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Analyzing Mitochondrial Transport and Morphology in Human Induced Pluripotent Stem Cell-Derived Neurons in Hereditary Spastic Paraplegia
Published on: February 9, 2020
Gait phenotypes in paediatric hereditary spastic paraplegia revealed by dynamic time warping analysis and random
Irene Pulido-Valdeolivas1,2, David Gómez-Andrés1,3, Juan Andrés Martín-Gonzalo1,4
1Department of Anatomy, Histology and Neuroscience, TRADESMA-IdiPaz Universidad Autónoma de Madrid, Madrid, Spain.
Insights
Hereditary Spastic Paraplegias (HSP) gait can be classified into six patterns using dynamic time warping, aiding in personalized therapy. Key indicators include pelvic tilt and hip flexion for gait pattern differentiation.
Area of Science:
- Neurology
- Biomechanical Engineering
- Clinical Gait Analysis
Background:
- Hereditary Spastic Paraplegias (HSP) encompass diverse neurological disorders causing varied gait abnormalities.
- Current gait analysis methods offer limited insight into the entire gait cycle structure.
- Phenotype classification is crucial for disease monitoring and tailored therapeutic interventions in HSP.
Purpose of the Study:
- To classify gait phenotypes in children with HSP using advanced time series analysis.
- To identify key kinematic parameters predictive of specific gait patterns and disease severity.
- To provide clinicians with objective measures for daily practice and patient management.
Main Methods:
- Acquisition of sagittal joint angular position data (pelvis, hip, knee, ankle, forefoot) using optokinetic instrumental gait analysis (IGA).
- Application of hierarchical clustering analysis with multivariate dynamic time warping (DTW) to time series data.
- Utilizing random forests to identify significant gait parameters for classification.
Main Results:
- DTW identified six distinct gait patterns in HSP patients, correlating with age, sex, GMFCS stage, and comorbidities.
- Mean pelvic tilt and hip flexion at initial contact emerged as critical differentiators between patterns.
- Specific parameters like time of support, hip extension, and knee flexion at initial contact distinguished mild HSP from healthy gaits.
Conclusions:
- A novel classification of HSP gait phenotypes is established through multivariate DTW.
- Key kinematic parameters, particularly at initial contact, are identified for differentiating gait patterns and disease severity.
- This classification framework supports objective patient assessment and personalized treatment strategies for HSP.
Abstract:
The Hereditary Spastic Paraplegias (HSP) are a group of heterogeneous disorders with a wide spectrum of underlying neural pathology, and hence HSP patients express a variety of gait abnormalities. Classification of these phenotypes may help in monitoring disease progression and personalizing therapies. This is currently managed by measuring values of some kinematic and spatio-temporal parameters at certain moments during the gait cycle, either in the doctor´s surgery room or after very precise measurements produced by instrumental gait analysis (IGA). These methods, however, do not provide information about the whole structure of the gait cycle. Classification of the similarities among time series of IGA measured values of sagittal joint positions throughout the whole gait cycle can be achieved by hierarchical clustering analysis based on multivariate dynamic time warping (DTW). Random forests can estimate which are the most important isolated parameters to predict the classification revealed by DTW, since clinicians need to refer to them in their daily practice. We acquired time series of pelvic, hip, knee, ankle and forefoot sagittal angular positions from 26 HSP and 33 healthy children with an optokinetic IGA system. DTW revealed six gait patterns with different degrees of impairment of walking speed, cadence and gait cycle distribution and related with patient's age, sex, GMFCS stage, concurrence of polyneuropathy and abnormal visual evoked potentials or corpus callosum. The most important parameters to differentiate patterns were mean pelvic tilt and hip flexion at initial contact. Longer time of support, decreased values of hip extension and increased knee flexion at initial contact can differentiate the mildest, near to normal HSP gait phenotype and the normal healthy one. Increased values of knee flexion at initial contact and delayed peak of knee flexion are important factors to distinguish GMFCS stages I from II-III and concurrence of polyneuropathy.
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