Abstract
Background: Theoretical models of conduct disorder (CD) highlight that deficits in emotion recognition, learning, and regulation play a pivotal role in CD etiology. With CD being more prevalent in boys than girls, various theories aim to explain this sex difference. The “differential threshold” hypothesis suggests greater emotion dysfunction in conduct-disordered girls than boys, but previous research using conventional statistical analyses has failed to support this hypothesis. Here, we used novel analytic techniques such as machine learning (ML) to uncover potentially sex-specific differences in emotion dysfunction among girls and boys with CD compared to their neurotypical peers. Methods: Multi-site data from 542 youth with CD and 710 neurotypical controls (64% girls, 9–18 years) who completed emotion recognition, learning, and regulation tasks were analyzed using a multivariate ML classifier to distinguish between youth with CD and controls separately by sex. Results: Both female and male ML classifiers accurately predicted (above chance level) individual CD status based solely on the neurocognitive features of emotion dysfunction. Notably, the female classifier outperformed the male classifier in identifying individuals with CD. However, the classification and identification performance of both classifiers was below the clinically relevant 80% accuracy threshold (although they still provided relatively fair and realistic estimates of ~ 60% classification performance), probably due to the substantial neurocognitive heterogeneity within such a large and diverse, multi-site sample of youth with CD (and neurotypical controls). Conclusions: These findings confirm the close association between emotion dysfunction and CD in both sexes, with a stronger association observed in affected girls, which aligns with the “differential threshold” hypothesis. However, the data also underscore the heterogeneity of CD, namely that only a subset of those affected are likely to have emotion dysfunction and that other neurocognitive domains (not tested here) probably also contribute to CD etiology. Clinical trial number: Not applicable.
| Original language | English |
|---|---|
| Article number | 105 |
| Journal | BMC Psychiatry |
| Volume | 25 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 6 Feb 2025 |
Data Availability Statement
Data are available from the corresponding author(s) on reasonable request.Acknowledgements
We thank all members of the FemNAT-CD consortium for their contributions to the project. In particular, we thank our participants, their families, and the many professionals who gave their time generously. We also thank Dr. Josefine Rothe, who was partially funded by a BBRF Grant (Grant No. 30849; PI: Gregor Kohls, PhD).Funding
Open Access funding enabled and organized by Projekt DEAL. This study was funded by the European Commission under the 7th Framework Health Program, Grant Agreement no. 602407. GK and EME were supported by a 2023 NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation (BBRF; Grant No. 30849; PI: GK). RP was supported by the Biotechnology and Biological Sciences Research Council’s Midlands Integrative Biosciences Training Partnership (BBSRC MIBTP) and an ESRC post-doctoral fellowship award. SADB was supported by an ESRC grant (ES/V003526/1). This work was also supported by the Federal Ministry of Education and Research (BMBF) as part of the German Center for Child and Adolescent Health (DZKJ) under the funding code 01GL2405B.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Conduct disorder
- Emotion dysfunction
- Emotion processing
- FemNAT-CD
- Machine learning
- Sex differences
- Youth
ASJC Scopus subject areas
- Psychiatry and Mental health
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