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

Research output: Contribution to journalArticlepeer-review

Authors

  • PRONIA Consortium
  • Popovic David
  • Anne Ruef
  • Dominic B. Dwyer
  • Linda A. Antonucci
  • Julia Eder
  • Rachele Sanfelici
  • Lana Kambeitz-Ilankovic
  • Omer Faruk Oztuerk
  • Mark S. Dong
  • Riya Paul
  • Marco Paolini
  • Dennis Hedderich
  • Theresa Haidl
  • Joseph Kambeitz
  • Stephan Ruhrmann
  • Katie Chisholm
  • Frauke Schultze-Lutter
  • Peter Falkai
  • Giulio Pergola
  • Giuseppe Blasi
  • Alessandro Bertolino
  • Rebekka Lencer
  • Udo Dannlowski
  • Rachel Upthegrove
  • Raimo K.r. Salokangas
  • Christos Pantelis
  • Eva Meisenzahl
  • Stephen J Wood
  • Paolo Brambilla
  • Stefan Borgwardt
  • Nikolaos Koutsouleris

External organisations

  • University of Munich

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 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.

Details

Original languageEnglish
Pages (from-to)829-842
Number of pages14
JournalBiological Psychiatry
Volume88
Issue number11
Early online date26 May 2020
Publication statusPublished - 1 Dec 2020

Keywords

  • childhood trauma, transdiagnostic, machine learning, sparse partial least squares, morphometry, MRI, Machine learning, Transdiagnostic, Morphometry, Childhood trauma, Sparse partial least squares

ASJC Scopus subject areas