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

• Published in: Frontiers in computational neuroscience Vol. 9; p. 50
• Main Authors: Chan, Lawrence Wing Chi, Pang, Bin, Shyu, Chi-Ren, Chan, Tao, Khong, Pek-Lan
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 05.05.2015; Frontiers Media S.A
• Subjects: Algorithms; Bilingualism; Bioinformatics; Brain; Brain mapping; effective connectivity; Environmental effects; Functional magnetic resonance imaging; Genetic algorithms; Magnetic Resonance Imaging; Medical imaging; Neural networks; Neuroimaging; neuronal circuitry; Neuroscience; Path model; Principal components analysis; Software; Structural Equation Modeling; Time series; Hong Kong China; China; neuronal circuitry; path model; magnetic resonance imaging; structural equation modeling; graphical processing unit; effective connectivity; genetic algorithms
• ISSN: 1662-5188; 1662-5188
• DOI: 10.3389/fncom.2015.00050

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.