Evidence map›Paper›PMID 35077393›Full record

ArticleAging2022

Age-related alterations of brain metabolic network based on [18F]FDG-PET of rats.

Xin Xue, Jia-Jia Wu, Bei-Bei Huo, Xiang-Xin Xing, Jie Ma, Yu-Lin Li, Mou-Xiong Zheng, Xu-Yun Hua, Jian-Guang Xu

Open access · hybridAbstract read
In one paragraph

Article in Aging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.9field-weighted citation impact, top 29% 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

7 citing papers in PubMed, 10 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Brain metabolic network covariance and aging in a mouse model of Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024
    Article
  6. Resting-State Functional MRI and PET Imaging as Noninvasive Tools to Study (Ab)Normal Neurodevelopment in Humans and Rodents.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023
    Review
  7. 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 at 2 institutions in 1 country.

Xin XueSchool of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
Jia-Jia WuDepartment of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China.
Bei-Bei HuoSchool of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
Xiang-Xin XingSchool of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
Jie MaSchool of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
Yu-Lin LiSchool of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
Mou-Xiong ZhengDepartment of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China.
Xu-Yun HuaDepartment of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China.
Jian-Guang XuSchool of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
Shanghai University of Traditional Chinese Medicine · CNYueyang Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Using animal models to study the underlying mechanisms of aging will create a critical foundation from which to develop new interventions for aging-related brain disorders. Aging-related reorganization of the brain network has been described for the human brain based on functional, metabolic and structural connectivity. However, alterations in the brain metabolic network of aging rats remain unknown. Here, we submitted young and aged rats to [18F]fluorodeoxyglucose with positron emission tomography (18F-FDG PET) and constructed brain metabolic networks. The topological properties were detected, and the network robustness against random failures and targeted attacks was analyzed for age-group comparison. Compared with young rats, aged rats showed reduced betweenness centrality (

Indexed as

Fluorodeoxyglucose F18Positron-Emission TomographyAgingAnimalsBrainMetabolic Networks and PathwaysRatsFluorodeoxyglucose F18agingbrain metabolic networknetwork robustnessPETtopological property

Identifiers

PMID35077393
PMCPMC8833125
OpenAlexW4206948760

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.