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.
Highlights
Human brain regions are activated in response to external stimuli or in carrying out cognitive tasks
As the time required for such computation increases geometrically with the number of the path coefficients, only a few brain regions were considered in the previous studies
This study demonstrates the powerful application of hierarchical parallel genetic simulated annealing (HP-GSA) under Graphical Processing Unit (GPU) in accurately analyzing Functional Magnetic Resonance Imaging (fMRI) data with much lower computational load
Summary
Human brain regions are activated in response to external stimuli or in carrying out cognitive tasks. With a fixed estimate of correlation matrix, the “best” path model is characterized by path coefficients that minimize the ML discrepancy function. The minimization process is performed iteratively where the number of unconstrained path coefficients increases after each constrained minimization of ML discrepancy function (Bullmore et al, 2000).
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