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<p>Multiple system atrophy-cerebellar type (MSA-C) is a rapidly progressive neurodegenerative disorder, yet objective digital biomarkers for disease severity remain scarce. This cross-sectional study aimed to identify disease-relevant gait patterns using a 2D video-based gait analysis algorithm and examine their clinical and neuroimaging correlates. Gait features were extracted from videos of patients with MSA-C using <i>Gaitome</i>, and an MSA-C gait pattern score was derived. This score significantly distinguished MSA-C from healthy controls (area under the curve = 0.98) and showed significant correlations with UMSAR part I (<i>r</i> = 0.49, <i>p</i> = 0.0014), part II (<i>r</i> = 0.51, <i>p</i> = 0.0014), MMSE (<i>r</i> = − 0.43, <i>p</i> = 0.012), and MoCA (<i>r</i> = − 0.34, <i>p</i> = 0.049). Tractography revealed significant associations between the gait score and structural connectivity in the middle cerebellar peduncle, cerebellum, and cingulate. Voxel-based morphometry showed that the gait score correlated with gray matter volume in the middle temporal and cerebellar regions, whereas UMSAR part II did not show significant structural associations. These findings suggest that gait patterns extracted from a single video camera can reflect both motor and cognitive severity in MSA-C, and may serve as a practical, non-invasive digital biomarker for disease monitoring.</p>

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Clinical and neuroimaging correlates of disease related gait patterns in patients with multiple system atrophy cerebellar type

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月15日

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  • Seungmin Lee,
  • Minchul Kim,
  • Kyu Sung Choi,
  • Chanhee Jeong,
  • Ri Yu,
  • Jee-Young Lee,
  • Jung Hwan Shin,
  • Han-Joon Kim,
  • Beomseok Jeon

摘要

Multiple system atrophy-cerebellar type (MSA-C) is a rapidly progressive neurodegenerative disorder, yet objective digital biomarkers for disease severity remain scarce. This cross-sectional study aimed to identify disease-relevant gait patterns using a 2D video-based gait analysis algorithm and examine their clinical and neuroimaging correlates. Gait features were extracted from videos of patients with MSA-C using Gaitome, and an MSA-C gait pattern score was derived. This score significantly distinguished MSA-C from healthy controls (area under the curve = 0.98) and showed significant correlations with UMSAR part I (r = 0.49, p = 0.0014), part II (r = 0.51, p = 0.0014), MMSE (r = − 0.43, p = 0.012), and MoCA (r = − 0.34, p = 0.049). Tractography revealed significant associations between the gait score and structural connectivity in the middle cerebellar peduncle, cerebellum, and cingulate. Voxel-based morphometry showed that the gait score correlated with gray matter volume in the middle temporal and cerebellar regions, whereas UMSAR part II did not show significant structural associations. These findings suggest that gait patterns extracted from a single video camera can reflect both motor and cognitive severity in MSA-C, and may serve as a practical, non-invasive digital biomarker for disease monitoring.