# Decision-related information at single-neuron resolution in human motor cortex and its implications for neuroprosthetics

> **NIH NIH K99** · STANFORD UNIVERSITY · 2020 · $23,679

## Abstract

Project Summary / Abstract
The long-term goal of my research is to gain a holistic understanding of how movement commands are
generated- including their relationship to sensory feedback and movement context- in order to restore movement
to those who have lost it. When the ability to move is lost, due to spinal cord injury or disease, the ability to
generate movement commands is still intact, but the command cannot reach the end effectors. One means of
restoring function after such an injury is brain-computer interface (BCI) which records the movement commands
directly from the brain and bypasses the injury to move external end effectors. These neural prostheses rely on
accurate decoding of movement intention to perform the user’s desired action. While good control of these
devices has been demonstrated, the control is not as quick as movement of a native limb. This may be due to
oversimplification of how movement is decoded- in isolation from factors such as feedback or movement context.
Cognitive processes that may be occurring at the same time are not accounted for when determining the user’s
movement intention. The specific objectives of this proposal are to identify and characterize activity related to
decision making in human motor cortex, at the single-cell level, and how it relates to movement command
generation. Using participants enrolled in a clinical trial, we will record neural activity intracortically from motor
cortex during decision-making tasks to identify decision-related activity and examine its relationship to movement
intention. This activity has not been characterized in human cortex at this resolution, and the data collected in
the proposed work will allow us to compare to models of decision making generated from non-human primate
work and expand on the uniquely human ability to complete a variety of tasks in a single day. The proposed
experiments will produce a valuable data set that will enable me to (1) identify how neurons represent both
movement and decision-related activity, (2) capitalize on the decades of research on non-human primate
decision making to identify appropriate models for decision-related activity in human motor cortex, (3) generate
a more inclusive model by incorporating data collected during decision-related and movement generation tasks,
using multiple end effectors and (4) integrate decision-related activity into BCI decoders to improve our ability to
decode a BCI user’s movement intention and enhance the user’s control of the device (R00). This will provide
insight - supplementing knowledge from non-human primate studies - as to how the brain decides to move.

## Key facts

- **NIH application ID:** 9977403
- **Project number:** 1K99NS112412-01A1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Sharlene Flesher
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $23,679
- **Award type:** 1
- **Project period:** 2020-05-01 → 2020-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9977403

## Citation

> US National Institutes of Health, RePORTER application 9977403, Decision-related information at single-neuron resolution in human motor cortex and its implications for neuroprosthetics (1K99NS112412-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9977403. Licensed CC0.

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