# Controllability of Brain Networks

arXiv: https://arxiv.org/abs/1406.5197 · HTML: https://mlsanity.com/paper/1406.5197/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2014.

## Built on by (most cited first, 12 of 12)

- [The Topology and Geometry of Neural Representations](https://mlsanity.com/paper/2309.11028/) (2023)
- [Explore Brain-Inspired Machine Intelligence for Connecting Dots on Graphs Through Holographic Blueprint of Oscillatory Synchronization](https://mlsanity.com/paper/2602.00057/) (2026)
- [Characterizing control between interacting subsystems with deep Jacobian estimation](https://mlsanity.com/paper/2507.01946/) (2025)
- [Causality-based Subject and Task Fingerprints using fMRI Time-series Data](https://mlsanity.com/paper/2409.18298/) (2024)
- [GINA: Neural Relational Inference From Independent Snapshots](https://mlsanity.com/paper/2105.14329/) (2021)
- [Controllability of Coarsely Measured Networked Linear Dynamical Systems (Extended Version)](https://mlsanity.com/paper/2206.10569/) (2022)
- [Uncovering the Origins of Instability in Dynamical Systems: How Attention Mechanism Can Help?](https://mlsanity.com/paper/2212.09641/) (2022)
- [Joint Learning of Linear Dynamical Systems under Smoothness Constraints](https://mlsanity.com/paper/2406.01094/) (2024)
- [Learning Backbones: Sparsifying Graphs through Zero Forcing for Effective Graph-Based Learning](https://mlsanity.com/paper/2502.17713/) (2025)
- [Sample-Efficient Diffusion-based Control of Complex Physics Systems](https://mlsanity.com/paper/2502.17893/) (2025)
- [Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning](https://mlsanity.com/paper/2507.15195/) (2025)
- [Trajectory-Aware Node Contributions and the Limits of Static Controllability](https://mlsanity.com/paper/2606.03067/) (2026)

## Abstract (arXiv)

Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behavior. Fundamental principles constraining these dynamic network processes have remained elusive. Here we use network control theory to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily-reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between the cognitive circuits dictate their distinct roles in controlling dynamic trajectories of brain network function.