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Adaptively and Spatially Estimating the Hemodynamic Response Functions in fMRI

  • Jiaping Wang
  • Hongtu Zhu
  • Jianqing Fan
  • Kelly Giovanello
  • Weili Lin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6892)

Abstract

In an event-related functional MRI data analysis, an accurate and robust extraction of the hemodynamic response function (HRF) and its associated statistics (e.g., magnitude, width, and time to peak) is critical to infer quantitative information about the relative timing of the neuronal events in different brain regions. The aim of this paper is to develop a multiscale adaptive smoothing model (MASM) to accurately estimate HRFs pertaining to each stimulus sequence across all voxels. MASM explicitly accounts for both spatial and temporal smoothness information, while incorporating such information to adaptively estimate HRFs in the frequency domain. One simulation study and a real data set are used to demonstrate the methodology and examine its finite sample performance in HRF estimation, which confirms that MASM significantly outperforms the existing methods including the smooth finite impulse response model, the inverse logit model and the canonical HRF.

Keywords

Statistical Parametric Mapping Stimulus Sequence Hemodynamic Response Function Linear Time Invariant fMRI Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jiaping Wang
    • 1
  • Hongtu Zhu
    • 1
    • 4
  • Jianqing Fan
    • 5
  • Kelly Giovanello
    • 3
    • 4
  • Weili Lin
    • 2
    • 4
  1. 1.Department of BiostatisticsUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.Department of RadiologyUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.Department of PsychologyUniversity of North Carolina at Chapel HillChapel HillUSA
  4. 4.Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillChapel HillUSA
  5. 5.Department of Operations Research & Financial EngineeringPrinceton UniversityPrincetonUSA

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