Evidence map›Paper›PMID 31631630›Full record

ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2019

[VisConnectome: an independent and graph-theory based software for visualizing the human brain connectome].

Yifan Wang, Li Zhu, Zerui He, Weihua Yang, Ge Tian, Jiali Shen, Yanlin Luo

Abstract read
In one paragraph

Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Yifan WangSchool of Artificial Intelligence, Beijing Normal University, Beijing 100875, P.R.China.
Li ZhuSchool of Artificial Intelligence, Beijing Normal University, Beijing 100875, P.R.China.
Zerui HeSchool of Artificial Intelligence, Beijing Normal University, Beijing 100875, P.R.China.
Weihua YangSchool of Artificial Intelligence, Beijing Normal University, Beijing 100875, P.R.China.
Ge TianSchool of Artificial Intelligence, Beijing Normal University, Beijing 100875, P.R.China.
Jiali ShenSchool of Artificial Intelligence, Beijing Normal University, Beijing 100875, P.R.China.
Yanlin LuoSchool of Artificial Intelligence, Beijing Normal University, Beijing 100875, P.R.China.luoyl@bnu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As a complex system, the topology of human's brain network has an important effect on further study of brain's structural and functional mechanism. Graph theory, a kind of sophisticated analytic strategies, is widely used for analyzing complex brain networks effectively and comparing difference of topological structure alteration in normal development and pathological condition. For the purpose of using this analysis methodology efficiently, it is necessary to develop graph-based visualization software. Thus, we developed VisConnectome, which displays analysis results of the brain network friendly and intuitively. It provides an original graphical user interface (GUI) including the tool window, tool bar and innovative double slider filter, brain region bar, runs in any Windows operating system and doesn't rely on any platform such as Matlab. When importing the user-defined script file that initializes the brain network, VisConnectome abstracts the brain network to the ball-and-stick model and render it. VisConnectome allows a series of visual operations, such as identifying nodes and connection, modifying properties of nodes and connection such as color and size with the color palette and size double slider, imaging the brain regions, filtering the brain network according to its size property in a specific domain as simplification and blending with the brain surface as a context of the brain network. Through experiment and analysis, we conclude that VisConnectome is an effective visualization software with high speed and quality, which helps researchers to visualize and compare the structural and functional brain networks flexibly.

Indexed as

ConnectomeSoftwareBrainHumansblending with brain surfacebrian networkdouble slider filtergraph theoryregion imagingvisualization of brain connectome

Identifiers

PMID31631630
PMCPMC9935147

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.