# Regions of Interest as nodes of dynamic functional brain networks

arXiv: https://arxiv.org/abs/1710.04056 · HTML: https://mlsanity.com/paper/1710.04056/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2017.

## Built on by (most cited first, 1 of 1)

- [Multi-Modal Graph Neural Network with Transformer-Guided Adaptive Diffusion for Preclinical Alzheimer Classification](https://mlsanity.com/paper/2606.03322/) (2026)

## Abstract (arXiv)

The properties of functional brain networks strongly depend on how their nodes are chosen. Commonly, nodes are defined by Regions of Interest (ROIs), pre-determined groupings of fMRI measurement voxels. Earlier, we have 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 a ROI. We find that spatial consistency varies non-uniformly in space and time, which is reflected in the variation of spatiotemporal consistency across ROIs. Further, 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.