Functional control of electrophysiological network architecture using direct neurostimulation in humans. Khambhati, A. N., Kahn, A. E., Costantini, J., Ezzyat, Y., Solomon, E. A., Gross, R. E., Jobst, B. C., Sheth, S. A., Zaghloul, K. A., Worrell, G., Seger, S., Lega, B. C., Weiss, S., Sperling, M. R., Gorniak, R., Das, S. R., Stein, J. M., Rizzuto, D. S., Kahana, M. J., Lucas, T. H., Davis, K. A., Tracy, J. I., & Bassett, D. S. Network Neuroscience, 3(3):848–877, January, 2019. ZSCC: NoCitationData[s0] Publisher: MIT Press![Functional control of electrophysiological network architecture using direct neurostimulation in humans [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Chronically implantable neurostimulation devices are becoming a clinically viable option for treating patients with neurological disease and psychiatric disorders. Neurostimulation offers the ability to probe and manipulate distributed networks of interacting brain areas in dysfunctional circuits. Here, we use tools from network control theory to examine the dynamic reconfiguration of functionally interacting neuronal ensembles during targeted neurostimulation of cortical and subcortical brain structures. By integrating multimodal intracranial recordings and diffusion-weighted imaging from patients with drug-resistant epilepsy, we test hypothesized structural and functional rules that predict altered patterns of synchronized local field potentials. We demonstrate the ability to predictably reconfigure functional interactions depending on stimulation strength and location. Stimulation of areas with structurally weak connections largely modulates the functional hubness of downstream areas and concurrently propels the brain towards more difficult-to-reach dynamical states. By using focal perturbations to bridge large-scale structure, function, and markers of behavior, our findings suggest that stimulation may be tuned to influence different scales of network interactions driving cognition.
@article{khambhati_functional_2019,
title = {Functional control of electrophysiological network architecture using direct neurostimulation in humans},
volume = {3},
url = {https://doi.org/10.1162/netn_a_00089},
doi = {10.1162/netn_a_00089},
abstract = {Chronically implantable neurostimulation devices are becoming a clinically viable option for treating patients with neurological disease and psychiatric disorders. Neurostimulation offers the ability to probe and manipulate distributed networks of interacting brain areas in dysfunctional circuits. Here, we use tools from network control theory to examine the dynamic reconfiguration of functionally interacting neuronal ensembles during targeted neurostimulation of cortical and subcortical brain structures. By integrating multimodal intracranial recordings and diffusion-weighted imaging from patients with drug-resistant epilepsy, we test hypothesized structural and functional rules that predict altered patterns of synchronized local field potentials. We demonstrate the ability to predictably reconfigure functional interactions depending on stimulation strength and location. Stimulation of areas with structurally weak connections largely modulates the functional hubness of downstream areas and concurrently propels the brain towards more difficult-to-reach dynamical states. By using focal perturbations to bridge large-scale structure, function, and markers of behavior, our findings suggest that stimulation may be tuned to influence different scales of network interactions driving cognition.},
number = {3},
urldate = {2020-10-06},
journal = {Network Neuroscience},
author = {Khambhati, Ankit N. and Kahn, Ari E. and Costantini, Julia and Ezzyat, Youssef and Solomon, Ethan A. and Gross, Robert E. and Jobst, Barbara C. and Sheth, Sameer A. and Zaghloul, Kareem A. and Worrell, Gregory and Seger, Sarah and Lega, Bradley C. and Weiss, Shennan and Sperling, Michael R. and Gorniak, Richard and Das, Sandhitsu R. and Stein, Joel M. and Rizzuto, Daniel S. and Kahana, Michael J. and Lucas, Timothy H. and Davis, Kathryn A. and Tracy, Joseph I. and Bassett, Danielle S.},
month = jan,
year = {2019},
note = {ZSCC: NoCitationData[s0]
Publisher: MIT Press},
pages = {848--877},
}
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