Evidence map›Paper›PMID 37202161›Full record

ArticleNeurology2023

Association of Cortical and Subcortical Microstructure With Clinical Progression and Fluid Biomarkers in Patients With Parkinson Disease.

Linbo Wang, Cheng Zhou, Wei Zhang, Minming Zhang, Wei Cheng, Jianfeng Feng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

12 citing papers in PubMed.

  1. Article
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  4. Advanced brain aging, selective vulnerability in gray matter, and cognition in Parkinson's disease.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025
    Article
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  9. Review
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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

6 authors.

Linbo WangFrom the Institute of Science and Technology for Brain-Inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; MOE Frontiers Center for Brain Science (L.W., W.Z., W.C., J.F.), Fudan University; Zhangjiang Fudan International Innovation Center (L.W., W.Z., W.C., J.F.), Shanghai; Department of Radiology (C.Z., M.Z.), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C.), Zhejiang Normal University, Jinhua, China; and Department of Computer Science (J.F.), University of Warwick, Coventry, United Kingdom.
Cheng ZhouFrom the Institute of Science and Technology for Brain-Inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; MOE Frontiers Center for Brain Science (L.W., W.Z., W.C., J.F.), Fudan University; Zhangjiang Fudan International Innovation Center (L.W., W.Z., W.C., J.F.), Shanghai; Department of Radiology (C.Z., M.Z.), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C.), Zhejiang Normal University, Jinhua, China; and Department of Computer Science (J.F.), University of Warwick, Coventry, United Kingdom.
Wei ZhangFrom the Institute of Science and Technology for Brain-Inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; MOE Frontiers Center for Brain Science (L.W., W.Z., W.C., J.F.), Fudan University; Zhangjiang Fudan International Innovation Center (L.W., W.Z., W.C., J.F.), Shanghai; Department of Radiology (C.Z., M.Z.), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C.), Zhejiang Normal University, Jinhua, China; and Department of Computer Science (J.F.), University of Warwick, Coventry, United Kingdom.
Minming ZhangFrom the Institute of Science and Technology for Brain-Inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; MOE Frontiers Center for Brain Science (L.W., W.Z., W.C., J.F.), Fudan University; Zhangjiang Fudan International Innovation Center (L.W., W.Z., W.C., J.F.), Shanghai; Department of Radiology (C.Z., M.Z.), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C.), Zhejiang Normal University, Jinhua, China; and Department of Computer Science (J.F.), University of Warwick, Coventry, United Kingdom.
Wei ChengFrom the Institute of Science and Technology for Brain-Inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; MOE Frontiers Center for Brain Science (L.W., W.Z., W.C., J.F.), Fudan University; Zhangjiang Fudan International Innovation Center (L.W., W.Z., W.C., J.F.), Shanghai; Department of Radiology (C.Z., M.Z.), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C.), Zhejiang Normal University, Jinhua, China; and Department of Computer Science (J.F.), University of Warwick, Coventry, United Kingdom.
Jianfeng FengFrom the Institute of Science and Technology for Brain-Inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; MOE Frontiers Center for Brain Science (L.W., W.Z., W.C., J.F.), Fudan University; Zhangjiang Fudan International Innovation Center (L.W., W.Z., W.C., J.F.), Shanghai; Department of Radiology (C.Z., M.Z.), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C.), Zhejiang Normal University, Jinhua, China; and Department of Computer Science (J.F.), University of Warwick, Coventry, United Kingdom. jffeng@fudan.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesMean diffusivity (MD) of diffusion MRI (dMRI) has been used to measure cortical and subcortical microstructural properties. This study investigated relationships of cortical and subcortical MD, clinical progression, and fluid biomarkers in Parkinson disease (PD).

methodsThis longitudinal study using data from the Parkinson's Progression Markers Initiative was collected from April 2011 to July 2022. Clinical symptoms were assessed with Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (UPDRS) and Montreal Cognitive Assessment (MoCA) scores. Clinical assessments were followed up to 5 years. Linear mixed-effects (LME) models were performed to examine associations of MD and the annual rate of changes in clinical scores. Partial correlation analysis was conducted to examine the associations of MD and fluid biomarker levels.

resultsA total of 174 patients with PD (age 61.9 ± 9.7 years, 63% male) with baseline dMRI and at least 2 years of clinical follow-up were included. Results of LME models revealed a significant association between MD values, predominantly in subcortical regions, temporal lobe, occipital lobe, and frontal lobe, and annual rate of changes in clinical scores (UPDRS-Part-I, standardized β > 2.35; UPDRS-Part-II, standardized β > 2.34; postural instability and gait disorder score, standardized β > 2.47; MoCA, standardized β < -2.42; all DISCUSSION: In this cohort study, cortical and subcortical MD values at baseline were associated with clinical progression and baseline fluid biomarkers, suggesting that microstructural properties could be useful for stratification of patients with fast clinical progression.

Indexed as

Parkinson DiseaseAgedBiomarkersCohort StudiesDisease ProgressionFemaleHumansLongitudinal StudiesMaleMiddle AgedSerotoninBiomarkersSerotonin

Identifiers

PMID37202161
PMCPMC10382272

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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.