# CRCNS: Deep Neural Network Approaches for Closed-Loop Deep Brain Stimulation

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $221,735

## Abstract

The discovery that aberrant synchronization of rhythmic neuronal activity recorded in PD
patients is suppressed by DBS has advanced the concept that measures associated with
pathological activity may be used as biomarkers to control the delivery of DBS therapy. Pilot
studies of aDBS in PD have reported promising clinical results from triggering DBS stimulation
when the signal recorded from the DBS electrode showed a high level of oscillatory power in the
beta frequency range (13 – 35 Hz). That approach, however, has important limitations. Most
importantly, beta power recorded from the DBS lead is suppressed by movement including PD
tremor, its detection is highly dependent on lead location and the recording montage needed to
record during stimulation is incompatible with directional current steering, a recent innovation
employing segmented stimulation contacts. The inherent complexity of the increased parameter
space through DBS innovations also overwhelms standard programming techniques. Finally,
use of additional biomarker signals (e.g., recorded from cortex) is likely to improve the ability to
adaptively control DBS for disorders marked by complex multidimensional symptomatologies
such as PD. The current proposal will establish methods for overcoming these limitations by
developing techniques for multi-feature classification from ECoG recordings, using advanced
machine learning algorithms. The proposed research builds upon the extensive and unique
experiences with multi-day, extra-operative recording from DBS leads in patients at Charité
Hospital and intraoperative ECoG and DBS recording from patients at the University of
Pittsburgh, in order to develop computational methods to advance closed-loop, adaptive DBS
(aDBS) strategies for movement disorders.

## Key facts

- **NIH application ID:** 10025184
- **Project number:** 5R01NS110424-03
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Robert Mark Richardson
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $221,735
- **Award type:** 5
- **Project period:** 2019-09-30 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10025184

## Citation

> US National Institutes of Health, RePORTER application 10025184, CRCNS: Deep Neural Network Approaches for Closed-Loop Deep Brain Stimulation (5R01NS110424-03). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10025184. Licensed CC0.

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