Translational methods to detect asymmetries in temporal and spatial walking metrics in parkinsonian mouse models and human subjects with Parkinson’s disease

Abstract

Clinical signs in Parkinson’s disease (PD), including parkinsonian gait, are often asymmetric, but mechanisms underlying gait asymmetries in PD remain poorly understood. A translational toolkit, a set of standardized measures to capture gait asymmetries in relevant mouse models and patients, would greatly facilitate research efforts. We validated approaches to quantify asymmetries in placement and timing of limbs in mouse models of parkinsonism and human PD subjects at speeds that are relevant for human walking. In mice, we applied regression analysis to compare left and right gait metrics within a condition. To compare alternation ratios of left and right limbs before and after induction of parkinsonism, we used circular statistics. Both approaches revealed asymmetries in hind- and forelimb step length in a unilateral PD model, but not in bilateral or control models. In human subjects, a similar regression approach showed a step length asymmetry in the PD but not control group. Sub-analysis of cohorts with predominant postural instability-gait impairment and with predominant tremor revealed asymmetries for step length in both cohorts and for swing time only in the former cohort. This translational approach captures asymmetries of gait in mice and patients. Application revealed striking differences between models, and that spatial and temporal asymmetries may occur independently. This approach will be useful to investigate circuit mechanisms underlying the heterogeneity between models.

Shifts in Gait Signatures Mark the End of Lifespan in Mice, With Sex Differences in Timing

“…While inclusion of a restricted set of primary gait metrics that dictate speed (stride length, swing, and stance time) is a strength ( Lord et al, 2013 ), there is a multitude of other gait metrics including variability or asymmetry ( Broom et al, 2019 ) that we did not touch upon. Similarly, inclusion of balance and locomotor tests in addition to measures of gait quality provided insights in age-related changes in these modalities during aging, but we did not include other frequently used tests such as rotarod or horizontal ladder.…”

“…Another relative weakness is that while this study provides insights into aging-related decline in the absence of common neuropathologies, it does not inform on the effects of (combinations of) specific neuropathologies that are common in older adults (see below). Further studies in mouse models that carry key features of neurodegenerative disorders ( Lok et al, 2013 ; Garvock-de Montbrun et al, 2019 ) will be necessary to reveal the distinctive patterns of gait signature shifts associated with various pathologies and their location as shown for different Parkinson’s models ( Broom et al, 2017 , 2019 ).…”

“…We used a validated approach to analyze gait metrics that were captured on video ( Akay et al, 2006 ; Broom et al, 2017 , 2019 ). Spatial (location of the footfalls) and temporal data (time stamps of stance and swing phases) were extracted from video frames using custom MATLAB code to obtain the following parameters: stride velocity, swing and stance time, stride length, cadence, and swing speed ( Broom et al, 2017 ).…”

“…This approach has the advantage that it takes into account the entire model, not only intercept or slope. While gait signatures can be reconstructed for both fore- and hindlimbs ( Broom et al, 2019 ), here we focus on hindlimb metrics as these are most important for propulsion and are most relevant for human gait comparisons. To provide insight in animal to animal variability, we supplemented this with a comparison of averaged data sets at baseline (19 weeks) and at aged time points using RM-one-way-ANOVA, followed by Dunnett’s multiple comparison tests as appropriate.…”

“…This approach has translational relevance, as the concept of gait signatures is conserved among quadruped and biped models ( Hutchinson et al, 2006 ; Witte et al, 2006 ; Broom et al, 2017 ). We employed this methodology to show that walking style changes in patients with neurodegenerative disorders as well as in murine disease models ( Neckel et al, 2013 ; Broom et al, 2017 , 2019 ) and that distinct changes in walking style are the result of modulation of highly specific circuit nodes in the CNS [Worley, submitted].…”