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Life Sciences

Examination of the association between exposure to childhood maltreatment and brain structure in young adults: a machine learning analysis

Published on - Neuropsychopharmacology

Authors: Matthew Price, Matthew Albaugh, Sage Hahn, Anthony Juliano, Negar Fani, Zoe Brier, Alison Legrand, Katherine van Stolk-Cooke, Bader Chaarani, Alexandra Potter, Kelly Peck, Nicholas Allgaier, Tobias Banaschewski, Arun Bokde, Erin Burke Quinlan, Sylvane Desrivières, Herta Flor, Antoine Grigis, Penny Gowland, Andreas Heinz, Bernd Ittermann, Jean-Luc Martinot, Marie-Laure Paillère, Eric Artiges, Frauke Nees, Dimitri Papadopoulos Orfanos, Luise Poustka, Sarah Hohmann, Juliane Fröhner, Michael Smolka, Henrik Walter, Robert Whelan, Gunter Schumann, Hugh Garavan

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Exposure to maltreatment during childhood is associated with structural changes throughout the brain. However, the structural differences that are most strongly associated with maltreatment remain unclear given the limited number of whole-brain studies. The present study used machine learning to identify if and how brain structure distinguished young adults with and without a history of maltreatment. Young adults (ages 18-21, n = 384) completed an assessment of childhood trauma exposure and a structural MRI as part of the IMAGEN study. Elastic net regularized regression was used to identify the structural features that identified those with a history of maltreatment. A generalizable model that included 7 cortical thicknesses, 15 surface areas, and 5 subcortical volumes was identified (area under the receiver operating characteristic curve = 0.71, p