# Optimizing oscillatory epidural electrical stimulation to selectively increase task-related population dynamics in motor areas

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2021 · $701,344

## Abstract

PROJECT SUMMARY
Stroke is the leading cause of motor disability in the United States. While brain stimulation to enhance motor
function after stroke has shown promise in small studies, two recent large stroke trials did not find evidence for
significant benefits. A key uncertainty is about how to exactly tailor brain stimulation to effectively modulate neural
dynamics associated with movement preparation and control. Our recent studies in rats (Ramanathan et al.,
Nature Medicine 2018; Lemke et al., Nature Neuroscience, 2019) demonstrated that population dynamics linked
to low-frequency oscillatory activity (0.5-4Hz “LFO”) are essential for movement control and can serve as a target
for modulation using electrical stimulation. More specifically, cortical stimulation was found to both boost LFO
power and augment motor function. We now also have substantial evidence in a non-human primate model
that such an approach can be effective in more complex brains. However, it is essential to further optimize the
delivery of such stimulation to specifically target cortical dynamics. We thus propose to optimize parameters for
epidural stimulation to selectively modulate population dynamics in the intact motor network. Our approach
entails simultaneous recording of single neurons in the non-human primate motor network along with electrical
stimulation using a customized “ring” of epidural cranial screw electrodes. Moreover, we will use computational
analysis to determine how task-related neural dynamics in a reach-to-grasp task are modulated by electrical
stimulation. More specifically, we will optimize and develop principles for large-scale electrical stimulation to
selectively enhance “neural modes” isolated to M1 or PMd or joint across both areas. This approach is built on
the growing consensus that motor networks perform computations through coordinated ensemble activity or
“neural modes”, i.e. patterns of neural covariation measured with dimensionality reduction methods. Activation
of neural modes (i.e. Neural Model Activation or NMA) appear to constitute building blocks for computations
underlying movement control. Our specific aims are: 1) Determine optimal ACS parameters that increases both
local and cross-area NMA between M1 and PMd during a reach-grasp task; 2) Determine optimal ACS
parameters that increases both local and cross-area NMA between M1 and S1 during a reach-grasp task; 3)
Determine parameters for ACS to enhance task NMA during time periods away from the task. Completion of
these aims will provide critical information for designing therapeutic stimulation that selectively targets population
dynamics in the distributed motor network. The information gained may also help improve methods for non-
invasive brain stimulation.

## Key facts

- **NIH application ID:** 10267682
- **Project number:** 5R01NS117406-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Karunesh Ganguly
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $701,344
- **Award type:** 5
- **Project period:** 2020-09-30 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10267682

## Citation

> US National Institutes of Health, RePORTER application 10267682, Optimizing oscillatory epidural electrical stimulation to selectively increase task-related population dynamics in motor areas (5R01NS117406-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10267682. Licensed CC0.

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