Evidence map›Paper›PMID 38855440›Full record

ArticleFrontiers in neuroscience2024

Rich-club organization of whole-brain spatio-temporal multilayer functional connectivity networks.

Jianhui Zheng, Yuhao Cheng, Xi Wu, Xiaojie Li, Ying Fu, Zhipeng Yang

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2024. 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
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jianhui ZhengCollege of Electronic Engineering, Chengdu University of Information Technology, Chengdu, China.
Yuhao ChengHuaxi Molecular Imaging Research Laboratory, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Xi WuDepartment of Computer Science, Chengdu University of Information Technology, Chengdu, China.
Xiaojie LiDepartment of Computer Science, Chengdu University of Information Technology, Chengdu, China.
Ying FuDepartment of Computer Science, Chengdu University of Information Technology, Chengdu, China.
Zhipeng YangCollege of Electronic Engineering, Chengdu University of Information Technology, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: In this work, we propose a novel method for constructing whole-brain spatio-temporal multilayer functional connectivity networks (FCNs) and four innovative rich-club metrics. Methods: Spatio-temporal multilayer FCNs achieve a high-order representation of the spatio-temporal dynamic characteristics of brain networks by combining the sliding time window method with graph theory and hypergraph theory. The four proposed rich-club scales are based on the dynamic changes in rich-club node identity, providing a parameterized description of the topological dynamic characteristics of brain networks from both temporal and spatial perspectives. The proposed method was validated in three independent differential analysis experiments: male-female gender difference analysis, analysis of abnormality in patients with autism spectrum disorders (ASD), and individual difference analysis. Results: The proposed method yielded results consistent with previous relevant studies and revealed some innovative findings. For instance, the dynamic topological characteristics of specific white matter regions effectively reflected individual differences. The increased abnormality in internal functional connectivity within the basal ganglia may be a contributing factor to the occurrence of repetitive or restrictive behaviors in ASD patients. Conclusion: The proposed methodology provides an efficacious approach for constructing whole-brain spatio-temporal multilayer FCNs and conducting analysis of their dynamic topological structures. The dynamic topological characteristics of spatio-temporal multilayer FCNs may offer new insights into physiological variations and pathological abnormalities in neuroscience.

Indexed as

functional connectivity networkfunctional MRIintraclass correlation coefficientrich-clubstatistical significance

Identifiers

PMID38855440
PMCPMC11157044

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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.