Adaptively and spatially estimating the hemodynamic response functions in fMRI

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

Research output: Contribution to journalConference articlepeer-review

7 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)269-276
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6892 LNCS
Issue numberPART 2
StatePublished - 2011
Externally publishedYes
Event14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
Duration: Sep 18 2011Sep 22 2011

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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