Newson Jennifer J, Bala Jerzy, Giedd Jay N, Maxwell Benjamin, Thiagarajan Tara C
Sapien Labs, Arlington, VA, United States.
Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States.
Front Psychiatry. 2024 Feb 19;15:1337740. doi: 10.3389/fpsyt.2024.1337740. eCollection 2024.
Over the past 30 years there have been numerous large-scale and longitudinal psychiatric research efforts to improve our understanding and treatment of mental health conditions. However, despite the huge effort by the research community and considerable funding, we still lack a causal understanding of most mental health disorders. Consequently, the majority of psychiatric diagnosis and treatment still operates at the level of symptomatic experience, rather than measuring or addressing root causes. This results in a trial-and-error approach that is a poor fit to underlying causality with poor clinical outcomes. Here we discuss how a research framework that originates from exploration of causal factors, rather than symptom groupings, applied to large scale multi-dimensional data can help address some of the current challenges facing mental health research and, in turn, clinical outcomes. Firstly, we describe some of the challenges and complexities underpinning the search for causal drivers of mental health conditions, focusing on current approaches to the assessment and diagnosis of psychiatric disorders, the many-to-many mappings between symptoms and causes, the search for biomarkers of heterogeneous symptom groups, and the multiple, dynamically interacting variables that influence our psychology. Secondly, we put forward a causal-orientated framework in the context of two large-scale datasets arising from the Adolescent Brain Cognitive Development (ABCD) study, the largest long-term study of brain development and child health in the United States, and the Global Mind Project which is the largest database in the world of mental health profiles along with life context information from 1.4 million people across the globe. Finally, we describe how analytical and machine learning approaches such as clustering and causal inference can be used on datasets such as these to help elucidate a more causal understanding of mental health conditions to enable diagnostic approaches and preventative solutions that tackle mental health challenges at their root cause.
在过去30年里，为了增进我们对心理健康状况的理解和治疗，已经开展了众多大规模的纵向精神病学研究工作。然而，尽管研究界付出了巨大努力并投入了大量资金，但我们对大多数心理健康障碍仍缺乏因果关系的理解。因此，大多数精神病学诊断和治疗仍停留在症状体验层面，而不是测量或解决根本原因。这导致了一种试错方法，这种方法与潜在因果关系不太匹配，临床效果不佳。在此，我们讨论一种源于对因果因素而非症状分组进行探索的研究框架，将其应用于大规模多维数据，如何有助于应对心理健康研究当前面临的一些挑战，进而改善临床结果。首先，我们描述了寻找心理健康状况因果驱动因素所面临的一些挑战和复杂性，重点关注当前精神病障碍的评估和诊断方法、症状与原因之间的多对多映射、寻找异质症状组的生物标志物，以及影响我们心理的多个动态相互作用的变量。其次，我们在美国最大的脑发育与儿童健康长期研究——青少年大脑认知发展（ABCD）研究以及全球最大的心理健康档案数据库及来自全球140万人的生活背景信息的全球心灵项目所产生的两个大规模数据集的背景下，提出了一个以因果关系为导向的框架。最后，我们描述了如何在这样的数据集上使用聚类和因果推断等分析和机器学习方法，以帮助阐明对心理健康状况更具因果关系的理解，从而实现从根本原因解决心理健康挑战的诊断方法和预防方案。
