Colloquium: Control of dynamics in brain networks

@article{Tang2017ColloquiumCO,
  title={Colloquium: Control of dynamics in brain networks},
  author={Evelyn Tang and Danielle S. Bassett},
  journal={Reviews of Modern Physics},
  year={2017}
}
The ability to effectively control brain dynamics holds great promise for the enhancement of cognitive function in humans, and the betterment of their quality of life. Yet, successfully controlling dynamics in neural systems is challenging, in part due to the immense complexity of the brain and the large set of interactions that can drive any single change. While we have gained some understanding of the control of single neurons, the control of large-scale neural systems -- networks of multiply… 
View PDF on arXiv
122 Citations
Highly Influential Citations
5
Background Citations
38
Methods Citations
12

122 Citations

Linear Dynamics and Control of Brain Networks

This chapter focuses on the simple but powerful framework of linear systems theory as a useful tool both for capturing biophysically relevant parameters of neural activity and connectivity and for analytical and numerical study.

Linear Dynamics&Control of Brain Networks

This chapter focuses on the simple but powerful framework of linear systems theory as a useful tool both for capturing biophysically relevant parameters of neural activity and connectivity, and for analytical and numerical study.

Models of communication and control for brain networks: distinctions, convergence, and future outlook

This work seeks to explicitly bridge computational models of communication and principles of network control in a conceptual review of the current literature by drawing comparisons between communication and control models in terms of the level of abstraction, the dynamical complexity, the dependence on network attributes, and the interplay of multiple spatiotemporal scales.

Colloquium : Multiscale modeling of brain network organization

A perspective discussion on how recent results from multilayer network theory, involving generative modeling, controllability and machine learning, could be adopted to address new questions in modern physics and neuroscience.

A practical guide to methodological considerations in the controllability of structural brain networks

The current state-of-the-art network control theory is extended by developing an alternative measure of structural connectivity that accounts for radial propagation of activity through abutting tissue, and by defining a complementary metric quantifying the complexity of the energy landscape of a system.

The role of PFC networks in cognitive control and executive function

This review describes six key PFC networks involved in cognitive control and elucidate key principles relevant for understanding how these networks implement cognitive control, and describes major empirical and theoretical models that have emerged in recent years and how their functional architecture and dynamic organization supports flexible cognitive control.

Multiscale modeling of brain network organization

Recent advances for the characterization of the multiscale brain organization in terms of structure-function, oscillation frequencies and temporal evolution are presented and perspective discussion on how recent results from multilayer network theory, involving generative modeling, controllability and machine learning, could be adopted to address new questions in modern neuroscience is discussed.

Control of brain network dynamics across diverse scales of space and time.

This work probes the control properties of each brain region and investigates their relationship with dynamics across various spatial scales using the Laplacian eigenspectrum, and offers parsimonious explanations for the activity propagation and network control profiles supported by regions of differing neuroanatomical structure.

The physics of brain network structure, function and control

The organizing principles of brain network architecture instantiated in structural wiring under constraints of spatial embedding and energy minimization are described and surveyed, as well as models ofbrain network function that stipulate how neural activity propagates along structural connections.

The physics of brain network structure, function and control

Recent efforts to meet the challenge to understand how the brain’s structural wiring supports cognitive processes are reviewed, drawing on physics intuitions, models and theories spanning the domains of statistical mechanics, information theory, dynamical systems and control.
...

165 References

Controllability of structural brain networks

Network control theory is used to demonstrate that the structure of brain networks dictates their functional role in controlling dynamics, suggesting that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function.

Optimal trajectories of brain state transitions

Cognitive Control in the Controllable Connectome

Structural brain networks are constructed from diffusion tensor imaging data acquired in 125 healthy adult individuals to provide the first empirical evidence that network control forms a fundamental mechanism of cognitive control.

Developmental increases in white matter network controllability support a growing diversity of brain dynamics

A network representation of diffusion imaging data from 882 youth ages 8–22 is used to show that white matter connectivity becomes increasingly optimized for a diverse range of predicted dynamics in development, revealing a possible mechanism of human brain development that preferentially optimizes dynamic network control over static network architecture.

Multistability in large scale models of brain activity

A simplified neural mass model inspired by the continuous Hopfield network is developed, which is capable of reproducing some aspects of the brain's spontaneous activity (switching between different attractors) and exploring a large region of the parameters space.

Multistability in Large Scale Models of Brain Activity

Only the model with a global dynamic density control is found to display robust and long-lasting non-stationarity with no tendency toward either overactivity or extinction in noise-driven generalizations of the models.

Stimulation-Based Control of Dynamic Brain Networks

By mapping brain regions to cognitive systems, this work observes that the default mode system imparts large global change despite being highly constrained by structural connectivity.

Control Principles of Complex Networks

Recent advances on the controllability and the control of complex networks are reviewed, exploring the intricate interplay between a system's structure, captured by its network topology, and the dynamical laws that govern the interactions between the components.

On Structural Controllability of Symmetric (Brain) Networks

Evidence supporting controllability of brain networks from a single region as discussed in [1] is provided, thus contradicting the main conclusion and methods developed in [2].

On simplicity and complexity in the brave new world of large-scale neuroscience

...