A short history of causal modeling of fMRI data

作者：Stephan Klaas Enno^*; Roebroeck Alard

来源：NeuroImage, 2012, 62(2): 856-863.

DOI：10.1016/j.neuroimage.2012.01.034

• Effective connectivity
• Dynamic causal modeling
• DCM
• Granger causality
• Granger causality mapping
• GCM
• Bayesian model selection
• BMS
• Model evidence
• Brain Connectivity Workshop

Twenty years ago, the discovery of the blood oxygen level dependent (BOLD) contrast and invention of functional magnetic resonance imaging (MRI) not only allowed for enhanced analyses of regional brain activity, but also laid the foundation for novel approaches to studying effective connectivity, which is essential for mechanistically interpretable accounts of neuronal systems. Dynamic causal modeling (DCM) and Granger causality (G-causality) modeling have since become the most frequently used techniques for inferring effective connectivity from fMRI data. In this paper, we provide a short historical overview of these approaches, describing milestones of their development from our subjective perspectives.

• 出版日期2012-8-15