A new mathematical approach for detection of active area in human brain fMRI using nonlinear model

Taalimi, A ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.4015/S1016237210002171
  3. Abstract:
  4. Functional magnetic resonance imaging (fMRI) is widely-used for detection of the brain's neural activity. The signals and images acquired through this imaging technique demonstrate the human brain's response to pre-scheduled tasks. Several studies on blood oxygenation level-dependent (BOLD) signal responses demonstrate nonlinear behavior in response to a stimulus. In this paper we propose a new mathematical approach for modeling BOLD signal activity, which is able to model nonlinear and time variant behaviors of this physiological system. We employ the Nonlinear Auto Regressive Moving Average (NARMA) model to describe the mathematical relationship between output signals and predesigned tasks. The model parameters can be used to distinguish between rest and active states of a brain region. We applied our proposed method for active region detection on real as well as simulated data sets. The results show superior performance in comparison with existing methods
  5. Keywords:
  6. Activation detection ; Functional magnetic resonance imaging ; Nonlinear auto regressive model ; Active area ; Active region detections ; Active state ; Auto regressive models ; Blood oxygenation ; Brain regions ; Existing method ; Human brain ; Mathematical approach ; Mathematical relationship ; Model parameters ; Neural activity ; Non-linear model ; Nonlinear auto-regressive moving averages ; Nonlinear behavior ; Output signal ; Physiological systems ; Signal activity ; Signal response ; Simulated datasets ; Time variant ; Brain ; Magnetic resonance imaging ; Oxygenation ; Physiological models ; Resonance ; Signal detection ; Mathematical models ; Article ; Brain blood flow ; Brain region ; Human ; Image analysis ; Mathematical model ; Nerve cell network ; Neuroimaging ; Nonlinear system ; Signal detection ; Stimulus response ; Task performance
  7. Source: Biomedical Engineering - Applications, Basis and Communications ; Volume 22, Issue 5 , 2010 , Pages 409-418 ; 10162372 (ISSN)
  8. URL: http://www.worldscientific.com/doi/abs/10.4015/S1016237210002171