The reproducibility of machine-learning analyses in computational psychiatry is a growing concern. In a multimodal neuropsychiatric dataset of antipsychotic-naïve, first-episode schizophrenia patients, we discuss a workflow aimed at reducing bias and overfitting by invoking simulated data in the design process and analysis in two independent machine-learning approaches, one based on a single algorithm and the other incorporating an ensemble of algorithms. We aimed to (1) classify patients from controls to establish the framework, (2) predict short- and long-term treatment response, and (3) validate the methodological framework. We included 138 antipsychotic-naïve, first-episode schizophrenia patients with data on psychopathology, cognition, electrophysiology, and structural magnetic resonance imaging (MRI). Perinatal data and long-term outcome measures were obtained from Danish registers. Short-term treatment response was defined as change in Positive And Negative Syndrome Score (PANSS) after the initial antipsychotic treatment period. Baseline diagnostic classification algorithms also included data from 151 matched controls. Both approaches significantly classified patients from healthy controls with a balanced accuracy of 63.8% and 64.2%, respectively. Post-hoc analyses showed that the classification primarily was driven by the cognitive data. Neither approach predicted short- nor long-term treatment response. Validation of the framework showed that choice of algorithm and parameter settings in the real data was successfully guided by results from the simulated data. In conclusion, this novel approach holds promise as an important step to minimize bias and obtain reliable results with modest sample sizes when independent replication samples are not available.

计算精神病学中机器学习分析的可重复性越来越受到关注。在抗精神病药物初治的首发精神分裂症患者的多模式神经精神病学数据集中，我们讨论了一种工作流程，旨在通过在设计过程中调用模拟数据并在两种独立的机器学习方法中进行分析来减少偏差和过度拟合，其中一种基于单一算法另一个包含一组算法。我们旨在 (1) 将患者与对照组进行分类以建立框架，(2) 预测短期和长期治疗反应，以及 (3) 验证方法框架。我们纳入了 138 名未接受抗精神病药物治疗的首发精神分裂症患者，他们提供了有关精神病理学、认知、电生理学和结构磁共振成像 (MRI) 的数据。从丹麦登记处获得围产期数据和长期结果测量。短期治疗反应定义为初始抗精神病药物治疗期后阳性和阴性综合征评分 (PANSS) 的变化。基线诊断分类算法还包括来自 151 个匹配对照的数据。两种方法均显着地将患者与健康对照组进行了分类，平衡准确率分别为 63.8% 和 64.2%。事后分析表明，分类主要是由认知数据驱动的。两种方法都不能预测短期或长期治疗反应。框架的验证表明，模拟数据的结果成功地指导了真实数据中算法和参数设置的选择。综上所述，