Machine learning reveals sex differences in distinguishing between conduct-disordered and neurotypical youth based on emotion processing dysfunction
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 thresho...
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BMC
2025-02-01
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| Series: | BMC Psychiatry |
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| Online Access: | https://doi.org/10.1186/s12888-025-06536-6 |
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| author | Gregor Kohls Erik M. Elster Peter Tino Graeme Fairchild Christina Stadler Arne Popma Christine M. Freitag Stephane A. De Brito Kerstin Konrad Ruth Pauli |
| author_facet | Gregor Kohls Erik M. Elster Peter Tino Graeme Fairchild Christina Stadler Arne Popma Christine M. Freitag Stephane A. De Brito Kerstin Konrad Ruth Pauli |
| author_sort | Gregor Kohls |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bad70f962c7640e28b2f0677154438bc |
| institution | Kabale University |
| issn | 1471-244X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Psychiatry |
| spelling | doaj-art-bad70f962c7640e28b2f0677154438bc2025-02-09T12:49:21ZengBMCBMC Psychiatry1471-244X2025-02-0125111010.1186/s12888-025-06536-6Machine learning reveals sex differences in distinguishing between conduct-disordered and neurotypical youth based on emotion processing dysfunctionGregor Kohls0Erik M. Elster1Peter Tino2Graeme Fairchild3Christina Stadler4Arne Popma5Christine M. Freitag6Stephane A. De Brito7Kerstin Konrad8Ruth Pauli9Department of Child and Adolescent Psychiatry, Faculty of Medicine, TUD Dresden University of Technology, German Center for Child and Adolescent Health (DZKJ), partner site Leipzig/DresdenDepartment of Child and Adolescent Psychiatry, Faculty of Medicine, TUD Dresden University of Technology, German Center for Child and Adolescent Health (DZKJ), partner site Leipzig/DresdenSchool of Computer Science, University of BirminghamDepartment of Psychology, University of BathDepartment of Child and Adolescent Psychiatry, Psychiatric University Hospital, University of BaselDepartment of Child and Adolescent Psychiatry, VU University Medical CenterDepartment of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Frankfurt, Goethe UniversityCentre for Human Brain Health, School of Psychology, University of BirminghamChild Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, RWTH Aachen UniversityCentre for Human Brain Health, School of Psychology, University of BirminghamAbstract 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.https://doi.org/10.1186/s12888-025-06536-6Conduct disorderEmotion processingEmotion dysfunctionMachine learningSex differencesYouth |
| spellingShingle | Gregor Kohls Erik M. Elster Peter Tino Graeme Fairchild Christina Stadler Arne Popma Christine M. Freitag Stephane A. De Brito Kerstin Konrad Ruth Pauli Machine learning reveals sex differences in distinguishing between conduct-disordered and neurotypical youth based on emotion processing dysfunction BMC Psychiatry Conduct disorder Emotion processing Emotion dysfunction Machine learning Sex differences Youth |
| title | Machine learning reveals sex differences in distinguishing between conduct-disordered and neurotypical youth based on emotion processing dysfunction |
| title_full | Machine learning reveals sex differences in distinguishing between conduct-disordered and neurotypical youth based on emotion processing dysfunction |
| title_fullStr | Machine learning reveals sex differences in distinguishing between conduct-disordered and neurotypical youth based on emotion processing dysfunction |
| title_full_unstemmed | Machine learning reveals sex differences in distinguishing between conduct-disordered and neurotypical youth based on emotion processing dysfunction |
| title_short | Machine learning reveals sex differences in distinguishing between conduct-disordered and neurotypical youth based on emotion processing dysfunction |
| title_sort | machine learning reveals sex differences in distinguishing between conduct disordered and neurotypical youth based on emotion processing dysfunction |
| topic | Conduct disorder Emotion processing Emotion dysfunction Machine learning Sex differences Youth |
| url | https://doi.org/10.1186/s12888-025-06536-6 |
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