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

Clicks: 267
ID: 209857
2015
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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 three 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 hours and provided more accurate solution when compared with standard program code under CPU.
Reference Key
chan2015frontiersgenetic Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Lawrence Wing Chi Chan;Bin ePang;Chi-Ren eShyu;Tao eChan;Pek-Lan eKhong
Journal population health management
Year 2015
DOI
10.3389/fncom.2015.00050
URL
http://journal.frontiersin.org/Journal/10.3389/fncom.2015.00050/full
Keywords
Magnetic resonance imaging structural equation modeling genetic algorithms effective connectivity neuropsychiatry biological psychiatry path modelneurosciences

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