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).
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.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
TREATMENT
Masking
NONE
Enrollment
12
Control condition, constant high-frequency DBS stimulation (130Hz)
Experimental condition, constant low-frequency DBS stimulation (60 Hz)
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.
Cleveland Clinic Foundation
Cleveland, Ohio, United States
Stride Time Coefficient of Variation
Marker of gait instability, derived from kinematic recordings from body-worn wireless sensors.
Time frame: During the intervention
Percentage of Time with Tremor Present
Marker of tremor severity, derived from kinematic recordings from body-worn wireless sensors.
Time frame: During the intervention
Tremor Amplitude
Marker of tremor severity, derived from kinematic recordings from body-worn wireless sensors.
Time frame: During the intervention
Total Freezing Time
Marker of gait instability, derived from kinematic recordings from body-worn wireless sensors.
Time frame: During the intervention
Freezing Index
Marker of gait instability, derived from kinematic recordings from body-worn wireless sensors.
Time frame: During the intervention
Gait Velocity
Marker of bradykinesia, derived from kinematic recordings from body-worn wireless sensors.
Time frame: During the intervention
Step Cadence
Marker of bradykinesia, derived from kinematic recordings from body-worn wireless sensors.
Time frame: During the intervention
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.
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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.
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.
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.
All six device interventions will be performed in medication OFF state
All six device interventions will be performed in medication ON state
Time frame: During the intervention
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.
Time frame: During the intervention