Dataset Information

Multi-site identification and generalization of clusters of walking behaviors in individuals with chronic stroke and neurotypical controls.

ABSTRACT:

Background

Walking patterns in stroke survivors are highly heterogeneous, which poses a challenge in systematizing treatment prescriptions for walking rehabilitation interventions.

Objective

We used bilateral spatiotemporal and force data during walking to create a multi-site research sample to: 1) identify clusters of walking behaviors in people post-stroke and neurotypical controls, and 2) determine the generalizability of these walking clusters across different research sites. We hypothesized that participants post-stroke will have different walking impairments resulting in different clusters of walking behaviors, which are also different from control participants.

Methods

We gathered data from 81 post-stroke participants across four research sites and collected data from 31 control participants. Using sparse K-means clustering, we identified walking clusters based on 17 spatiotemporal and force variables. We analyzed the biomechanical features within each cluster to characterize cluster-specific walking behaviors. We also assessed the generalizability of the clusters using a leave-one-out approach.

Results

We identified four stroke clusters: a fast and asymmetric cluster, a moderate speed and asymmetric cluster, a slow cluster with frontal plane force asymmetries, and a slow and symmetric cluster. We also identified a moderate speed and symmetric gait cluster composed of controls and participants post-stroke. The moderate speed and asymmetric stroke cluster did not generalize across sites.

Conclusions

Although post-stroke walking patterns are heterogenous, these patterns can be systematically classified into distinct clusters based on spatiotemporal and force data. Future interventions could target the key features that characterize each cluster to increase the efficacy of interventions to improve mobility in people post-stroke.

SUBMITTER: Sanchez N

PROVIDER: S-EPMC10197630 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Multi-site identification and generalization of clusters of walking behaviors in individuals with chronic stroke and neurotypical controls.

Sánchez Natalia N Schweighofer Nicolas N Mulroy Sara J SJ Roemmich Ryan T RT Kesar Trisha M TM Torres-Oviedo Gelsy G Fisher Beth E BE Finley James M JM Winstein Carolee J CJ

bioRxiv : the preprint server for biology 20231030

<h4>Background</h4>Walking patterns in stroke survivors are highly heterogeneous, which poses a challenge in systematizing treatment prescriptions for walking rehabilitation interventions.<h4>Objective</h4>We used bilateral spatiotemporal and force data during walking to create a multi-site research sample to: 1) identify clusters of walking behaviors in people post-stroke and neurotypical controls, and 2) determine the generalizability of these walking clusters across different research sites. ...[more]

PMID: 37214916

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Project description:Muscle coordination is often impaired after stroke, leading to deficits in the control of walking and balance. In this study, we examined features of muscle coordination associated with reduced walking performance in chronic stroke survivors using motor module (a.k.a. muscle synergy) analysis. We identified differences between stroke survivors and age-similar neurotypical controls in the modular control of both overground walking and standing reactive balance. In contrast to previous studies that demonstrated reduced motor module number poststroke, our cohort of stroke survivors did not exhibit a reduction in motor module number compared with controls during either walking or reactive balance. Instead, the pool of motor modules common to walking and reactive balance was smaller, suggesting reduced generalizability of motor module function across behaviors. The motor modules common to walking and reactive balance tended to be less variable and more distinct, suggesting more reliable output compared with motor modules specific to either behavior. Greater motor module generalization in stroke survivors was associated with faster walking speed, more normal step length asymmetry, and narrower step widths. Our work is the first to show that motor module generalization across walking and balance may help to distinguish important and clinically relevant differences in walking performance across stroke survivors that would have been overlooked by examining only a single behavior. Finally, because similar relationships between motor module generalization and walking performance have been demonstrated in healthy young adults and individuals with Parkinson's disease, this suggests that motor module generalization across walking and balance may be important for well-coordinated walking. NEW & NOTEWORTHY This is the first work to simultaneously examine neuromuscular control of walking and standing reactive balance in stroke survivors. We show that motor module generalization across these behaviors (i.e., recruiting common motor modules) is reduced compared with controls and is associated with slower walking speeds, asymmetric step lengths, and larger step widths. This is true despite no between-group differences in module number, suggesting that motor module generalization across walking and balance is important for well-coordinated walking.

Project description:BackgroundHeterogeneous types of gait impairment are common post-stroke. Studies used supervised and unsupervised machine learning on discrete biomechanical features to summarize the gait cycle and identify common patterns of gait behaviors. However, discrete features cannot account for temporal variations that occur during gait. Here, we propose a novel machine-learning pipeline to identify subgroups of gait behaviors post-stroke using kinematic time series data.MethodsWe analyzed ankle and knee kinematic data during treadmill walking data in 39 individuals post-stroke and 28 neurotypical controls. The data were first input into a supervised dual-stage Convolutional Neural Network-Temporal Convolutional Network, trained to extract temporal and spatial gait features. Then, we used these features to find clusters of different gait behaviors using unsupervised time series k-means. We repeated the clustering process using 10,000 bootstrap training data samples and a Gaussian Mixture Model to identify stable clusters representative of our dataset. Finally, we assessed the kinematic differences between the identified clusters using 1D statistical parametric mapping ANOVA. We then compared gait spatiotemporal and clinical characteristics between clusters using one-way ANOVA.ResultsWe obtained five clusters: two clusters of neurotypical individuals (C1 and C2) and three clusters of individuals post-stroke (S1, S2, S3). C1 had kinematics that resembled the normative gait pattern. Individuals in C2 had a shorter stride time than C1. Individuals in S1 had mild impairment and walked with increased bilateral knee flexion during the loading response. Individuals in S2 had moderate impairment, were the slowest among the clusters, took shorter steps, had increased knee flexion during stance bilaterally and reduced paretic knee flexion during swing. Individuals in S3 had mild impairment, asymmetric swing time, had increased ankle abduction during the gait cycle and reduced dorsiflexion bilaterally during loading response and stance. Every individual was assigned to a cluster with a cluster membership likelihood above 93%.ConclusionsOur results indicate that joint kinematics in individuals post-stroke are distinct from controls, even in those individuals with mild impairment. The three subgroups post-stroke showed distinct kinematic impairments during specific phases in the gait cycle, providing additional information to clinicians for gait retraining interventions.

Dataset Information

Multi-site identification and generalization of clusters of walking behaviors in individuals with chronic stroke and neurotypical controls.

Background

Objective

Methods

Results

Conclusions

Publications

Multi-site identification and generalization of clusters of walking behaviors in individuals with chronic stroke and neurotypical controls.

Similar Datasets

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