In the last 2 decades, several neuroimaging studies investigated brain abnormalities associated with the early stages of psychosis in the hope that these could aid the prediction of onset and clinical outcome. Despite advancements in the field, neuroimaging has yet to deliver. This is in part explained by the use of univariate analytical techniques, small samples and lack of statistical power, lack of external validation of potential biomarkers, and lack of integration of nonimaging measures (eg, genetic, clinical, cognitive data). PSYSCAN is an international, longitudinal, multicenter study on the early stages of psychosis which uses machine learning techniques to analyze imaging, clinical, cognitive, and biological data with the aim of facilitating the prediction of psychosis onset and outcome. In this article, we provide an overview of the PSYSCAN protocol and we discuss benefits and methodological challenges of large multicenter studies that employ neuroimaging measures.

中文翻译：

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走向精神病精准医学：多模式多中心研究的好处和挑战 - PSYSCAN：将神经影像学发现从研究转化为临床实践。


在过去的 20 年中，多项神经影像学研究调查了与精神病早期阶段相关的大脑异常，希望这些可以帮助预测发病和临床结果。尽管该领域取得了进步，但神经影像学尚未取得进展。这在一定程度上是由于使用单变量分析技术、小样本和缺乏统计能力、缺乏潜在生物标志物的外部验证以及缺乏非影像测量（例如遗传、临床、认知数据）的整合造成的。 PSYSCAN 是一项针对精神病早期阶段的国际、纵向、多中心研究，它使用机器学习技术来分析影像、临床、认知和生物数据，旨在促进精神病发作和结果的预测。在本文中，我们概述了 PSYSCAN 协议，并讨论了采用神经影像测量的大型多中心研究的优点和方法学挑战。