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 modelling. 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 multi-center 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 grey matter volume (GMV) and tested their generalizability via nested cross-validation as well as 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, sex-dependent physical and sexual abuse as well as emotional trauma, which projected onto GMV patterns in prefrontocerebellar, limbic and sensory networks. These signatures were associated with predominantly impaired clinical state- and trait-level phenotypes, while pointing towards 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 multi-layered 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.

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

Antonucci, Linda A.;Pergola, Giulio;Blasi, Giuseppe;Bertolino, Alessandro;
2020-01-01

Abstract

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 modelling. 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 multi-center 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 grey matter volume (GMV) and tested their generalizability via nested cross-validation as well as 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, sex-dependent physical and sexual abuse as well as emotional trauma, which projected onto GMV patterns in prefrontocerebellar, limbic and sensory networks. These signatures were associated with predominantly impaired clinical state- and trait-level phenotypes, while pointing towards 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 multi-layered 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/299410
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