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

Overview of attention for article published in Frontiers in Human Neuroscience, May 2016
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Title
Combining Partial Directed Coherence and Graph Theory to Analyse Effective Brain Networks of Different Mental Tasks
Published in
Frontiers in Human Neuroscience, May 2016
DOI 10.3389/fnhum.2016.00235
Pubmed ID
Authors

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

Abstract

The 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. 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. The 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. This study reveals the network measures during different mental states and efficiency measures may be used as characteristic quantities for improvement in attentional performance.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 1%
Unknown 89 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 29%
Researcher 11 12%
Student > Master 10 11%
Student > Doctoral Student 6 7%
Student > Bachelor 4 4%
Other 14 16%
Unknown 19 21%
Readers by discipline Count As %
Engineering 24 27%
Neuroscience 14 16%
Computer Science 10 11%
Psychology 8 9%
Medicine and Dentistry 5 6%
Other 7 8%
Unknown 22 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 June 2016.
All research outputs
#15,166,320
of 23,325,355 outputs
Outputs from Frontiers in Human Neuroscience
#4,963
of 7,266 outputs
Outputs of similar age
#199,910
of 334,769 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#147
of 190 outputs
Altmetric has tracked 23,325,355 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,266 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.6. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 334,769 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 190 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.