# The control of brain network dynamics across diverse scales of space and time

## TL;DR

This study investigates how different brain regions control neural dynamics across various spatial and temporal scales, revealing that regions controlling easy transitions operate on short scales and slow times, while those controlling difficult transitions operate on long scales and fast times.

## Contribution

It provides a novel analysis linking brain control profiles with spatial and temporal dynamics using diffusion tensor imaging and Laplacian eigenspectrum.

## Key findings

- Control of easy transitions linked to short spatial and slow temporal scales.
- Control of difficult transitions linked to long spatial and fast temporal scales.
- Linear models explain activity propagation and control profiles across brain regions.

## Abstract

The human brain is composed of distinct regions that are each associated with particular functions and distinct propensities for the control of neural dynamics. However, the relation between these functions and control profiles is poorly understood, as is the variation in this relation across diverse scales of space and time. Here we probe the relation between control and dynamics in brain networks constructed from diffusion tensor imaging data in a large community based sample of young adults. Specifically, we probe the control properties of each brain region and investigate their relationship with dynamics across various spatial scales using the Laplacian eigenspectrum. In addition, through analysis of regional modal controllability and partitioning of modes, we determine whether the associated dynamics are fast or slow, as well as whether they are alternating or monotone. We find that brain regions that facilitate the control of energetically easy transitions are associated with activity on short length scales and slow time scales. Conversely, brain regions that facilitate control of difficult transitions are associated with activity on long length scales and fast time scales. Built on linear dynamical models, our results offer parsimonious explanations for the activity propagation and network control profiles supported by regions of differing neuroanatomical structure.