Conduct disorder (CD) is a common but complex psychiatric disorder characterized by aggressive and destructive behavior. Factors contributing to the development of CD span biological, psychological and social domains. Researchers have identified a myriad of risk factors that could help predict CD, but they are often considered in isolation. Now, a new study uses a machine learning approach for the first time to assess risk factors in all three domains in combination and predict the later development of CD with high accuracy.
The study appears in Biological psychiatry: cognitive neuroscience and neuroimagingedited by Elsevier.
The researchers used baseline data from more than 2,300 children between the ages of 9 and 10 enrolled in the Adolescent Brain Cognitive Development (ABCD) study, a longitudinal study tracking children’s biopsychosocial development. The researchers “trained” their machine learning model using previously identified risk factors across several biopsychosocial domains. For example, measures included brain imagery (biological), cognitive abilities (psychological), and family characteristics (social). The model correctly predicted the development of CD two years later with over 90% accuracy.
Cameron Carter, MD, editor of Biological psychiatry: cognitive neuroscience and neuroimagingsaid of the study: “These striking results using functional task-based MRI to study reward system function suggest that the risk of later depression in children of depressed mothers may be more dependent on the mothers’ responses. to the emotional behavior of their children than to the mood of the mother in itself. »
The ability to accurately predict who might develop CD would help researchers and healthcare workers design interventions for at-risk youth that could minimize or even prevent the harmful effects of CD on children and their families.
“The results of our study highlight the added value of the combination of neural, social and psychological factors in predicting conduct disorder, a serious psychiatric problem in young people,” said lead author Arielle Baskin-Sommers, PhD. at Yale University, New Haven, CT, USA. “These findings hold promise for the development of more specific identification and intervention approaches that take into account the multiple factors that contribute to this disorder. They also highlight the value of leveraging large open datasets, such as ABCD, which collect metrics about the individual at all levels. analysis. »
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A very precise model uses factors in all biopsychosocial domains – Psychology and Psychiatry News
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