Macroscopic brain networks have been widely described with the manifold of metrics available using graph theory. However, most analyses do not incorporate information about the physical position of network nodes. Here, we provide a multimodal macroscopic network characterization while considering the physical positions of nodes. To do so, we examined anatomical and functional macroscopic brain networks in a sample of twenty healthy subjects. Anatomical networks are obtained with a graph based tractography algorithm from diffusion-weighted magnetic resonance images (DW-MRI). Anatomical connections identified via DW-MRI provided probabilistic constraints for determining the connectedness of 90 different brain areas. Functional networks are derived from temporal linear correlations between blood-oxygenation level-dependent signals derived from the same brain areas. Rentian Scaling analysis, a technique adapted from very-large-scale integration circuits analyses, shows that functional networks are more random and less optimized than the anatomical networks. We also provide a new metric that allows quantifying the global connectivity arrangements for both structural and functional networks. While the functional networks show a higher contribution of inter-hemispheric connections, the anatomical networks highest connections are identified in a dorsal–ventral arrangement. These results indicate that anatomical and functional networks present different connectivity organizations that can only be identified when the physical locations of the nodes are included in the analysis.
|Number of pages||10|
|Publication status||Published - Mar 2014|
Bibliographical noteFunding Information:
The authors are grateful to the research participants for their participation in this study. We also thank Yasser Iturria-Medina for providing the tractography scripts. We also thank Frank G. Hillary and Nazareth P. Castellanos for fruitful comments on the manuscript. José Á. Pineda-Pardo was supported by the Spanish Ministry of Education through the National Program FPU (grant number AP2010-1317).
© 2014, Springer Science+Business Media New York.