Controllability of Brain Networks
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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.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Jun 2014.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 2; 2025: 4; 2026: 2.
12 papers.
- 2023The Topology and Geometry of Neural Representationscited by 14
- 2026Explore Brain-Inspired Machine Intelligence for Connecting Dots on Graphs Through Holographic Blueprint of Oscillatory Synchronizationcited by 3
- 2025Characterizing control between interacting subsystems with deep Jacobian estimationcited by 2
- 2024Causality-based Subject and Task Fingerprints using fMRI Time-series Datacited by 1
- 2021GINA: Neural Relational Inference From Independent Snapshotscited by 0
- 2022Controllability of Coarsely Measured Networked Linear Dynamical Systems (Extended Version)cited by 0
- 2022Uncovering the Origins of Instability in Dynamical Systems: How Attention Mechanism Can Help?cited by 0
- 2024Joint Learning of Linear Dynamical Systems under Smoothness Constraintscited by 0
- 2025Learning Backbones: Sparsifying Graphs through Zero Forcing for Effective Graph-Based Learningcited by 0
- 2025Sample-Efficient Diffusion-based Control of Complex Physics Systemscited by 0
- 2025Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learningcited by 0
- 2026Trajectory-Aware Node Contributions and the Limits of Static Controllabilitycited by 0
- 2026Trajectory-Aware Node Contributions and the Limits of Static Controllability2 Jun 2026
- 2026Explore Brain-Inspired Machine Intelligence for Connecting Dots on Graphs Through Holographic Blueprint of Oscillatory Synchronization20 Jan 2026
- 2025Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning21 Jul 2025
- 2025Characterizing control between interacting subsystems with deep Jacobian estimation2 Jul 2025
- 2025Sample-Efficient Diffusion-based Control of Complex Physics Systems25 Feb 2025
- 2025Learning Backbones: Sparsifying Graphs through Zero Forcing for Effective Graph-Based Learning24 Feb 2025
- 2024Causality-based Subject and Task Fingerprints using fMRI Time-series Data26 Sep 2024
- 2024Joint Learning of Linear Dynamical Systems under Smoothness Constraints3 Jun 2024
- 2023The Topology and Geometry of Neural Representations20 Sep 2023
- 2022Uncovering the Origins of Instability in Dynamical Systems: How Attention Mechanism Can Help?19 Dec 2022
- 2022Controllability of Coarsely Measured Networked Linear Dynamical Systems (Extended Version)21 Jun 2022
- 2021GINA: Neural Relational Inference From Independent Snapshots29 May 2021