Traditional mental health diagnoses rely on symptom-based classifications. Yet this approach can oversimplify clinical presentations as diagnoses often do not adequately map onto neurobiological features. Alternatively, our study used structural imaging data and a semisupervised machine learning technique, heterogeneity through discriminative analysis, to identify neurobiological subtypes in 9- to 10-year-olds with high psychopathology endorsements (n = 9,027). Our model revealed two stable neurobiological subtypes (adjusted Rand index = 0.38). Subtype 1 showed smaller structural properties, elevated conduct problems and attention-deficit/hyperactivity disorder symptoms, and impaired cognitive performance compared to Subtype 2 and typically developing youth. Subtype 2 had larger structural properties, cognitive abilities comparable to typically developing youth, and elevated internalizing symptoms relative to Subtype 1 and typically developing youth. These subtypes remained stable in their neurobiological characteristics, cognitive ability, and associated psychopathology traits over time. Taken together, our data-driven approach uncovered evidence of neural heterogeneity as demonstrated by structural patterns that map onto divergent profiles of psychopathology symptoms and cognitive performance in youth. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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使用机器学习推导出儿童晚期一般精神病理学的神经生物学亚型。


传统的心理健康诊断依赖于基于症状的分类。然而，这种方法可能会过度简化临床表现，因为诊断通常不能充分映射到神经生物学特征。或者，我们的研究使用结构成像数据和半监督机器学习技术，通过判别分析的异质性，来识别具有高精神病理学认可的 9 至 10 岁儿童的神经生物学亚型 （n = 9,027）。我们的模型揭示了两种稳定的神经生物学亚型 （调整后的 Rand 指数 = 0.38）。与亚型 2 相比，亚型 1 表现出较小的结构特性、行为问题升高和注意力缺陷/多动障碍症状，以及认知能力受损，与亚型 2 相比，通常发育中的青年。亚型 2 具有更大的结构特性，认知能力与正常发育的青年相当，并且相对于亚型 1 和通常发育的青年，内化症状升高。随着时间的推移，这些亚型的神经生物学特征、认知能力和相关的精神病理学特征保持稳定。综上所述，我们的数据驱动方法发现了神经异质性的证据，正如映射到青少年精神病理学症状和认知表现的不同特征的结构模式所证明的那样。（PsycInfo 数据库记录 （c） 2024 APA，保留所有权利）。