A1 Refereed original research article in a scientific journal
Regions of Interest as nodes of dynamic functional brain networks
Authors: Elisa Ryyppö, Enrico Glerean, Elvira Brattico, Jari Saramäki, Onerva Korhonen
Publisher: MIT PRESS
Publication year: 2018
Journal: Network Neuroscience
Journal name in source: NETWORK NEUROSCIENCE
Journal acronym: NETW NEUROSCI
Volume: 2
Issue: 4
First page : 513
Last page: 535
Number of pages: 23
ISSN: 2472-1751
eISSN: 2472-1751
DOI: https://doi.org/10.1162/netn_a_00047
Web address : https://www.mitpressjournals.org/doi/full/10.1162/netn_a_00047
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/37115563
The properties of functional brain networks strongly depend on how their nodes are chosen. Commonly, nodes are defined by Regions of Interest (ROIs), predetermined groupings of fMRI measurement voxels. Earlier, we demonstrated that the functional homogeneity of ROIs, captured by their spatial consistency, varies widely across ROIs in commonly used brain atlases. Here, we ask how ROIs behave as nodes of dynamic brain networks. To this end, we use two measures: spatiotemporal consistency measures changes in spatial consistency across time and network turnover quantifies the changes in the local network structure around an ROI. We find that spatial consistency varies non-uniformly in space and time, which is reflected in the variation of spatiotemporal consistency across ROIs. Furthermore, we see time-dependent changes in the network neighborhoods of the ROIs, reflected in high network turnover. Network turnover is nonuniformly distributed across ROIs: ROIs with high spatiotemporal consistency have low network turnover. Finally, we reveal that there is rich voxel-level correlation structure inside ROIs. Because the internal structure and the connectivity of ROIs vary in time, the common approach of using static node definitions may be surprisingly inaccurate. Therefore, network neuroscience would greatly benefit from node definition strategies tailored for dynamical networks.
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