Open AccessGranger causal influence predicts BOLD activity levels in the default mode network
Author(s) -
Jiao Qing,
Lu Guangming,
Zhang Zhiqiang,
Zhong Yuan,
Wang Zhengge,
Guo Yongxin,
Li Kai,
Ding Mingzhou,
Liu Yijun
Publication year - 2011
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.21065
Subject(s) - default mode network , precuneus , posterior cingulate , neuroscience , granger causality , prefrontal cortex , task positive network , psychology , functional magnetic resonance imaging , premovement neuronal activity , brain activity and meditation , superior frontal gyrus , electroencephalography , cognition , computer science , machine learning
Although the brain areas in the default‐mode network (DMN) act in a coordinated way during rest, the activity levels in the individual areas of the DMN are highly heterogeneous. The relation between the activity levels and the pattern of causal interaction among the DMN areas remains unknown. In the present fMRI study, seven nodes of the DMN were identified and their activity levels were rank‐ordered based on a power spectral analysis of the resting blood oxygenation level‐dependent (BOLD) signals. Furthermore, the direction of information flow among these DMN nodes was determined using Granger causality analysis and graph‐theoretic methods. We found that the activity levels in these seven DMN nodes had a highly consistent hierarchical distribution, with the highest activity level in the posterior cingulate/precuneus cortices, followed by ventral medial prefrontal cortex and dorsal medial prefrontal cortex, and with the lowest level in the left inferior temporal gyrus. Importantly, a significant correlation was found between the activity levels and the In‐Out degrees of information flow across the DMN nodes, suggesting that Granger causal influences can be used to predict BOLD activity levels. These findings shed light on the dynamical organization of cortical neuronal networks and may provide the basis for characterizing network disruption by brain disorders. Hum Brain Mapp, 2010. © 2010 Wiley‐Liss, Inc.