Evidence map›Paper›PMID 42174463›Full record

ArticleBMC medical imaging2026

Brain structural volume changes and gait disorders in patients with cerebral small vessel disease: a principal component and logistic regression analysis.

Hong-Bo Wang, Hua-Wei Chen, Xiao-Ying Zhao, Jia-Jing Tian, Bing-Bing Gao, Yan-Wei Miao, Bing-Wei Zhang

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Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Hong-Bo WangDepartment of Neurology and Psychiatry, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China.
Hua-Wei ChenDalian Rehabilitation Recuperation Center of Joint Logistics Support Force of PLA, Dalian, 116016, China.
Xiao-Ying ZhaoDepartment of Neurology and Psychiatry, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China.
Jia-Jing TianDepartment of Neurology and Psychiatry, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China.
Bing-Bing GaoDepartment of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China.
Yan-Wei MiaoDepartment of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China. ywmiao716@163.com.
Bing-Wei ZhangDepartment of Neurology and Psychiatry, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China. zhangbingweidoc@163.com.

Funding

Department of Science and Technology of Liaoning Province No.2024-MSLH-082Xingliao Talents Program-Distinguished Medical Experts XLYC2412084
6 · The paper itself

Abstract

aimsTo investigate the relationship between the relative volume changes of brain structures and gait disorders in patients with cerebral small vessel disease (CSVD), and to identify key brain volume indicators that influence gait disorders (GD) through principal component analysis (PCA) combined with logistic regression analysis.

methodsA total of 90 patients with CSVD were included and divided into two groups based on their Timed Up and Go Test (TUG) results: the CSVD gait disorder group (GD group, N = 50, TUG ≥ 11.5 s) and the CSVD non-gait disorder group (NGD group, N = 40, TUG < 11.5 s). Brain structural volumes and associated imaging markers were calculated using a 3.0T superconducting magnetic resonance imaging (MRI) system combined with an intelligent image analysis platform. The relative volume data of brain structures were subjected to PCA for dimensionality reduction. Regression analysis of the principal components was performed to identify key predictive indicators for gait disorders.

resultsPrincipal components 2 and 3 exhibited significant predictive power for gait disorders. Component 2 primarily represented cerebrospinal fluid, the temporal lobe, and the frontal lobe, while Component 3 primarily represented the third ventricle, lateral ventricle, and corpus callosum. Analysis of component loadings indicated that the expansion of cerebrospinal fluid-related structures was positively correlated with the severity of gait disorders, while atrophy of brain tissue structures showed a negative correlation. No significant difference was observed in the total CSVD burden score between the GD and NGD groups.

conclusionThe enlargement of the cerebrospinal fluid spaces and the ventricular system are key factors contributing to gait disorders in CSVD patients. Additionally, atrophy in cortical areas such as the temporal and frontal lobes, as well as the corpus callosum, plays a predictive role in the severity of gait disturbances. The total CSVD burden score does not adequately reflect the degree of gait impairment and requires further refinement for clinical application.

Indexed as

BrainCerebral Small Vessel DiseasesGait Disorders, NeurologicAgedFemaleHumansLogistic ModelsMagnetic Resonance ImagingMaleMiddle AgedOrgan SizePrincipal Component AnalysisBrain structure and volumeCerebral small vessel diseaseGait disturbanceLogistic regression analysisPrincipal component analysis

Identifiers

PMID42174463
PMCPMC13374239

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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.