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Genetic algorithm supported by graphical processing unit improves the exploration of effective connectivity in functional brain imaging

Overview of attention for article published in Frontiers in Computational Neuroscience, May 2015
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Title
Genetic algorithm supported by graphical processing unit improves the exploration of effective connectivity in functional brain imaging
Published in
Frontiers in Computational Neuroscience, May 2015
DOI 10.3389/fncom.2015.00050
Pubmed ID
Authors

Lawrence Wing Chi Chan, Bin Pang, Chi-Ren Shyu, Tao Chan, Pek-Lan Khong

Abstract

Brain regions of human subjects exhibit certain levels of associated activation upon specific environmental stimuli. Functional Magnetic Resonance Imaging (fMRI) detects regional signals, based on which we could infer the direct or indirect neuronal connectivity between the regions. Structural Equation Modeling (SEM) is an appropriate mathematical approach for analyzing the effective connectivity using fMRI data. A maximum likelihood (ML) discrepancy function is minimized against some constrained coefficients of a path model. The minimization is an iterative process. The computing time is very long as the number of iterations increases geometrically with the number of path coefficients. Using regular Quad-Core Central Processing Unit (CPU) platform, duration up to 3 months is required for the iterations from 0 to 30 path coefficients. This study demonstrates the application of Graphical Processing Unit (GPU) with the parallel Genetic Algorithm (GA) that replaces the Powell minimization in the standard program code of the analysis software package. It was found in the same example that GA under GPU reduced the duration to 20 h and provided more accurate solution when compared with standard program code under CPU.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 3 27%
Student > Ph. D. Student 2 18%
Other 2 18%
Researcher 1 9%
Unknown 3 27%
Readers by discipline Count As %
Unspecified 3 27%
Engineering 2 18%
Computer Science 1 9%
Neuroscience 1 9%
Social Sciences 1 9%
Other 0 0%
Unknown 3 27%
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 12 June 2015.
All research outputs
#16,721,717
of 25,373,627 outputs
Outputs from Frontiers in Computational Neuroscience
#806
of 1,463 outputs
Outputs of similar age
#159,780
of 279,206 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#20
of 36 outputs
Altmetric has tracked 25,373,627 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 1,463 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one is in the 39th percentile – i.e., 39% 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 279,206 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.