Evidence map›Paper›PMID 40303467›Full record

ArticleFrontiers in aging neuroscience2025

Changes of brain structure and structural covariance networks in Parkinson's disease with different sides of onset.

Tianqi Xu, Zhihuai Deng, Yinhui Yu, Wenchao Duan, Zeyu Ma, Haoran Liu, Lianling Li, Moxuan Zhang, Siyu Zhou, Pengda Yang and 4 more

Abstract read
In one paragraph

Article in Frontiers in aging neuroscience, 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

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

14 authors.

Tianqi Xu *Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Zhihuai Deng *Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Yinhui Yu *Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Wenchao DuanDepartment of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Zeyu MaDepartment of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Haoran LiuDepartment of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Lianling LiDepartment of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Moxuan ZhangBeijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Siyu ZhouBeijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Pengda YangBeijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Xueyan QinYantai Affiliated Hospital of Binzhou Medical University, Yantai, Shandong, China.
Zhenyu ZhangDepartment of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Fangang MengDepartment of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Yuchen JiDepartment of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Parkinson's disease (PD) typically presents with unilateral symptoms in early stages, starting on one side and progressing, with the onset side showing more severe motor symptoms even after bilateralization. This asymmetry may reflect complex interactions among multiple brain regions and their network connections. In this study, we aimed to use surface-based morphometry (SBM) and structural covariance networks (SCNs) to investigate the differences in brain structure and network characteristics between patients with left-onset PD (LPD) and right-onset PD (RPD). Methods: A total of 51 LPD and 49 RPD patients were recruited. Clinical assessments included the Unified Parkinson's Disease Rating Scale motor section, Hoehn and Yahr stage, Mini-Mental State Examination, Parkinson's Disease Questionnaire, and Beck Depression Inventory. All participants underwent 3 T structural MRI. FreeSurfer was used to perform vertex-wise comparisons of cortical surface area (CSA) and cortical thickness (CT), whereas the Brain Connectivity Toolbox was implemented to construct and analyze the structural covariance networks. Results: In patients with LPD, we found reduced CSA in the right supramarginal gyrus (SMG), right precuneus (PCUN), left inferior parietal lobule (IPL), and left lingual gyrus (LING) compared to RPD, while no significant differences in CT were found between the two groups. The CSA of the right PCUN showed a significant positive correlation with MMSE score in LPD patients. In our SCNs analysis, LPD patients exhibited increased normalized characteristic path length and decreased small-world index in CSA-based networks, while in CT-based networks, they showed increased small-world index and global efficiency compared to RPD. No significant differences in nodal characteristics were observed in either CSA-based or CT-based networks between the two groups. Conclusion: In patients with LPD, reductions in CSA observed in the right PCUN, right SMG, left IPL, and left LING may be associated with cognitive impairments and hallucinations among non-motor symptoms of PD. Additionally, the SCNs of LPD and RPD patients show significant differences in global topology, but regional node characteristics do not reflect lateralization differences. These findings offer new insights into the mechanisms of symptom lateralization in PD from the perspective of brain regional structure and network topology.

Indexed as

asymmetrycortical surface areacortical thicknessParkinson’s diseaseside-of-onsetstructural covariance networkssurface-based morphometry

Identifiers

PMID40303467
PMCPMC12037599

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.