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

"But I'm not going to be a mental health nurse": nursing students' perceptions of the influence of experts by experience on their attitudes to mental health nursing

Published online 18 October 2019Background: Mental health nursing skills and knowledge are vital for the provision of high-quality healthcare across all settings. Negative attitudes of nurses, towards both mental illness and mental health nursing as a profession, limit recognition of the value of these skills and knowledge. Experts by Experience have a significant role in enhancing mental health nursing education. The impact of this involvement on attitudes to mental health nursing has not been well researched. Aim: To explore the impact of Expert by Experience-led teaching on students' perceptions of mental health nursing. Methods: Qualitative exploratory study involving focus groups with nursing students from five European countries and Australia. Results: Following Expert by Experience-led teaching, participants described more positive views towards mental health nursing skills and knowledge in three main ways: learning that mental health is everywhere, becoming better practitioners, and better appreciation of mental health nursing. Conclusions: Experts by experience contribute to promoting positive attitudinal change in nursing students towards mental health nursing skills and knowledge. Attitudinal change is essential for the provision of high-quality mental health care in specialist mental health services and throughout the healthcare sector.Brenda Happell ... Brett Scholz ... et al