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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
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Time series modeling characterizes stride time variability to identify individuals with neurodegenerative disorders
Yannis Halkiadakis1, Noah Davidson1, Kristin D Morgan1
1Biomedical Engineering, School of Engineering, University of Connecticut, Storrs, CT, USA.
Human Movement Science
|October 28, 2023
Summary
Individuals with Huntington's Disease exhibit chaotic gait dynamics, while those with Amyotrophic Lateral Sclerosis show more ordered stride patterns. This study quantifies gait variability differences in neurodegenerative disorders.
Area of Science:
- Neuroscience
- Biomechanical Engineering
- Data Science
Background:
- Neurodegenerative diseases like Amyotrophic Lateral Sclerosis (ALS) and Huntington's Disease (HD) cause progressive neuronal death, leading to altered gait and stride-to-stride variability.
- The specific gait alterations can differ between ALS and HD due to the distinct ways these diseases affect the central nervous system.
- Quantifying gait dynamics through time series analysis offers a potential method for detecting and differentiating these neurodegenerative conditions.
Purpose of the Study:
- To utilize autoregressive (AR) modeling time series analysis to quantify and compare stride time variability among individuals with ALS, HD, and healthy controls.
- To identify distinct gait dynamics associated with each group (ALS, HD, Controls) using quantitative metrics.
Main Methods:
- Collected continuous 5-minute stride time data from 15 Controls, 12 individuals with ALS, and 15 individuals with HD using force-sensitive resistors in footwear.
- Applied a second-order autoregressive (AR) model to the stride time data series.
- Used mean stride time and two AR model coefficients as key metrics to analyze stride time variability.
Main Results:
- Individuals with HD demonstrated significantly greater stride time variability, indicative of a more chaotic gait (p < 0.001).
- Individuals with ALS exhibited significantly more ordered and less variable stride time dynamics compared to controls and HD patients (p < 0.001).
- The identified stride time metrics effectively distinguished the gait dynamics across the three groups, highlighting significant differences.
Conclusions:
- Stride time variability analysis using AR modeling can successfully quantify and differentiate gait dynamics in neurodegenerative disorders like ALS and HD.
- The findings provide valuable insights into how these neuromuscular conditions disrupt motor coordination, leading to distinct compensatory gait strategies.
- This quantitative approach may aid in the early detection and characterization of gait disturbances in neurodegenerative diseases.
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