Evidence map›Paper›PMID 27242495›Full record

ArticleFrontiers in human neuroscience2016

Combining Partial Directed Coherence and Graph Theory to Analyse Effective Brain Networks of Different Mental Tasks.

Dengfeng Huang, Aifeng Ren, Jing Shang, Qiao Lei, Yun Zhang, Zhongliang Yin, Jun Li, Karen M von Deneen, Liyu Huang

Abstract read
In one paragraph

Article in Frontiers in human neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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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

9 authors.

Dengfeng HuangSchool of Life Science and Technology, Xidian UniversityXi'an, China; Department of Radiology, Medical Physics, University Medical Center FreiburgFreiburg, Germany.
Aifeng RenSchool of Electronic Engineering, Xidian University Xi'an, China.
Jing ShangSchool of Electronic Engineering, Xidian University Xi'an, China.
Qiao LeiSchool of Life Science and Technology, Xidian University Xi'an, China.
Yun ZhangSchool of Life Science and Technology, Xidian University Xi'an, China.
Zhongliang YinSchool of Life Science and Technology, Xidian University Xi'an, China.
Jun LiSchool of Life Science and Technology, Xidian University Xi'an, China.
Karen M von DeneenSchool of Life Science and Technology, Xidian University Xi'an, China.
Liyu HuangSchool of Life Science and Technology, Xidian University Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe aim of this study is to qualify the network properties of the brain networks between two different mental tasks (play task or rest task) in a healthy population. METHODS AND MATERIALS: EEG signals were recorded from 19 healthy subjects when performing different mental tasks. Partial directed coherence (PDC) analysis, based on Granger causality (GC), was used to assess the effective brain networks during the different mental tasks. Moreover, the network measures, including degree, degree distribution, local and global efficiency in delta, theta, alpha, and beta rhythms were calculated and analyzed.

resultsThe local efficiency is higher in the beta frequency and lower in the theta frequency during play task whereas the global efficiency is higher in the theta frequency and lower in the beta frequency in the rest task. SIGNIFICANCE: This study reveals the network measures during different mental states and efficiency measures may be used as characteristic quantities for improvement in attentional performance.

Indexed as

effective networkselectroencephalogram (EEG)graph theorymental taskspartial directed coherence

Identifiers

PMID27242495
PMCPMC4873517

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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.