The turning and barrier course reveals gait parameters for detecting freezing of gait and measuring the efficacy of deep brain stimulation

Abstract

Freezing of gait (FOG) is a devastating motor symptom of Parkinson's disease that leads to falls, reduced mobility, and decreased quality of life. Reliably eliciting FOG has been difficult in the clinical setting, which has limited discovery of pathophysiology and/or documentation of the efficacy of treatments, such as different frequencies of subthalamic deep brain stimulation (STN DBS). In this study we validated an instrumented gait task, the turning and barrier course (TBC), with the international standard FOG questionnaire question 3 (FOG-Q3, r = 0.74, p < 0.001). The TBC is easily assembled and mimics real-life environments that elicit FOG. People with Parkinson's disease who experience FOG (freezers) spent more time freezing during the TBC compared to during forward walking (p = 0.007). Freezers also exhibited greater arrhythmicity during non-freezing gait when performing the TBC compared to forward walking (p = 0.006); this difference in gait arrhythmicity between tasks was not detected in non-freezers or controls. Freezers' non-freezing gait was more arrhythmic than that of non-freezers or controls during all walking tasks (p < 0.05). A logistic regression model determined that a combination of gait arrhythmicity, stride time, shank angular range, and asymmetry had the greatest probability of classifying a step as FOG (area under receiver operating characteristic curve = 0.754). Freezers' percent time freezing and nonfreezing gait arrhythmicity decreased, and their shank angular velocity increased in the TBC during both 60 Hz and 140 Hz STN DBS (p < 0.05) to non-freezer values. The TBC is a standardized tool for eliciting FOG and demonstrating the efficacy of 60 Hz and 140 Hz STN DBS for gait impairment and FOG. The TBC revealed gait parameters that differentiated freezers from non-freezers and best predicted FOG; these may serve as relevant control variables for closed loop neurostimulation for FOG in Parkinson's disease.

Perspective: Evolution of Control Variables and Policies for Closed-Loop Deep Brain Stimulation for Parkinson’s Disease Using Bidirectional Deep-Brain-Computer Interfaces

“…During open loop STN DBS at both 60 Hz and 140 Hz, gait arrhythmicity and FOG improved and beta band power and burst durations decreased in freezers (Anidi et al, 2018;O'Day et al, 2020b). The normal gait rhythmicity and shorter burst durations were left unchanged during DBS in the non-freezers.…”

“…This revealed a functional relevance of beta-band burst durations as neural inputs for closed-loop DBS for gait impairment and FOG using bidirectional dBCIs. Sixty Hertz DBS resulted in improved rhythmicity in both progressive limb bradykinesia and during forward-walking tasks (Blumenfeld et al, 2017;Anidi et al, 2018;O'Day et al, 2020b). A superior effect of 60 Hz to high-frequency DBS for FOG has been reported (Moreau et al, 2008;Xie et al, 2015), suggesting the need for control policies to include adjustments in neurostimulation intensity and frequency.…”

Gait Parameters Measured from Wearable Sensors Reliably Detect Freezing of Gait in a Stepping in Place Task

“…Freezing of gait (FOG), often defined as a feeling of one's feet being "glued" to the floor [1,2], is a debilitating phenomenon in Parkinson's disease (PD) that negatively impacts quality of life and can lead to falls, serious injury, or even death [3][4][5][6]. FOG is a challenging phenomenon to objectively measure in the clinic and laboratory [7], but several tasks, such as 360-degree turning in place [8], the turning and barrier course (TBC) [9], and stepping in place (SIP) [10], have been developed to safely and reliably elicit FOG.…”

“…IMUs have been used to characterize and predict FOG during several tasks [9,, and many of these IMU-based FOG detection schemes rely on statistical and machine learning methods [9,[11][12][13][14][15][16][17][18][19][20][21][22]. For example, IMUs measured kinematic data during straight walking and the TBC [9,37]. A statistical model developed using this data revealed that certain gait parameters-stride time, swing angular range, asymmetry, and arrhythmicity-reliably predicted FOG during the TBC [9,37].…”

“…For example, IMUs measured kinematic data during straight walking and the TBC [9,37]. A statistical model developed using this data revealed that certain gait parameters-stride time, swing angular range, asymmetry, and arrhythmicity-reliably predicted FOG during the TBC [9,37]. No study has investigated if IMUs can detect FOG during SIP, and no statistical model has been developed to determine which gait parameters most reliably predict FOG during SIP.…”