# Identifying Circuit Dynamics Underlying Motor Dysfunction in Parkinson’s Disease Using Real-Time Neural Control

> **NIH NIH R01** · CLEVELAND CLINIC LERNER COM-CWRU · 2024 · $591,277

## Abstract

PROJECT SUMMARY
While much research has been dedicated to understanding the pathophysiology of Parkinson’s disease (PD),
the neural dynamics underlying the manifestation of motor signs remain unclear. Studies over the past two
decades have shown a correlation between the amplitude and incidence of beta band oscillations in the
subthalamic nucleus (STN) and changes in bradykinesia and rigidity mediated by levodopa or deep brain
stimulation (DBS) therapies. Yet, no study has conclusively or deductively demonstrated a causal link. A
limitation to establishing causality is the lack of available neuromodulation tools capable of predictably and
precisely controlling neural oscillatory activity in the human brain in real-time without introducing confounding
factors. Establishing these tools and clarifying whether the relationship of beta band oscillations with PD motor
signs is causal or epiphenomenon are critical steps to better understand PD pathophysiology and advance
personalized DBS technology in PD and other conditions. This project aims to address these technology and
knowledge gaps by leveraging feedback control engineering and patient-specific computational modeling tools.
We will employ a new neural control approach developed in our group (evoked interference closed-loop DBS,
eiDBS) to characterize the degree by which controlled suppression or amplification of beta oscillations in the
STN influences bradykinesia and rigidity in PD (Specific Aim 1, SA1). In SA2, we will employ levodopa medication
to characterize how changes in bradykinesia and rigidity relate to variations in the amplitude, natural frequency,
and resonance of neural responses in the STN and primary motor cortex (MC) evoked by STN stimulation. The
results from SA2 will help us gain a greater understanding of intrinsic circuit dynamics associated with PD and
identify strategies to optimize closed-loop DBS algorithms (e.g., eiDBS) in the face of concurrent levodopa
therapy, a necessary step to bring this technology to clinical trials. Combining electrophysiological data with high-
resolution (7T) magnetic resonance (MR) imaging and computational modeling, we will identify which specific
neuronal pathways connected with the STN need to be activated to evoke frequency-specific neural responses
in the STN and MC (SA3). The data from SA3 will shed light on which sub-circuits are involved in the generation
of stimulation-evoked and spontaneous beta oscillations in PD, and inform how we can use directional DBS
leads to shape electric fields in the STN to selectively modulate the STN or MC via eiDBS. We will address the
three aims of this project with 25 PD patients implanted with DBS leads in the STN, whose DBS lead extensions
will be externalized and connected to our recording and closed-loop stimulation infrastructure. This project is
well aligned with the NINDS Parkinson’s Disease 2014 Research Recommendations, as we “use a combination
of sensor technologies and imaging to develop a ...

## Key facts

- **NIH application ID:** 10879102
- **Project number:** 5R01NS129600-02
- **Recipient organization:** CLEVELAND CLINIC LERNER COM-CWRU
- **Principal Investigator:** David Escobar Sanabria
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $591,277
- **Award type:** 5
- **Project period:** 2023-07-01 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10879102

## Citation

> US National Institutes of Health, RePORTER application 10879102, Identifying Circuit Dynamics Underlying Motor Dysfunction in Parkinson’s Disease Using Real-Time Neural Control (5R01NS129600-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10879102. Licensed CC0.

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