title: MULTISCALE ADAPTIVE SMOOTHING MODELS FOR THE HEMODYNAMIC RESPONSE FUNCTION IN FMRI.
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 temporal 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-art methods, such as the smooth finite impulse response (sFIR) model.
authors.affiliation: University of North Carolina at Chapel Hill.
authors.forename: Jiaping
authors.initials: J
authors.lastname: Wang
authors.affiliation: University of North Carolina at Chapel Hill.
authors.forename: Hongtu
authors.initials: H
authors.lastname: Zhu
authors.affiliation: Princeton University.
authors.forename: Jianqing
authors.initials: J
authors.lastname: Fan
authors.affiliation: University of North Carolina at Chapel Hill.
authors.forename: Kelly
authors.initials: K
authors.lastname: Giovanello
authors.affiliation: University of North Carolina at Chapel Hill.
authors.forename: Weili
authors.initials: W
authors.lastname: Lin
keywords: Frequency domain; Functional magnetic resonance imaging; Multiscale adaptive smoothing model; Weighted least square estimate