O. Korhonen – How consistent are ROIs as nodes of functional brain networks?

Onerva Korhonen (Aalto University)

Monday 2016-05-02 11.00 – 12.00

Seminar room 1021-1022, TUAS building

How consistent are ROIs as nodes of functional brain networks?

The functional network approach, where fMRI BOLD time series are mapped to complex networks depicting functional relationships between brain areas, has opened new insights into the structure and function of the human brain. In this approach, choosing what the network nodes represent is of crucial importance. One option is to consider individual fMRI voxels as nodes. This results in a large number of nodes and may make network analysis challenging, not only in terms of computational cost but also in terms of interpretation of results. A common alternative is to build the nodes of functional brain networks from pre-defined clusters of anatomically close voxels, known as Regions of Interest (ROIs). This approach assumes that voxels within ROIs are functionally similar and have reasonably similar dynamics, which may not always be true. Because these two approaches are known to result in dissimilar network structures, we argue that it is crucial to understand what happens to network connectivity when moving from the voxel level to the ROI level. We study the consistency of ROIs, defined as the mean Pearson correlation coefficient between the time series of voxels within a ROI, and show that this consistency varies widely in resting-state experimental data. This indicates that the underlying assumption of similar voxel dynamics within each ROI may not generally hold. We then show that this variation is reflected in network properties: consistency correlates with node connectivity and link weight in ROI-level networks.