Evidence map›Paper›PMID 35072833›Full record

ArticleBrain topography2022

Examining the Effects of Normal Ageing on Cortical Connectivity of Older Adults.

Muhammad Aamir Panhwar, Muhammad Mohsin Pathan, Nasrullah Pirzada, Muhammad Aashed Khan Abbasi, Deng ZhongLiang, Ghazala Panhwar

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Article in Brain topography, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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.

Muhammad Aamir PanhwarSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China. maamirpanhwar@gmail.com.
Muhammad Mohsin PathanInstitute of Biomedical Engineering and Technology, LUMHS, Jamshoro, Pakistan.
Nasrullah PirzadaDepartment of Telecommunication Engineering, Mehran University of Engineering & Technology, Jamshoro, Pakistan.
Muhammad Aashed Khan AbbasiCollege of Engineering, Prince Sultan University, Riyadh, Saudi Arabia.
Deng ZhongLiangSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
Ghazala PanhwarInstitute of Science, Technology and Development Jamshoro, Mehran University of Engineering & Technology, Jamshoro, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the recent advancement in computer technology, we can extract the picture of the brain as a network. The aim of this study is to constructs large scale individual anatomical brain networks using regional gray matter cortical thickness from individual subject's magnetic resonance imaging (MRI) data, as well as to investigate changes with normal aging in global network organization. The dataset includes 183 healthy subjects sMRI data with an age range from 50 to 80 plus. For all brain networks, we calculated the global network measures and nodal network measures by using network analysis toolkit GRETNA. From global network measurements we calculated small-world measurements and network efficiency measurements, from nodal measurements we calculated node clustering coefficient (CC) and node efficiency at a wide-range of threshold values. All small world measurements showed more clustering at all the given threshold values than random networks and a alike least path length, indicative of that they were "small world". To analyze the effect normal ageing on networks organization, the networks of subjects were categorized into three age groups (50s, 60s, and 70 over). The global and nodal network measurements of each group were statistically analyzed to investigate the significant difference in network organization with in age groups. Results shows that the age has no significance effect in global measurements of brain network. However, by analysis the nodal measures of brain network between age group, network nodes from brain frontal lobe and temporal lobe showed age related significant difference. The results obtained from the proposed study suggest that this network method can deliver a concise network-level picture of brain organization and be used from the outlook of composite networks to investigate inter-individual variability in brain morphology.

Indexed as

BrainMagnetic Resonance ImagingAgedAgingCluster AnalysisHumansNerve NetAnatomical brain networkCortical thicknessMRINormal ageingsMRI

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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.