Mapping the voxel-wise effective connectome in resting state FMRI

PLoS One. 2013 Sep 12;8(9):e73670. doi: 10.1371/journal.pone.0073670. eCollection 2013.


A network approach to brain and dynamics opens new perspectives towards understanding of its function. The functional connectivity from functional MRI recordings in humans is widely explored at large scale, and recently also at the voxel level. The networks of dynamical directed connections are far less investigated, in particular at the voxel level. To reconstruct full brain effective connectivity network and study its topological organization, we present a novel approach to multivariate Granger causality which integrates information theory and the architecture of the dynamical network to efficiently select a limited number of variables. The proposed method aggregates conditional information sets according to community organization, allowing to perform Granger causality analysis avoiding redundancy and overfitting even for high-dimensional and short datasets, such as time series from individual voxels in fMRI. We for the first time depicted the voxel-wise hubs of incoming and outgoing information, called Granger causality density (GCD), as a complement to previous repertoire of functional and anatomical connectomes. Analogies with these networks have been presented in most part of default mode network; while differences suggested differences in the specific measure of centrality. Our findings could open the way to a new description of global organization and information influence of brain function. With this approach is thus feasible to study the architecture of directed networks at the voxel level and individuating hubs by investigation of degree, betweenness and clustering coefficient.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Connectome / methods*
  • Humans
  • Magnetic Resonance Imaging / methods*

Grants and funding

This work was supported by the China Scolarship Council (grant number 2011607033 for G-RW), the Natural Science Foundation of China (grant number 81201155 for WL), and the Belgian Science Policy (IUAP VII project CEREBNET P7 11 for DM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.