Application of Granger causality analysis to effective connectivity of the default-mode network

• Published in: 2010 IEEE/ICME International Conference on Complex Medical Engineering pp. 156 - 160
• Main Authors: Xiaoyan Miao, Kewei Chen, Rui Li, Xiaotong Wen, Li Yao, Xia Wu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2010
• Subjects: Algorithm design and analysis; Brain models; Laboratories; Large Hadron Collider; Magnetic analysis; Magnetic resonance imaging
• DOI: 10.1109/ICCME.2010.5558851
• ISBN: 1424468418; 9781424468416

The default-mode network (DMN), which is suggested to have important functions related to internal modes of cognition and increasingly implicated in brain disorders, has attracted much attention in the past few years. Effective connectivity, defined as the influence one neuronal system exerts over another, can provide deep understanding of directed influence between brain regions in the network from the view of functional integration. Granger causality analysis is one of the conventional approaches to explore the effective connectivity in brain imaging researches. In this study, we applied Granger causality analysis to resting-state functional Magnetic Resonance Imaging (fMRI) data from 12 young subjects to explore the effective connectivity pattern of the DMN. The results demonstrated that posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC) and inferior parietal cortex (IPC) were the only three regions had significant causal relationship with all other regions in more than 50% subjects and PCC was the only brain area influenced by all others while had no directed influence to others. The strong effective connectivity pattern demonstrated that PCC, MPFC and IPC were the three key regions and PCC was the convergence hub in the network. These results provide further understanding of physiological mechanism of DMN underlying internal modes of cognition.