Evidence map›Paper›PMID 38694258›Full record

ArticleFrontiers in aging neuroscience2024

Morphological changes in the cerebellum during aging: evidence from convolutional neural networks and shape analysis.

Yu Wang, Ye Teng, Tianci Liu, Yuchun Tang, Wenjia Liang, Wenjun Wang, Zhuoran Li, Qing Xia, Feifei Xu, Shuwei Liu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in aging neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
3.7field-weighted citation impact, top 7% of its field
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

11 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
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  5. Review
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  9. Glial Cells and Aging: From the CNS to the Cerebellum.International journal of molecular sciences · 2025
    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

10 authors at 3 institutions in 1 country.

Yu WangDepartment of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
Ye TengDepartment of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
Tianci LiuDepartment of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
Yuchun TangDepartment of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
Wenjia LiangDepartment of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
Wenjun WangDepartment of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
Zhuoran LiDepartment of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Qing XiaDepartment of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
Feifei XuDepartment of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
Shuwei LiuDepartment of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Provincial Key Laboratory of Mental Disorder, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
Shanghai Center for Brain Science and Brain-Inspired Technology · CNShandong University · CNShandong First Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The morphology and function of the cerebellum are associated with various developmental disorders and healthy aging. Changes in cerebellar morphology during the aging process have been extensively investigated, with most studies focusing on changes in cerebellar regional volume. The volumetric method has been used to quantitatively demonstrate the decrease in the cerebellar volume with age, but it has certain limitations in visually presenting the morphological changes of cerebellar atrophy from a three-dimensional perspective. Thus, we comprehensively described cerebellar morphological changes during aging through volume measurements of subregions and shape analysis. This study included 553 healthy participants aged 20-80 years. A novel cerebellar localized segmentation algorithm based on convolutional neural networks was utilized to analyze the volume of subregions, followed by shape analysis for localized atrophy assessment based on the cerebellar thickness. The results indicated that out of the 28 subregions in the absolute volume of the cerebellum, 15 exhibited significant aging trends, and 16 exhibited significant sex differences. Regarding the analysis of relative volume, only 11 out of the 28 subregions of the cerebellum exhibited significant aging trends, and 4 exhibited significant sex differences. The results of the shape analysis revealed region-specific atrophy of the cerebellum with increasing age. Regions displaying more significant atrophy were predominantly located in the vermis, the lateral portions of bilateral cerebellar hemispheres, lobules I-III, and the medial portions of the posterior lobe. This atrophy differed between sexes. Men exhibited slightly more severe atrophy than women in most of the cerebellar regions. Our study provides a comprehensive perspective for observing cerebellar atrophy during the aging process.

Indexed as

agingcerebellar atrophyconvolutional neural networkssex differenceshape analysis

Identifiers

PMID38694258
PMCPMC11061448
OpenAlexW4394890157

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.