Evidence map›Paper›PMID 41398003›Full record

ArticleScientific reports2025

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

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

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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Seungmin Lee *Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Minchul Kim *Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, South Korea.
Kyu Sung Choi *Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul National University, Seoul, South Korea.
Chanhee JeongDepartment of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Ri YuDepartment of Software and Computer Engineering, Ajou University, Suwon, South Korea.
Jee-Young LeeDepartment of Neurology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul National University College of Medicine, Seoul, South Korea.
Jung Hwan ShinDepartment of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea. neo2003@snu.ac.kr.
Han-Joon KimDepartment of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea. movement@snu.ac.kr.
Beomseok JeonDepartment of Neurology, Chung-ang University Health Care System Hyundae Hospital, Namyangju, South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

GaitMultiple System AtrophyNeuroimagingAgedCerebellumCross-Sectional StudiesFemaleGait AnalysisGray MatterHumansMaleMiddle AgedDTIMSA-C gait pattern scoreVBMVideo-based analysis

Identifiers

PMID41398003
PMCPMC12706005

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.