Deep Neural Network Approaches for Closed-Loop Deep Brain Stimulation

NCT ID: NCT04277689

Last Updated: 2026-01-20

Basic Information

• Recruitment Status: ENROLLING_BY_INVITATION
• Clinical Phase: NA
• Total Enrollment: 30 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2021-06-10
• Study Completion Date: 2026-02-28

Brief Summary

In this research study the researchers want to learn more about brain activity related to speech perception and production in patients with Parkinson's Disease who are undergoing deep brain stimulation (DBS).

Detailed Description

Deep brain stimulation (DBS) is the gold-standard treatment for patients with medication resistant motor complications of Parkinson's disease (PD) and provides the only opportunity to record and stimulate in the human basal ganglia. Most recently, the concurrent use of research electrocorticography (ECoG) during DBS surgery, including pioneering work from Pittsburgh, has further enabled basic neuroscience investigation of human cortical-subcortical network dynamics. 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.

Conditions

Parkinson Disease

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: BASIC_SCIENCE
• Blinding Strategy: NONE

Study Groups

Brain signal data collection

Collection of brain data during deep brain stimulation

Group Type: EXPERIMENTAL

Brain signal data collection

Intervention Type: PROCEDURE

Collection of speech related electrophysiological data at the time of DBS.

Interventions

Brain signal data collection

Collection of speech related electrophysiological data at the time of DBS.

Intervention Type: PROCEDURE

Eligibility Criteria

Inclusion Criteria

1. Subjects scheduled for DBS implantation, as determined by the clinical multidisciplinary movement disorders board with definitive diagnosis of Parkinson's disease

2. Subjects able to provide informed consent and comply with task instructions.

3. Subjects 18-85 years old

Exclusion Criteria

1. Non-English-speaking subjects

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 85 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Massachusetts General Hospital

OTHER

Sponsor Role: lead

Responsible Party

Robert Mark Richardson

Director of Functional Neurosurgery at MGH

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Robert M Richardson, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

Massachusetts General Hospital

Locations

Massachusetts General Hospital

Boston, Massachusetts, United States

Countries

United States

Other Identifiers

2019P003874

Identifier Type: -

Identifier Source: org_study_id