Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/7531
Title: Genetic algorithm supported by graphical processing unit improves the exploration of effective connectivity in functional brain imaging
Authors: Chan, LWC 
Pang, B
Shyu, CR
Chan, T
Khong, PL
Keywords: Effective connectivity
Genetic algorithms
Graphical processing unit
Magnetic resonance imaging
Neuronal circuitry
Path model
Structural equation modeling
Issue Date: 2015
Publisher: Frontiers Research Foundation
Source: Frontiers in computational neuroscience, 2015, v. 9, 50 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
Appears in Collections:Journal/Magazine Article

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