Evidence map›Paper›PMID 30971907›Full record

ArticleFrontiers in human neuroscience2019

Meta-Network Analysis of Structural Correlation Networks Provides Insights Into Brain Network Development.

Xiaohua Xu, Ping He, Pew-Thian Yap, Han Zhang, Jingxin Nie, Dinggang Shen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in human neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Xiaohua XuDepartment of Computer Science, Yangzhou University, Yangzhou, China.
Ping HeDepartment of Computer Science, Yangzhou University, Yangzhou, China.
Pew-Thian YapDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Han ZhangDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Jingxin NieSchool of Psychology, South China Normal University, Guangzhou, China.
Dinggang ShenDepartment of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Analysis of developmental brain networks is fundamentally important for basic developmental neuroscience. In this paper, we focus on the temporally-covarying connection patterns, called meta-networks, and develop a new mathematical model for meta-network decomposition. With the proposed model, we decompose the developmental structural correlation networks of cortical thickness into five meta-networks. Each meta-network exhibits a distinctive spatial connection pattern, and its covarying trajectory highlights the temporal contribution of the meta-network along development. Systematic analysis of the meta-networks and covarying trajectories provides insights into three important aspects of brain network development.

Indexed as

brain network developmentcortical thicknesslow rankmeta-network analysistemporal smoothness

Identifiers

PMID30971907
PMCPMC6444117

What OpenQuestion holds

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Registered trials

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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.