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Ramanathan DS, Guo L, Gulati T, Davidson G, Hishinuma AK, Won SJ, Knight RT, Chang EF, Swanson RA, Ganguly K. Low-frequency cortical activity is a neuromodulatory target that tracks recovery after stroke. Nat Med 2018; 24:1257-1267. [PMID: 29915259 PMCID: PMC6093781 DOI: 10.1038/s41591-018-0058-y] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Accepted: 04/25/2018] [Indexed: 12/24/2022]
Abstract
Recent work has highlighted the importance of transient low-frequency oscillatory (LFO; <4 Hz) activity in the healthy primary motor cortex during skilled upper-limb tasks. These brief bouts of oscillatory activity may establish the timing or sequencing of motor actions. Here, we show that LFOs track motor recovery post-stroke and can be a physiological target for neuromodulation. In rodents, we found that reach-related LFOs, as measured in both the local field potential and the related spiking activity, were diminished after stroke and that spontaneous recovery was closely correlated with their restoration in the perilesional cortex. Sensorimotor LFOs were also diminished in a human subject with chronic disability after stroke in contrast to two non-stroke subjects who demonstrated robust LFOs. Therapeutic delivery of electrical stimulation time-locked to the expected onset of LFOs was found to significantly improve skilled reaching in stroke animals. Together, our results suggest that restoration or modulation of cortical oscillatory dynamics is important for the recovery of upper-limb function and that they may serve as a novel target for clinical neuromodulation.
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Affiliation(s)
- Dhakshin S Ramanathan
- Neurology Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
- Mental Health Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA
- Mental Health Service, VA San Diego Health System, San Diego, San Diego, CA, USA
- Department of Psychiatry, University of California, San Diego, San Diego, CA, USA
| | - Ling Guo
- Neurology Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
- Neuroscience Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Tanuj Gulati
- Neurology Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Gray Davidson
- Neurology Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
- Mental Health Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA
| | - April K Hishinuma
- Neurology Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Seok-Joon Won
- Neurology Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Robert T Knight
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
| | - Edward F Chang
- Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, USA
| | - Raymond A Swanson
- Neurology Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Karunesh Ganguly
- Neurology Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA.
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
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Abstract
The stability and frequency content of local field potentials (LFPs) offer key advantages for long-term, low-power neural interfaces. However, interpreting LFPs may require new signal processing techniques which should be informed by a scientific understanding of how these recordings arise from the coordinated activity of underlying neuronal populations. We review current approaches to decoding LFPs for brain-machine interface (BMI) applications, and suggest several directions for future research. To facilitate an improved understanding of the relationship between LFPs and spike activity, we share a dataset of multielectrode recordings from monkey motor cortex, and describe two unsupervised analysis methods we have explored for extracting a low-dimensional feature space that is amenable to biomimetic decoding and biofeedback training.
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