BACKGROUND Childhood trauma (CT) is a major yet elusive psychiatric risk factor, whose multidimensional conceptualization and heterogeneous effects on brain morphology might demand advanced mathematical modeling. Therefore, we present an unsupervised machine learning approach to characterize the clinical and neuroanatomical complexity of CT in a larger, transdiagnostic context. METHODS We used a multicenter European cohort of 1076 female and male individuals (discovery: n = 649; replication: n = 427) comprising young, minimally medicated patients with clinical high-risk states for psychosis; patients with recent-onset depression or psychosis; and healthy volunteers. We employed multivariate sparse partial least squares analysis to detect parsimonious associations between combinations of items from the Childhood Trauma Questionnaire and gray matter volume and tested their generalizability via nested cross-validation as well as via external validation. We investigated the associations of these CT signatures with state (functioning, depressivity, quality of life), trait (personality), and sociodemographic levels. RESULTS We discovered signatures of age-dependent sexual abuse and sex-dependent physical and sexual abuse, as well as emotional trauma, which projected onto gray matter volume patterns in prefronto-cerebellar, limbic, and sensory networks. These signatures were associated with predominantly impaired clinical state- and trait-level phenotypes, while pointing toward an interaction between sexual abuse, age, urbanicity, and education. We validated the clinical profiles for all three CT signatures in the replication sample. CONCLUSIONS Our results suggest distinct multilayered associations between partially age- and sex-dependent patterns of CT, distributed neuroanatomical networks, and clinical profiles. Hence, our study highlights how machine learning approaches can shape future, more fine-grained CT research.

中文翻译：

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创伤痕迹——童年创伤、脑结构和临床表型的多变量模式分析

背景童年创伤 (CT) 是一个主要但难以捉摸的精神疾病风险因素，其多维概念化和对大脑形态的异质影响可能需要先进的数学建模。因此，我们提出了一种无监督的机器学习方法，以在更大的跨诊断背景下表征 CT 的临床和神经解剖学复杂性。方法 我们使用了一个由 1076 名女性和男性组成的多中心欧洲队列（发现：n = 649；复制：n = 427），其中包括具有临床精神病高危状态的年轻、最少用药患者；近期出现抑郁症或精神病的患者；和健康的志愿者。我们采用多元稀疏偏最小二乘分析来检测来自童年创伤问卷和灰质体积的项目组合之间的简约关联，并通过嵌套交叉验证和外部验证测试它们的普遍性。我们调查了这些 CT 特征与状态（功能、抑郁、生活质量）、特征（个性）和社会人口统计学水平的关联。结果我们发现了年龄相关性虐待和性别相关身体和性虐待以及情绪创伤的特征，这些特征投射到前额小脑、边缘和感觉网络的灰质体积模式上。这些特征与主要受损的临床状态和特征水平的表型有关，同时指向性虐待之间的相互作用，年龄、城市化和教育。我们验证了复制样本中所有三个 CT 特征的临床特征。结论 我们的结果表明，部分年龄和性别依赖性 CT 模式、分布式神经解剖网络和临床特征之间存在明显的多层关联。因此，我们的研究强调了机器学习方法如何塑造未来更细粒度的 CT 研究。