Structural degree predicts functional network connectivity: A multimodal resting-state fMRI and MEG study

P. Tewarie, A. Hillebrand, E. van Dellen, M.M. Schoonheim, F. Barkhof, C.H. Polman, C. Beaulieu, G. Gong, B.W. van Dijk, C.J. Stam
2014 NeuroImage  
a r t i c l e i n f o Communication between neuronal populations in the human brain is characterized by complex functional interactions across time and space. Recent studies have demonstrated that these functional interactions depend on the underlying structural connections at an aggregate level. Multiple imaging modalities can be used to investigate the relation between the structural connections between brain regions and their functional interactions at multiple timescales. We investigated if
more » ... We investigated if consistent modality-independent functional interactions take place between brain regions, and whether these can be accounted for by underlying structural properties. We used functional MRI (fMRI) and magnetoencephalography (MEG) recordings from a population of healthy adults together with a previously described structural network. A high overlap in resting-state functional networks was found in fMRI and especially alpha band MEG recordings. This overlap was characterized by a strongly interconnected functional core network in temporo-posterior brain regions. Anatomically realistically coupled neural mass models revealed that this strongly interconnected functional network emerges near the threshold for global synchronization. Most importantly, this functional core network could be explained by a trade-off between the product of the degrees of structurally-connected regions and the Euclidean distance between them. For both fMRI and MEG, the product of the degrees of connected regions was the most important predictor for functional network connectivity. Therefore, irrespective of the modality, these results indicate that a functional core network in the human brain is especially shaped by communication between high degree nodes of the structural network.
doi:10.1016/j.neuroimage.2014.04.038 pmid:24769185 fatcat:gfdyyeulkrhufmanfmtxasi4cm