Controllability of structural brain networks

By Shi Gu, Fabio Pasqualetti, M. Cieslak, Qawi K. Telesford, Alfred B. Yu, Ari E. Kahn, John D. Medaglia, Jean M. Vettel, Michael B. Miller, Scott T. Grafton and 1 more. Nature Communications, 2014

860 citations recorded by the source corpus.

Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories 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 cognitive circuits dictate their distinct roles in controlling trajectories of brain network function. Cognitive control is fundamental to human intelligence, yet the principles constraining the neural dynamics of cognitive control remain elusive. Here, the authors use network control theory to demonstrate that the structure of brain networks dictates their functional role in controlling dynamics.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/96adae381fdd84b6df1ebac48b40087556071783

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Citations in the corpus

Small-World Brain Networks Revisited (2016, background): “Looking forward, critical open questions lie in how these weak connections drive global dynamics, and how one can intervene in a system to manipulate those processes [59–61].”
Closed-loop stimulation of temporal cortex rescues functional networks and improves memory (2018, positive): “Measures of network controllability based on connectivity could also be used to predict the extent to which a stimulation target can control functional dynamics in the memory network 47 .”
Activity flow over resting-state networks shapes cognitive task activations (2016, methodology): “However, like recent models of activity-spreading dynamics 25,26 , it is not meant to be a realistic simulation of neuronal dynamics but rather a tool for quantifying (and making inferences about) brain activity relationships.”
Cliques and cavities in the human connectome (2016, methodology): “Our results demonstrate that while cliques are observed in the structural core, cycles enclosing topological cavities are observed to link regions of subcortex, frontal cortex, and parietal cortex in long loops, underscoring their unique role in controlling brain function [23–25].”
Network neuroscience (2017, background): “Such a theory offers the fundamental backdrop against which to better understand cognitive control 113 , optimize stimulation for neurological disorders 114 , maintain and con-trol levels of anesthesia 115 , and inform surgical or stimulation-based interventions such as in the case of epilepsy 116…”
Network control principles predict neuron function in the Caenorhabditis elegans connectome (2017, background): “E u r op e P M C F und e r s A u t ho r M a nu s c r i p t s predict control energy and control time25,26 and ultimately tease apart involvement of network components when the input and output nodes are indistinguishable.”
Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity (2021, background): “While there has been growing interest in the general notion of controllability in neural networks [73], our current conception of this problem is at its infancy [74] and more work is needed to understand how control may impact embedding dimensionality.”
Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior (2019, unknown): “First, the model of dynamics that we employ is linear and noise free, unlike the brain [46], but has proven useful in gaining intuitions about the behavior of non-linear systems [55, 76].”