Quantification of Gait Abnormalities in Healthy-Looking Multiple Sclerosis Patients (with Expanded Disability Status Scale 0-1.5)

Novotná, Klára; Sobíšek, Lukáš; Horáková, Dana; Havrdová, Eva; Preiningerová, Jana Lízrová

doi:10.1159/000448091

Cited by 13 publications

(8 citation statements)

References 32 publications

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“…Walking is a complex task involving the cooperation of several bodily functional systems, including pyramidal motor movement control and cerebellar coordination and balance [6]. Previous studies have provided evidence that gait abnormalities may well be present before they become clinically apparent [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis

et al. 2021

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In multiple sclerosis (MS), gait impairment is one of the most prominent symptoms. For a sensitive assessment of pathological gait patterns, a comprehensive analysis and processing of several gait analysis systems is necessary. The objective of this work was to determine the best diagnostic gait system (DIERS pedogait, GAITRite system, and Mobility Lab) using six machine learning algorithms for the differentiation between people with multiple sclerosis (pwMS) and healthy controls, between pwMS with and without fatigue and between pwMS with mild and moderate impairment. The data of the three gait systems were assessed on 54 pwMS and 38 healthy controls. Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbor, and Support Vector Machines (SVM) with linear, radial basis function (rbf) and polynomial kernel were applied for the detection of subtle walking changes. The best performance for a healthy-sick classification was achieved on the DIERS data with a SVM rbf kernel (k = 0.49 ± 0.11). For differentiating between pwMS with mild and moderate disability, the GAITRite data with the SVM linear kernel (k = 0.61 ± 0.06) showed the best performance. This study demonstrates that machine learning methods are suitable for identifying pathologic gait patterns in early MS.

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Section: Introductionmentioning

confidence: 99%

Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis

et al. 2021

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“…They could show that, compared to healthy controls, RRMS show an increase of Swing Time [66]. Beyond, Novotna et al were able to show that MS patients with no apparent disability (EDSS 0-1.5) showed abnormalities in the GAITRite gait analysis instrument [67], suggesting that particular aspects of human gait abnormalities can be investigated in mice.…”

Section: Discussionmentioning

confidence: 99%

High Speed Ventral Plane Videography as a Convenient Tool to Quantify Motor Deficits during Pre-Clinical Experimental Autoimmune Encephalomyelitis

Zhan

Yakimov

Rühling

et al. 2019

Cells

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Experimental autoimmune encephalomyelitis (EAE) is the most commonly used multiple sclerosis animal model. EAE mice typically develop motor deficits in a caudal-to-rostral pattern when inflammatory lesions have already developed. However, to monitor more subtle behavioral deficits during lesion development (i.e., pre-clinical phase), more sophisticated methods are needed. Here, we investigated whether high speed ventral plane videography can be applied to monitor early motor deficits during ‘pre-clinical’ EAE. For this purpose, EAE was induced in C57BL/6 mice and gait abnormalities were quantified using the DigiGait™ apparatus. Gait deficits were related to histopathological changes. 10 out of 10 control (100%), and 14 out of 18 (77.8%) pre-clinical EAE mice could be evaluated using DigiGait™. EAE severity was not influenced by DigiGait™-related mice handlings. Most gait parameters recorded from day 6 post-immunization until the end of the experiment were found to be stable in control mice. During the pre-clinical phase, when conventional EAE scorings failed to detect any functional impairment, EAE mice showed an increased Swing Time, increased %Swing Stride, decreased %Stance Stride, decreased Stance/Swing, and an increased Absolute Paw Angle. In summary, DigiGait™ is more sensitive than conventional scoring approaches to study motor deficits during the EAE pre-clinical phase.

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“…The GaitRite system is a widely accepted tool used for capturing spatiotemporal parameters of gait in multiple sclerosis, including patients with minimal disability. 19,20…”

Section: Methodsmentioning

confidence: 99%

“…The GaitRite system is a widely accepted tool used for capturing spatiotemporal parameters of gait in multiple sclerosis, including patients with minimal disability. 19,20 In the present study, we extracted the walking speed (cm/s) and variability of step time expressed by the coefficient of variation (SD/ mean). The coefficient of variation (%) was operationalized as a measure of relative gait variability and selected in line with previous studies demonstrating their relevance in gait difficulties in the multiple sclerosis population.…”

Section: Gait Assessmentmentioning

confidence: 99%

Gait Variability, Not Walking Speed, Is Related to Cognition in Adolescents With Multiple Sclerosis

2018

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Gait variability is associated with cognitive performance in people with central neurologic damage illnesses, which includes multiple sclerosis. However, presently, there have been no data available as to whether this association exists in adolescents with multiple sclerosis. Therefore, our objective was to investigate the association between gait variability and cognition in adolescents with multiple sclerosis encompassing 48 recently diagnosed adolescents with multiple sclerosis (26 girls; 22 boys), average age of 16.0 years (SD ¼ 2.2), and an Expanded Disability Status Scale (EDSS) score of 1.6 (SD ¼ 1.3). Walking speed and gait variability expressed by the coefficient of variation of the mean step time was studied using an electronic walkway. A computerized cognitive battery of tests evaluated cognition. Cognitive outcome measurements included verbal and nonverbal memory, executive function, visual spatial processing, verbal function, attention, information processing speed, and motor skills. Mean walking speed was 76.9 cm/s (SD ¼ 57.6); mean step time variability was 3.5 (SD ¼ 1.3) and the global cognitive score was 93.9 (SD ¼ 12.5). According to linear regression analysis, a significant association was found between step time variability, cognitive subdomains of attention, and information processing speed. After incorporating walking speed into the model, the association remained significant. Increased gait variability, not walking speed, is suggested as a clinical marker of cognitive performance in minimally disabled adolescents with multiple sclerosis.

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Quantification of Gait Abnormalities in Healthy-Looking Multiple Sclerosis Patients (with Expanded Disability Status Scale 0-1.5)

Cited by 13 publications

References 32 publications

Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis

Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis

High Speed Ventral Plane Videography as a Convenient Tool to Quantify Motor Deficits during Pre-Clinical Experimental Autoimmune Encephalomyelitis

Gait Variability, Not Walking Speed, Is Related to Cognition in Adolescents With Multiple Sclerosis

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