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

Wing Chi Chan, Bin Pang, Chi Ren Shyu, Tao Chan, Pek Lan Khong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

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.
Original languageEnglish
Article number50
JournalFrontiers in Computational Neuroscience
Volume9
Issue numberMAY
DOIs
Publication statusPublished - 5 May 2015

Keywords

  • Effective connectivity
  • Genetic algorithms
  • Graphical processing unit
  • Magnetic resonance imaging
  • Neuronal circuitry
  • Path model
  • Structural equation modeling

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Cellular and Molecular Neuroscience

Cite this