Multiscale adaptive smoothing models for the hemodynamic response function in fMRI

Jiaping Wang, Hongtu Zhu, Jianqing Fan, Kelly Giovanello, Weili Lin

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

In the event-related functional magnetic resonance imaging (fMRI) data analysis, there is an extensive interest in accurately and robustly estimating the hemodynamic response function (HRF) and its associated statistics (e.g., the magnitude and duration of the activation). Most methods to date are developed in the time domain and they have utilized almost exclusively the temporal information of fMRI data without accounting for the spatial information. The aim of this paper is to develop a multiscale adaptive smoothing model (MASM) in the frequency domain by integrating the spatial and frequency information to adaptively and accurately estimate HRFs pertaining to each stimulus sequence across all voxels in a three-dimensional (3D) volume. We use two sets of simulation studies and a real data set to examine the finite sample performance of MASM in estimating HRFs. Our real and simulated data analyses confirm that MASM outperforms several other state-of-the-art methods, such as the smooth finite impulse response (sFIR) model.

Original languageEnglish (US)
Pages (from-to)904-935
Number of pages32
JournalAnnals of Applied Statistics
Volume7
Issue number2
DOIs
StatePublished - Jun 2013

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

Keywords

  • Frequency domain
  • Functional magnetic resonance imaging
  • Multiscale adaptive smoothing model
  • Weighted least square estimate

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