Understanding the biological and behavioral heterogeneity underlying psychiatric disorders is critical for advancing precision diagnosis, treatment, and prevention. This paper addresses the scientific question of how multimodal data, spanning clinical, cognitive, and neuroimaging measures, can be integrated to identify biologically meaningful subtypes of mental disorders. We introduce Mixed INtegrative Data Subtyping (MINDS), a Bayesian hierarchical model designed to jointly analyze mixed-type data for simultaneous dimension reduction and clustering. Using data from the Adolescent Brain Cognitive Development (ABCD) Study, MINDS integrates clinical symptoms, cognitive performance, and brain structure measures to subtype Attention-Deficit/Hyperactivity Disorder (ADHD) and Obsessive-Compulsive Disorder (OCD). Our method leverages Polya-Gamma augmentation for computational efficiency and robust inference. Simulations demonstrate improved stability and accuracy compared to existing clustering approaches. Application to the ABCD data reveals clinically interpretable subtypes of ADHD and OCD with distinct cognitive and neurodevelopmental profiles. These findings show how integrative multimodal modeling can enhance the reproducibility and clinical relevance of psychiatric subtyping, supporting data-driven policies for early identification and targeted interventions in mental health.

翻译：理解精神障碍背后的生物学与行为异质性对于推进精准诊断、治疗和预防至关重要。本文探讨了一个科学问题：如何整合涵盖临床、认知和神经影像测量的多模态数据，以识别具有生物学意义的精神障碍亚型。我们提出了混合集成数据亚型分型（MINDS），这是一种贝叶斯层次模型，旨在联合分析混合类型数据，实现同步降维与聚类。利用青少年脑认知发展（ABCD）研究的数据，MINDS整合了临床症状、认知表现和脑结构测量，对注意缺陷/多动障碍（ADHD）和强迫症（OCD）进行亚型分型。我们的方法利用Polya-Gamma增强技术以提高计算效率和推理稳健性。模拟实验表明，与现有聚类方法相比，该方法在稳定性和准确性上均有提升。应用于ABCD数据揭示了ADHD和OCD具有不同认知与神经发育特征的临床可解释亚型。这些发现表明，集成多模态建模能够增强精神障碍亚型分型的可重复性与临床相关性，为心理健康领域的早期识别和针对性干预提供数据驱动的策略支持。