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Building better biomarkers: brain models in translational neuroimaging

机译:建立更好的生物标志物:转化神经影像学中的大脑模型

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摘要

Despite its great promise, neuroimaging has yet to substantially impact clinical practice and public health. However, a developing synergy between emerging analysis techniques and data-sharing initiatives has the potential to transform the role of neuroimaging in clinical applications. We review the state of translational neuroimaging and outline an approach to developing brain signatures that can be shared, tested in multiple contexts and applied in clinical settings. The approach rests on three pillars: (i) the use of multivariate pattern-recognition techniques to develop brain signatures for clinical outcomes and relevant mental processes; (ii) assessment and optimization of their diagnostic value; and (iii) a program of broad exploration followed by increasingly rigorous assessment of generalizability across samples, research contexts and populations. Increasingly sophisticated models based on these principles will help to overcome some of the obstacles on the road from basic neuroscience to better health and will ultimately serve both basic and applied goals.
机译:尽管其前景广阔,但神经影像学尚未对临床实践和公共卫生产生重大影响。但是,新兴分析技术与数据共享计划之间不断发展的协同作用可能会改变神经成像在临床应用中的作用。我们审查了翻译神经影像学的状态,并概述了开发可以共享,在多种情况下进行测试并在临床环境中应用的脑签名的方法。该方法基于三个支柱:(i)使用多元模式识别技术来为临床结果和相关的心理过程开发大脑特征; (ii)评估和优化其诊断价值; (iii)进行广泛探索的计划,然后对样本,研究背景和人群的可概括性进行越来越严格的评估。基于这些原理的日益复杂的模型将有助于克服从基础神经科学到更好的健康发展道路上的一些障碍,并将最终为基本目标和应用目标服务。

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