# Consistency of Regions of Interest as nodes of functional brain networks measured by fMRI

## TL;DR

This paper investigates how the consistency of Regions of Interest (ROIs) in fMRI data affects the construction of functional brain networks, revealing variability that impacts network analysis and interpretation.

## Contribution

It demonstrates that ROI consistency varies widely and can lead to spurious correlations, highlighting the need for careful consideration when averaging signals over ROIs.

## Key findings

- ROI consistency varies widely in resting-state data
- Low-consistency ROIs can produce spurious correlations
- Averaging over ROIs should be done cautiously

## Abstract

The functional network approach, where fMRI BOLD time series are mapped to networks depicting functional relationships between brain areas, has opened new insights into the function of the human brain. In this approach, the choice of network nodes is of crucial importance. One option is to consider fMRI voxels as nodes. This results in a large number of nodes, making network analysis and interpretation of results challenging. A common alternative is to use pre-defined clusters of anatomically close voxels, Regions of Interest (ROIs). This approach assumes that voxels within ROIs are functionally similar. Because these two approaches result in different network structures, it is crucial to understand what happens to network connectivity when moving from the voxel level to the ROI level. We show that the consistency of ROIs, defined as the mean Pearson correlation coefficient between the time series of their voxels, varies widely in resting-state experimental data. Therefore the assumption of similar voxel dynamics within each ROI does not generally hold. Further, the time series of low-consistency ROIs may be highly correlated, resulting in spurious links in ROI-level networks. Based on these results, we recommend that averaging BOLD signals over anatomically defined ROIs should be carefully considered.