Evidence map›Paper›PMID 32695269›Full record

ReviewComputational and structural biotechnology journal2020

Rich-club in the brain's macrostructure: Insights from graph theoretical analysis.

Dae-Jin Kim, Byoung-Kyong Min

Open access · goldAbstract readReview
In one paragraph

Review in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
5.6field-weighted citation impact, top 3% 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

30 citing papers in PubMed, 72 citations in OpenAlex.

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  11. Network Statistics of the Whole-Brain Connectome ofbioRxiv : the preprint server for biology · 2024
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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

2 authors at 2 institutions in 2 countries.

Dae-Jin KimDepartment of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA.
Byoung-Kyong MinDepartment of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
Indiana University Bloomington · USKorea University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The brain is a complex network. Growing evidence supports the critical roles of a set of brain regions within the brain network, known as the brain's cores or hubs. These regions require high energy cost but possess highly efficient neural information transfer in the brain's network and are termed the rich-club. The rich-club of the brain network is essential as it directly regulates functional integration across multiple segregated regions and helps to optimize cognitive processes. Here, we review the recent advances in rich-club organization to address the fundamental roles of the rich-club in the brain and discuss how these core brain regions affect brain development and disorders. We describe the concepts of the rich-club behind network construction in the brain using graph theoretical analysis. We also highlight novel insights based on animal studies related to the rich-club and illustrate how human studies using neuroimaging techniques for brain development and psychiatric/neurological disorders may be relevant to the rich-club phenomenon in the brain network.

Indexed as

AD, Alzheimer’s diseaseADHD, attention deficit hyperactivity disorderASD, autism spectrum disorderBD, bipolar disorderBrain connectivityBrain networkDTI, diffusion tensor imagingEEG, electroencephalographyGraph theoryMDD, major depressive disorderMEG, magnetoencephalographyMRI, magnetic resonance imagingNeuroimagingRich-clubTBI, traumatic brain injury

Identifiers

PMID32695269
PMCPMC7355726
OpenAlexW3039918054

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.