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|Title:||Genetic algorithm supported by graphical processing unit improves the exploration of effective connectivity in functional brain imaging||Authors:||Chan, LWC
Graphical processing unit
Magnetic resonance imaging
Structural equation modeling
|Issue Date:||5-May-2015||Publisher:||Frontiers Research Foundation||Source:||Frontiers in computational neuroscience, 5 May 2015, v. 9, 50, p. 1-8 How to cite?||Journal:||Frontiers in computational neuroscience||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.||URI:||http://hdl.handle.net/10397/7531||EISSN:||1662-5188||DOI:||10.3389/fncom.2015.00050||Rights:||Copyright © 2015 Chan, Pang, Shyu, Chan and Khong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
The following publication Chan LWC, Pang B, Shyu C-R, Chan T and Khong P-L (2015) Genetic algorithm supported by graphical processing unit improves the exploration of effective connectivity in functional brain imaging. Front. Comput. Neurosci. 9:50,1-8 is available at https://dx.doi.org/10.3389/fncom.2015.00050
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Citations as of May 6, 2020
Citations as of May 6, 2020
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