Clinical trial NCT04277689: Deep Neural Network Approaches for Closed-Loop Deep Brain Stimulation
Official title: Deep Neural Network Approaches for Closed-Loop Deep Brain Stimulation
Study type: INTERVENTIONAL
Overall status: ENROLLING_BY_INVITATION
Phase: NA
Conditions: Parkinson Disease
Lead sponsor: Massachusetts General Hospital
Sponsor class: OTHER
First posted: 2020-02-20
Enrollment: 30
Sex: ALL
Minimum age (years): 18
Maximum age (years): 85
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: BASIC_SCIENCE

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.

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

Primary outcomes:
measure: The number of subjects providing interpretable electrophysiological data during DBS surgery
timeFrame: Duration of single DBS surgery
Secondary outcomes:
Other outcomes:
Locations:
city: Boston
country: United States
facility: Massachusetts General Hospital
state: Massachusetts
Study officials:
affiliation: Massachusetts General Hospital
name: Robert M Richardson, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References: