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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT05022147
Other study ID # 21-739
Secondary ID
Status Recruiting
Phase Phase 1
First received
Last updated
Start date October 4, 2021
Est. completion date July 2024

Study information

Verified date July 2023
Source The Cleveland Clinic
Contact James Liao, MD PhD
Phone 216-445-1108
Email liaoj2@ccf.org
Is FDA regulated No
Health authority
Study type Interventional

Clinical Trial Summary

The purpose of this study is to assess how alternating-frequency Deep Brain Stimulation (DBS) works to improve postural instability and gait, while also treating other motor symptoms of Parkinson Disease (PD).


Description:

Postural instability, gait impairment, and falls are among the greatest unmet needs in Parkinson disease (PD). A single fall can be catastrophic, and impairments that limit mobility lead to social isolation or depression, and adversely affect bone and cardiovascular health. Unfortunately, postural instability and gait disorders are refractory to current pharmacological and surgical treatments, including deep brain stimulation (DBS). This project will directly address this pressing need. We will recruit participants to perform a gait task, using a new, alternating DBS frequency paradigm, while body movements and neural signals are recorded. The findings will lead to improved therapies to address these symptoms in the future.


Recruitment information / eligibility

Status Recruiting
Enrollment 12
Est. completion date July 2024
Est. primary completion date July 2024
Accepts healthy volunteers No
Gender All
Age group 21 Years and older
Eligibility Inclusion Criteria: - Bilateral STN DBS for PD - Medtronic Percept PC implanted DBS battery/pulse generator/recording system - Presence of balance and/or walking impairment and/or freezing of gait - Can walk without assistance, OFF meds, based on yes/no verbal response Exclusion Criteria: - Severity of gait impairment should not require dependency to walker or cane - Cannot tolerate monopolar stimulation at either of the two middle electrode contacts (prerequisite for the recording mode of the DBS) at high frequency (130 Hz) or low frequency (60 Hz). - Concomitant conditions that may affect significantly the evaluation of balance or gait, including orthopedic, rheumatologic or other neurological diseases - Contraindication to physical therapy - Age < 21 - Diagnosis of dementia - Not agreeable to having video taken of entire research visit

Study Design


Intervention

Device:
High-Frequency-Only Stimulation
Control condition, constant high-frequency DBS stimulation (130Hz)
Low-Frequency-Only Stimulation
Experimental condition, constant low-frequency DBS stimulation (60 Hz)
Alternating, 50 sec High-Frequency, 10 sec Low-Frequency Stimulation
Experimental condition where stimulation frequency is changed from high (130Hz) to low (60Hz) frequency. The time interval for each frequency is 50 seconds for high, and 10 seconds for low, respectively.
Alternating, 50 sec High-Frequency, 50 sec Low-Frequency Stimulation
Experimental condition where stimulation frequency is changed from high (130Hz) to low (60Hz) frequency. The time interval for each frequency is 50 seconds for high, and 50 seconds for low, respectively.
Alternating, 10 sec High-Frequency, 50 sec Low-Frequency Stimulation
Experimental condition where stimulation frequency is changed from high (130Hz) to low (60Hz) frequency. The time interval for each frequency is 10 seconds for high, and 50 seconds for low, respectively.
Alternating, 10 sec High-Frequency, 10 sec Low-Frequency Stimulation
Experimental condition where stimulation frequency is changed from high (130Hz) to low (60Hz) frequency. The time interval for each frequency is 10 seconds for high, and 10 seconds for low, respectively.
Drug:
OFF Dopaminergic Medication
All six device interventions will be performed in medication OFF state
ON Dopaminergic Medication
All six device interventions will be performed in medication ON state

Locations

Country Name City State
United States Cleveland Clinic Foundation Cleveland Ohio

Sponsors (1)

Lead Sponsor Collaborator
James Liao

Country where clinical trial is conducted

United States, 

Outcome

Type Measure Description Time frame Safety issue
Primary Stride Time Coefficient of Variation Marker of gait instability, derived from kinematic recordings from body-worn wireless sensors. During the intervention
Primary Percentage of Time with Tremor Present Marker of tremor severity, derived from kinematic recordings from body-worn wireless sensors. During the intervention
Primary Tremor Amplitude Marker of tremor severity, derived from kinematic recordings from body-worn wireless sensors. During the intervention
Secondary Total Freezing Time Marker of gait instability, derived from kinematic recordings from body-worn wireless sensors. During the intervention
Secondary Freezing Index Marker of gait instability, derived from kinematic recordings from body-worn wireless sensors. During the intervention
Secondary Gait Velocity Marker of bradykinesia, derived from kinematic recordings from body-worn wireless sensors. During the intervention
Secondary Step Cadence Marker of bradykinesia, derived from kinematic recordings from body-worn wireless sensors. During the intervention
Secondary LFP and EEG power spectrum correlation with behavior and kinematics Neural recordings (LFP = Local Field Potential and EEG = Electroencephalogram) from the DBS electrode and from EEG electrodes will be analyzed in the frequency domain. Assessed frequency bands will include delta, theta, alpha, beta, and gamma activity. These will be correlated with behavior and kinematic recordings to determine the neural correlates of gait instability and other parkinsonian symptoms. During the intervention
Secondary LFP and EEG connectivity correlation with behavior and kinematics Neural recordings (LFP = Local Field Potential and EEG = Electroencephalogram) consist of multiple channels of simultaneously measured electrical activity. Connectivity is a measure of correlations between each pair of recorded and postprocessed channels, at each frequency band, over time. A machine learning algorithm will be trained to correlate the connectivity to behavior and kinematic recordings, to determine the neural correlates of gait instability and other parkinsonian symptoms. During the intervention
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