Traces of Trauma: A Multivariate Pattern Analysis of Childhood Trauma, Brain Structure, and Clinical Phenotypes.

Brain / diagnostic imaging Brain Injuries, Traumatic Child Female Gray Matter Humans Male Phenotype Quality of Life

Journal

Biological psychiatry

ISSN: 1873-2402

Titre abrégé: Biol Psychiatry

Pays: United States

ID NLM: 0213264

Informations de publication

Date de publication:
01 12 2020

Historique:

received: 23 10 2019

revised: 02 05 2020

accepted: 04 05 2020

pubmed: 13 8 2020

medline: 9 3 2021

entrez: 13 8 2020

Statut: ppublish

Résumé

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. 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. 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. 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.

Sections du résumé

BACKGROUND

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.

Identifiants

DOI: 10.1016/j.biopsych.2020.05.020 PMID: 32782139

pubmed: 32782139

pii: S0006-3223(20)31626-7

doi: 10.1016/j.biopsych.2020.05.020

pii:

doi:

Types de publication

Journal Article Multicenter Study Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

Pagination

829-842

Investigateurs

Mark Sen Dong (MS)

Anne Erkens (A)

Eva Gussmann (E)

Shalaila Haas (S)

Alkomiet Hasan (A)

Claudius Hoff (C)

Ifrah Khanyaree (I)

Aylin Melo (A)

Susanna Muckenhuber-Sternbauer (S)

Janis Köhler (J)

Ömer Faruk Öztürk (ÖF)

Nora Penzel (N)

Adrian Rangnick (A)

Sebastian von Saldern (S)

Rachele Sanfelici (R)

Moritz Spangemacher (M)

Ana Tupac (A)

Maria Fernanda Urquijo (MF)

Johanna Weiske (J)

Julian Wenzel (J)

Antonia Wosgien (A)

Linda Betz (L)

Karsten Blume (K)

Mauro Seves (M)

Nathalie Kaiser (N)

Thorsten Lichtenstein (T)

Christiane Woopen (C)

Christina Andreou (C)

Laura Egloff (L)

Fabienne Harrisberger (F)

Claudia Lenz (C)

Letizia Leanza (L)

Amatya Mackintosh (A)

Renata Smieskova (R)

Erich Studerus (E)

Anna Walter (A)

Sonja Widmayer (S)

Chris Day (C)

Sian Lowri Griffiths (SL)

Mariam Iqbal (M)

Mirabel Pelton (M)

Pavan Mallikarjun (P)

Alexandra Stainton (A)

Ashleigh Lin (A)

Alexander Denissoff (A)

Anu Ellilä (A)

Tiina From (T)

Markus Heinimaa (M)

Tuula Ilonen (T)

Päivi Jalo (P)

Heikki Laurikainen (H)

Maarit Lehtinen (M)

Antti Luutonen (A)

Akseli Mäkela (A)

Janina Paju (J)

Henri Pesonen (H)

Reetta-Liina Armio Säilä (RL)

Elina Sormunen (E)

Anna Toivonen (A)

Otto Turtonen (O)

Ana Beatriz Solana (AB)

Manuela Abraham (M)

Nicolas Hehn (N)

Timo Schirmer (T)

Carlo Altamura (C)

Marika Belleri (M)

Francesca Bottinelli (F)

Adele Ferro (A)

Marta Re (M)

Emiliano Monzani (E)

Mauro Percudani (M)

Maurizio Sberna (M)

Armando D'Agostino (A)

Lorenzo Del Fabro (L)

Giampaolo Perna (G)

Maria Nobile (M)

Alessandra Alciati (A)

Matteo Balestrieri (M)

Carolina Bonivento (C)

Giuseppe Cabras (G)

Franco Fabbro (F)

Marco Garzitto (M)

Sara Piccin (S)

Commentaires et corrections

Type : CommentIn