kinyarwanda with diagnostic cut-off provides mental healthcare staff with an accurate tool to diagnose moderate to severe depression, enabling closure of the diagnosis and treatment gap

Network structures and temporal stability of self- and informant-rated affective symptoms in Alzheimer's disease

Background: Affective symptoms in Alzheimer's disease (AD) can be rated with both informantand self-ratings. Information from these two modalities may not converge. We estimated network structures of affective symptoms in AD with both rating modalities and assessed the longitudinal stability of the networks. Methods: Network analyses combining self-rated and informant-rated affective symptoms were conducted in 3198 individuals with AD at two time points (mean follow-up 387 days), drawn from the NACC database. Self rated symptoms were assessed by Geriatric Depression Scale, and informant-rated symptoms included depression, apathy and anxiety questions from Neuropsychiatric Inventory Questionnaire. Results: Informant-rated symptoms were mainly connected to symptoms expressing lack of positive affect, but not to the more central symptoms of self-rated worthlessness and helplessness. Networks did not differ in structure (p = .71), or connectivity (p = .92) between visits. Symptoms formed four clinically meaningful clusters of depressive symptoms and decline, lack of positive affect, informant-rated apathy and anxiety and informant-rated depression. Limitations: The symptom dynamics in our study could have been present before AD diagnosis. The lack of positive affect cluster may represent a methodological artefact rather than a theoretically meaningful subgroup. Requiring follow-up lead to a selection of patients with less cognitive decline. Conclusions: Informant rating may only capture the more visible affective symptoms, such as not being in good spirits, instead of more central and severe symptoms, such as hopelessness and worthlessness. Future research should continue to be mindful of differences between self- and informant-rated symptoms even in earlier stages of AD.Peer reviewe

Translational genetic modelling of 3D craniofacial dysmorphology: elaborating the facial phenotype of neurodevelopmental disorders through the prism of schizophrenia

Purpose of Review: In the context of human developmental conditions, we review the conceptualisation of schizophrenia as a neurodevelopmental disorder, the status of craniofacial dysmorphology as a clinically accessible index of brain dysmorphogenesis, the ability of genetically modified mouse models of craniofacial dysmorphology to inform on the underlying dysmorphogenic process and how geometric morphometric techniques in mutant mice can extend quantitative analysis. Recent Findings: Mutant mice with disruption of neuregulin-1, a gene associated meta-analytically with risk for schizophrenia, constitute proof-of-concept studies of murine facial dysmorphology in a manner analogous to clinical studies in schizophrenia. Geometric morphometric techniques informed on the topography of facial dysmorphology and identified asymmetry therein. Summary: Targeted disruption in mice of genes involved in individual components of developmental processes and analysis of resultant facial dysmorphology using geometric morphometrics can inform on mechanisms of dysmorphogenesis at levels of incisiveness not possible in human subjects

Using network analysis for the prediction of treatment dropout in patients with mood and anxiety disorders: a methodological proof-of-concept study

There are large health, societal, and economic costs associated with attrition from psychological services. The recently emerged, innovative statistical tool of complex network analysis was used in the present proof-of-concept study to improve the prediction of attrition. Fifty-eight patients undergoing psychological treatment for mood or anxiety disorders were assessed using Ecological Momentary Assessments four times a day for two weeks before treatment (3,248 measurements). Multilevel vector autoregressive models were employed to compute dynamic symptom networks. Intake variables and network parameters (centrality measures) were used as predictors for dropout using machine-learning algorithms. Networks for patients differed significantly between completers and dropouts. Among intake variables, initial impairment and sex predicted dropout explaining 6% of the variance. The network analysis identified four additional predictors: Expected force of being excited, outstrength of experiencing social support, betweenness of feeling nervous, and instrength of being active. The final model with the two intake and four network variables explained 32% of variance in dropout and identified 47 out of 58 patients correctly. The findings indicate that patients’ dynamic network structures may improve the prediction of dropout. When implemented in routine care, such prediction models could identify patients at risk for attrition and inform personalized treatment recommendations.This work was supported by the German Research Foundation National Institute (DFG, Grant nos. LU 660/8-1 and LU 660/10-1 to W. Lutz). The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript. The corresponding author had access to all data in the study and had final responsibility for the decision to submit for publication. Dr. Hofmann receives financial support from the Alexander von Humboldt Foundation (as part of the Humboldt Prize), NIH/NCCIH (R01AT007257), NIH/NIMH (R01MH099021, U01MH108168), and the James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition - Special Initiative. (LU 660/8-1 - German Research Foundation National Institute (DFG); LU 660/10-1 - German Research Foundation National Institute (DFG); Alexander von Humboldt Foundation; R01AT007257 - NIH/NCCIH; R01MH099021 - NIH/NIMH; U01MH108168 - NIH/NIMH; James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition - Special Initiative)Accepted manuscrip

Predicting Mental Conditions Based on History of Present Illness in Psychiatric Notes with Deep Neural Networks

Background—Applications of natural language processing to mental health notes are not common given the sensitive nature of the associated narratives. The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) changed this scenario by providing the first set of neuropsychiatric notes to participants. This study summarizes our efforts and results in proposing a novel data use case for this dataset as part of the third track in this shared task. Objective—We explore the feasibility and effectiveness of predicting a set of common mental conditions a patient has based on the short textual description of patient’s history of present illness typically occurring in the beginning of a psychiatric initial evaluation note. Materials and Methods—We clean and process the 1000 records made available through the N-GRID clinical NLP task into a key-value dictionary and build a dataset of 986 examples for which there is a narrative for history of present illness as well as Yes/No responses with regards to presence of specific mental conditions. We propose two independent deep neural network models: one based on convolutional neural networks (CNN) and another based on recurrent neural networks with hierarchical attention (ReHAN), the latter of which allows for interpretation of model decisions. We conduct experiments to compare these methods to each other and to baselines based on linear models and named entity recognition (NER). Results—Our CNN model with optimized thresholding of output probability estimates achieves best overall mean micro-F score of 63.144% for 11 common mental conditions with statistically significant gains (p \u3c 0.05) over all other models. The ReHAN model with interpretable attention mechanism scored 61.904% mean micro-F1 score. Both models’ improvements over baseline models (support vector machines and NER) are statistically significant. The ReHAN model additionally aids in interpretation of the results by surfacing important words and sentences that lead to a particular prediction for each instance