Kahan Joshua, Foltynie Tom
Sobell Department for Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, London, UK.
Neuroimage. 2013 Dec;83:542-9. doi: 10.1016/j.neuroimage.2013.07.008. Epub 2013 Jul 10.
Despite almost a decade since the introduction of Dynamic Causal Modelling (DCM), there remains some confusion within the wider neuroimaging, neuroscience and clinical communities as to what DCM studies are probing, and what all the jargon means. We provide ten simple rules, and a theoretical example to gently introduce the reader to the rationale behind DCM analyses, and how one should consider neuroimaging data and experiments that use DCM. It is deliberately written as a primer or orientation for non-technical imaging neuroscientists or clinicians who have had to contend with the technical intricacies of understanding DCM.
尽管自动态因果模型(DCM)问世已近十年,但在更广泛的神经影像学、神经科学和临床领域,对于DCM研究探究的内容以及所有这些专业术语的含义仍存在一些困惑。我们提供十条简单规则以及一个理论示例,以温和地向读者介绍DCM分析背后的基本原理,以及人们应如何看待使用DCM的神经影像学数据和实验。本文特意写成一篇入门指南或指南,面向那些不得不应对理解DCM技术复杂性的非技术型影像神经科学家或临床医生。