Traces of trauma – a multivariate pattern analysis of childhood trauma, brain structure and clinical phenotypes

Background: Childhood trauma (CT) is a major yet elusive psychiatric risk factor, whose multidimensional conceptualization and heterogeneous effects on brain morphology might demand advanced mathematical modeling. Therefore, we present an unsupervised machine learning approach to characterize the clinical and neuroanatomical complexity of CT in a larger, transdiagnostic context. Methods: We used a multicenter European cohort of 1076 female and male individuals (discovery: n = 649; replication: n = 427) comprising young, minimally medicated patients with clinical high-risk states for psychosis; patients with recent-onset depression or psychosis; and healthy volunteers. We employed multivariate sparse partial least squares analysis to detect parsimonious associations between combinations of items from the Childhood Trauma Questionnaire and gray matter volume and tested their generalizability via nested cross-validation as well as via external validation. We investigated the associations of these CT signatures with state (functioning, depressivity, quality of life), trait (personality), and sociodemographic levels. Results: We discovered signatures of age-dependent sexual abuse and sex-dependent physical and sexual abuse, as well as emotional trauma, which projected onto gray matter volume patterns in prefronto-cerebellar, limbic, and sensory networks. These signatures were associated with predominantly impaired clinical state- and trait-level phenotypes, while pointing toward an interaction between sexual abuse, age, urbanicity, and education. We validated the clinical profiles for all three CT signatures in the replication sample. Conclusions: Our results suggest distinct multilayered associations between partially age- and sex-dependent patterns of CT, distributed neuroanatomical networks, and clinical profiles. Hence, our study highlights how machine learning approaches can shape future, more fine-grained CT research

Validation of a German Version of the Resilience Scale for Adults (RSA)

Our study aimed at investigating the psychometric properties of a German version of the Resilience Scale for Adults (RSA). The RSA is a multifactorial resilience questionnaire, which measures dispositional resources, family support, and external supporting systems. To date, it was not possible to assess resilience as a multifactorial construct in German speaking countries. A non-clinical sample (N = 524) took part in a cross-sectional, Internet-based survey. Additionally, in an explorative approach, a clinical sample of patients with recent-onset depression (N = 69) were investigated. A confirmatory factor analysis supported the six-factor solution suggested for the original RSA. Further, the results indicated moderate-to-high internal consistencies for the whole scale and all but one subscale. Construct validity was demonstrated by significant correlations between the RSA and another resilience scale as welt as scales assessing perceived social support and general psychopathology. Group comparisons revealed significant differences between patients and healthy controls. The German version of the RSA is a valid and suitable questionnaire for assessing factors protecting mental health

Health literacy in clinical-high-risk individuals for psychosis: A systematic mixed-methods review

Objective Numerous studies suggest that health literacy (HL) plays a crucial role in maintaining and improving individual health. Empirical findings highlight the relation between levels of a person's HL and her/his clinical outcome. To date, the role of HL in persons at-risk for psychosis has not been systematically reviewed. Methods We conducted a systematic review using a mixed-methods approach to analyse a variety of study types. Peer-reviewed publications were systematically searched in PUBMED, Cochrane Library, PsycINFO and Web of Science. Results The search string returned 10587 publications. After screening, 15 quantitative, four qualitative studies and two reviews were included. Only one study assessed HL as primary outcome, assessing knowledge and beliefs about psychosis among the general population. In the other studies, sub-dimensions of HL were investigated. None of the publications operationalized HL or it's sub-dimensions with a validated measure. Conclusions A lack of understanding of their condition, and fear of stigmatization, were associated with a delay in help-seeking among people with clinical-high-risk state for psychosis. Family members, school personnel, general practitioners and the internet play a crucial role in the HL process. Considerable barriers in obtaining adequate specialist support emphasize the urgent need of a HL environment for persons at risk for psychosis

SINGLE-SUBJECT PREDICTION OF FUNCTIONAL OUTCOMES ACROSS DIAGNOSTIC GROUPS USING CLINICAL DATA

Abstract Background Psychotic disorders are associated with serious deterioration in functioning even before the first psychotic episode. Also on clinical high risk (CHR) states of developing a first psychotic episode, several studies reported a decreased global functioning. In a considerable proportion of CHR individuals, functional deterioration remains even after (transient) remission of symptomatic risk indicators. Furthermore, deficits in functioning cause immense costs for the health care system and are often more debilitating for individuals than positive symptoms. However in the past, CHR research has mostly focused on clinical outcomes like transition. Prediction of functioning in CHR populations has received less attention. Therefore, the current study aims at predicting functioning in CHR individuals at a single subject level applying multi pattern recognition to clinical data. Patients with a first depressive episode who frequently have persistent functional deficits comparable to patients in the CHR state were investigated in addition. Methods PRONIA (‘Personalized Prognostic Tools for Early Psychosis Management’) is a prospective collaboration project funded by the European Union under the 7th Framework Programme (grant agreement n°602152). Considering a broad set of variables (MRI, clinical data, neurocognition, genomics and other blood derived parameters) as well as advanced statistical methods, PRONIA aims at developing an innovative multivariate prognostic tool enabling an individualized prediction of illness trajectories and outcome. 11 university centers in five European countries and in Australia (Munich, Basel, Birmingham, Cologne, Düsseldorf, Münster, Melbourne, Milan, Udine, Bari, Turku) participate in the evaluation of three clinical groups (subjects clinically at high risk of developing a psychosis [CHR], patients with a recent onset psychosis [ROP] and patients with a recent onset depression [ROD]) as well as healthy controls. In the current study, we analysed data of 114 CHR and 106 ROD patients. Functioning was measured by the ‘Global Functioning: Social and Role’ Scales (GF S/R). In a repeated, nested cross validation framework we trained a l1-regularized SVM to predict good versus bad outcome. Multivariate pattern recognition analysis allowed to identify most predictive variables from a multitude of clinical, environmental as well as sociodemographic potential predictors assessed in PRONIA. Results Based on the 5 to 20 identified most predictive features, prediction models revealed a balanced accuracy (BAC) up to 77/72 for social functioning in CHR/ROD patients and up to 73/69 for role functioning. These models showed satisfying performance of BACs up to 69/63 for social functioning and 67/60 for role functioning in an independent test sample. As expected, prior functioning levels were identified as main predictive factor but also distinct protective and risk factors were selected into the prediction models. Discussion Results suggest that especially prediction of the multi-faceted construct of role functioning could benefit from inclusion of a rich set of clinical variables. To the best of our knowledge this is the first study that has validated clinical prediction models of functioning in an independent test sample. Identification of predictive variables enables a much more efficient prognostic process. Moreover, understanding the mechanisms underlying functional decline and its illness related pattern might enable an improved definition of targets for intervention. Future research should aim at further maximisation of prediction accuracy and cross-centre generalisation capacity. In addition, other functioning outcomes as well as clinical outcomes need to be focused on