Department of Psychology, Yale University, New Haven, Connecticut.
Department of Psychology, University of Michigan Ann Arbor, Ann Arbor, Michigan.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2023 Jun;8(6):599-608. doi: 10.1016/j.bpsc.2022.02.004. Epub 2022 Feb 22.
Conduct disorder (CD) is a common syndrome with far-reaching effects. Risk factors for the development of CD span social, psychological, and biological domains. Researchers note that predictive models of CD are limited if the focus is on a single risk factor or even a single domain. Machine learning methods are optimized for the extraction of trends across multidomain data but have yet to be implemented in predicting the development of CD.
Social (e.g., family, income), psychological (e.g., psychiatric, neuropsychological), and biological (e.g., resting-state graph metrics) risk factors were measured using data from the baseline visit of the Adolescent Brain Cognitive Development Study when youth were 9 to 10 years old (N = 2368). Applying a feed-forward neural network machine learning method, risk factors were used to predict CD diagnoses 2 years later.
A model with factors that included social, psychological, and biological domains outperformed models representing factors within any single domain, predicting the presence of a CD diagnosis with 91.18% accuracy. Within each domain, certain factors stood out in terms of their relationship to CD (social: lower parental monitoring, more aggression in the household, lower income; psychological: greater attention-deficit/hyperactivity disorder and oppositional defiant disorder symptoms, worse crystallized cognition and card sorting performance; biological: disruptions in the topology of subcortical and frontoparietal networks).
The development of an accurate, sensitive, and specific predictive model of CD has the potential to aid in prevention and intervention efforts. Key risk factors for CD appear best characterized as reflecting unpredictable, impulsive, deprived, and emotional external and internal contexts.
品行障碍（CD）是一种具有深远影响的常见综合征。CD 的发展风险因素涉及社会、心理和生物等多个领域。研究人员指出，如果研究仅限于单一风险因素或单一领域，那么 CD 的预测模型将存在局限性。机器学习方法可优化多维数据中的趋势提取，但尚未应用于 CD 发展的预测中。
使用青少年大脑认知发展研究基线访视时青少年 9 至 10 岁时的数据（N=2368），测量社会（如家庭、收入）、心理（如精神科、神经心理学）和生物（如静息态图度量）风险因素。应用前馈神经网络机器学习方法，使用风险因素预测 2 年后 CD 的诊断。
一个包含社会、心理和生物领域因素的模型优于代表单一领域因素的模型，对 CD 诊断的准确率为 91.18%。在每个领域中，某些因素与 CD 的关系更为突出（社会：父母监控较少，家庭中攻击性更强，收入较低；心理：注意力缺陷多动障碍和对立违抗性障碍症状更严重，晶体认知和卡片分类表现更差；生物：皮质下和额顶网络拓扑的中断）。
开发一种准确、敏感、特异的 CD 预测模型有可能有助于预防和干预工作。CD 的关键风险因素似乎最好被描述为反映不可预测、冲动、贫困和情绪化的外部和内部环境。
