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Updated: May 20, 2026

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Quantitative Gait Analysis Reveals Distinct Patterns Associated With Pyramidal Involvement in Amyotrophic Lateral

Nan Hu1, Ming Qi1, Ning Su1

  • 1Department of Neurology, Peking Union Medical College Hospital, Beijing, China.

Brain and Behavior
|May 19, 2026
PubMed

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Summary
This summary is machine-generated.

Quantitative gait analysis reveals distinct gait changes in amyotrophic lateral sclerosis (ALS) patients with upper motor neuron (UMN) dysfunction. These findings support using objective gait metrics for identifying UMN involvement in ALS.

Area of Science:

  • Neurology
  • Biomechanical Engineering
  • Data Science

Background:

  • Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease affecting motor neurons.
  • Upper motor neuron (UMN) dysfunction is a key feature of ALS, leading to characteristic motor impairments.
  • Understanding specific gait abnormalities in ALS with UMN dysfunction is crucial for accurate diagnosis and management.

Purpose of the Study:

  • To identify specific gait abnormalities associated with UMN dysfunction in ALS.
  • To develop a multivariate classification model for identifying ALS patients with UMN dysfunction.
  • To control for overall disease severity (ALSFRS-R score) when analyzing gait parameters.

Main Methods:

  • 3D gait analysis was performed on 118 ALS patients and 1796 healthy controls.
Keywords:
UMN dysfunctionamyotrophic lateral sclerosisgait

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  • ALS patients were classified into UMN dysfunction (ALS-UMN) and no UMN signs (ALS-Numn) groups.
  • Partial correlation and machine learning (Random Forest, Lasso) were used to analyze gait parameters and UMN involvement.

    • Main Results:

    • ALS patients showed widespread gait deterioration compared to controls.
    • Reduced stride, increased step width, prolonged double support, and gait cycle time asymmetry were independently associated with UMN severity after controlling for ALSFRS-R.
    • A multivariate model achieved an AUC of 0.690 for identifying ALS-UMN patients, with high sensitivity (0.816).

    Conclusions:

    • Quantitative gait analysis identifies a distinct spatiotemporal gait pattern in ALS with UMN dysfunction.
    • A gait-feature-based model shows potential for identifying patients with pyramidal signs, especially due to its high sensitivity.
    • Objective gait metrics offer exploratory utility for motor phenotyping in ALS, requiring external validation.