Stephan Klaas E, Penny Will D, Marshall John C, Fink Gereon R, Friston Karl J
Wellcome Department of Imaging Neuroscience, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, United Kingdom.
Ann N Y Acad Sci. 2005 Dec;1064:16-36. doi: 10.1196/annals.1340.008.
The anatomy of the corpus callosum has been described in considerable detail. Tracing studies in animals and human postmortem experiments are currently complemented by diffusion-weighted imaging, which enables noninvasive investigations of callosal connectivity to be conducted. In contrast to the wealth of anatomical data, little is known about the principles by which interhemispheric integration is mediated by callosal connections. Most importantly, we lack insights into the mechanisms that determine the functional role of callosal connections in a context-dependent fashion. These mechanisms can now be disclosed by models of effective connectivity that explain neuroimaging data from paradigms that manipulate interhemispheric interactions. In this article, we demonstrate that dynamic causal modeling (DCM), in conjunction with Bayesian model selection (BMS), is a powerful approach to disentangling the various factors that determine the functional role of callosal connections. We first review the theoretical foundations of DCM and BMS before demonstrating the application of these techniques to empirical data from a single subject.
胼胝体的解剖结构已得到相当详细的描述。目前，动物追踪研究和人类尸检实验通过扩散加权成像得到补充，扩散加权成像能够进行胼胝体连接性的无创研究。与丰富的解剖学数据形成对比的是，对于胼胝体连接介导半球间整合的原理知之甚少。最重要的是，我们缺乏对以依赖于上下文的方式决定胼胝体连接功能作用的机制的深入了解。现在，通过有效连接模型可以揭示这些机制，这些模型可以解释来自操纵半球间相互作用范式的神经成像数据。在本文中，我们证明动态因果模型（DCM）与贝叶斯模型选择（BMS）相结合，是一种强大的方法，可用于理清决定胼胝体连接功能作用的各种因素。在将这些技术应用于来自单个受试者的经验数据之前，我们首先回顾DCM和BMS的理论基础。
