Stanford Movement Disorders
Palo Alto, California, 94304, United States
NCT Number: NCT03180515
Continuous deep brain stimulation (cDBS) is an established therapy for the major motor signs in Parkinson's disease, however some patients find that it does not adequately treat their freezing of gait (FOG). Currently, cDBS is limited to "open-loop" stimulation,without real-time adjustment to the patient's state of activity, fluctuations and types of motor symptoms, medication dosages, or neural markers of the disease. The purpose of this study is to determine if an adaptive DBS system,responding to patient specific, clinically relevant neural or kinematic feedback related to FOG, is more effective than continuous DBS on the motor Unified Parkinson's Disease Rating Scale (UPDRS III) and gait measures of PD.
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Notify Me18 year–80 year
All sexes
Interventional
Not applicable
Palo Alto, California, 94304, United States
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Activa PC+S Neurostimulator is approved for both aDBS and cDBS paradigms.
Other names: adaptive deep brain stimulation (aDBS), continuous deep brain stimulation (cDBS)
Time frame: 30 min - 2 hours
Safety, tolerability and feasibility of aDBS
Time frame: 30 minutes
Subthalamic nucleus (STN) local field potentials (LFP) recordings demonstrate oscillatory neuronal activity in both the alpha (8-12 Hz) and beta (13-30 Hz) bands in the resting state in PD. Spectrograms were generated using a short-time Fourier transform, with a 1 second Hanning window and a 0.5 second overlap, creating a frequency resolution of 1 Hz. Power spectral densities were calculated using the Welch method with the same window and overlap parameters. Power was summed in the beta and alpha bands. This power can be representative of the magnitude of oscillatory activity in this frequency band occurring in this brain region.
Time frame: 30 minutes
Subthalamic nucleus (STN) local field potentials (LFP) recordings demonstrate oscillatory neuronal activity in both the alpha (8-12 Hz) and beta (13-30 Hz) bands in the resting state in PD. Spectrograms were generated using a short-time Fourier transform, with a 1 second Hanning window and a 0.5 second overlap, creating a frequency resolution of 1 Hz. Power spectral densities were calculated using the Welch method with the same window and overlap parameters. Power was summed in the beta and alpha bands. This power can be representative of the magnitude of oscillatory activity in this frequency band occurring in this brain region.
Time frame: 30 minutes
The predictability of the local field potentials (band-pass filtered between 8-12 Hz for alpha) was analyzed using Sample Entropy (SampEn), a nonlinear measure suitable for physiological time series. SampEn may be a more consistent measure and more suitable to shorter time series data than approximate entropy, partially due to the elimination of counting self matches. SampEn is calculated as the negative logarithm of the estimated conditional probability that if consecutive subseries of length m are similar according to some preset tolerance r, the consecutive subseries of length m+1 will be similar too. Here the length of the vector pairs, m, denotes the embedding dimension.
Time frame: 30 minutes
The predictability of the local field potentials (band-pass filtered between 15-30 Hz for beta) was analyzed using Sample Entropy (SampEn), a nonlinear measure suitable for physiological time series. SampEn may be a more consistent measure and more suitable to shorter time series data than approximate entropy, partially due to the elimination of counting self matches. SampEn is calculated as the negative logarithm of the estimated conditional probability that if consecutive subseries of length m are similar according to some preset tolerance r, the consecutive subseries of length m+1 will be similar too. Here the length of the vector pairs, m, denotes the embedding dimension.
Time frame: 30 minutes
Asymmetry during both forward walking and stepping in place was calculated using periods of walking or stepping when the subject was not freezing. According to previous studies, asymmetry is defined as: 100*(absolute value of the natural log of the shorter average swing time over the longer average swing time) or mathematically: 100*| ln (SSWT/LSWT) |
where SSWT = shorter mean swing time LSWT = longer mean swing time
Time frame: 30 minutes
Arrhythmicity during both forward walking and stepping in place was calculated using periods of walking or stepping when the subject was not freezing. According to previous studies, arrhythmicity is defined as the mean stride time coefficient of variation of both legs, and a greater stride time CV implies less rhythmic gait or stepping. Higher arrhythmicity corresponds to more arrhythmic, or more impaired, gait.
Time frame: 30 minutes
Kinematic Features associated with Freezing of Gait
Time frame: 30 minutes
Freezing of gait episodes during stepping in place were identified using a validated computerized algorithm, and during forward walking by a blinded rater. The percent time freezing was calculated by dividing the time spent freezing by the total time to complete the task then multiplying by 100 to get a percent. If no freezing was observed, then the percent time freezing reported was 0.0%.
Time frame: 30 minutes
Freezing of gait episodes during stepping in place were identified using a validated computerized algorithm. The percent time freezing was calculated by dividing the time spent freezing by the total time to complete the task then multiplying by 100 to get a percent. If no freezing was observed, then the percent time freezing reported was 0.0%. The percent time spent freezing was compared while the participant was doing the stepping in place task on continuous deep brain stimulation (cDBS) and while the participant was doing the stepping in place task on adaptive deep brain stimulation (aDBS).
Stanford University
Other
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