Altering alpha-frequency brain oscillations with rapid analog feedback-driven neurostimulation
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journal.pone.0207781.pdf
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1.71 MB
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Author(s)
Boggess, Matthew
Rockhill, Alexander P.
Miller, Earl K.
Widge, Alik
Mullen, Andrew C.
Sheopory, Shivani
Loonis, Roman Florian
Freeman, Daniel
Miller, Earl K
Date Issued
December 2018
Journal
PLOS ONE
Publisher
Public Library of Science
Citation
Widge, Alik S., Matthew Boggess, Alexander P. Rockhill, Andrew Mullen, Shivani Sheopory, Roman Loonis, Daniel K. Freeman, and Earl K. Miller. “Altering Alpha-Frequency Brain Oscillations with Rapid Analog Feedback-Driven Neurostimulation.” Edited by Gennady Cymbalyuk. PLOS ONE 13, no. 12 (December 5, 2018): e0207781.
Version
Final published version
Abstract
Oscillations of the brain’s local field potential (LFP) may coordinate neural ensembles and brain networks. It has been difficult to causally test this model or to translate its implications into treatments, because there are few reliable ways to alter LFP oscillations. We developed a closed-loop analog circuit to enhance brain oscillations by feeding them back into cortex through phase-locked transcranial electrical stimulation. We tested the system in a rhesus macaque with chronically implanted electrode arrays, targeting 8–15 Hz (alpha) oscillations. Ten seconds of stimulation increased alpha oscillatory power for up to 1 second after stimulation offset. In contrast, open-loop stimulation decreased alpha power. There was no effect in the neighboring 15–30 Hz (beta) LFP rhythm or on a neighboring array that did not participate in closed-loop feedback. Analog closed-loop neurostimulation might thus be a useful strategy for altering brain oscillations, both for basic research and the treatment of neuropsychiatric disease.
MIT Department
Lincoln Laboratory
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Research Laboratory of Electronics
Picower Institute for Learning and Memory
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1371/journal.pone.0207781
