Research Article

A comparative study of one-level and two-level semiparametric estimation of hemodynamic response function for fMRI data

  1. C. M. Zhang*,
  2. Y. Jiang,
  3. T. Yu

Article first published online: 5 JUN 2007

DOI: 10.1002/sim.2936

Issue

Statistics in Medicine

Statistics in Medicine

Special Issue: Statistical Analysis of Neuronal Data (SAND3)

Volume 26, Issue 21, pages 3845–3861, 20 September 2007

Additional Information

How to Cite

Zhang, C. M., Jiang, Y. and Yu, T. (2007), A comparative study of one-level and two-level semiparametric estimation of hemodynamic response function for fMRI data. Statistics in Medicine, 26: 3845–3861. doi: 10.1002/sim.2936

Author Information

  1. Department of Statistics, University of Wisconsin-Madison, 1300 University Avenue, Madison, WI 53706, U.S.A.

*Department of Statistics, University of Wisconsin, 1300 University Avenue, Madison, WI 53706, U.S.A.

Publication History

  1. Issue published online: 14 AUG 2007
  2. Article first published online: 5 JUN 2007
  3. Manuscript Accepted: 19 APR 2007
  4. Manuscript Received: 18 APR 2007

Funded by

  • Wisconsin Alumni Research Foundation
  • National Science Foundation. Grant Numbers: DMS-03-53941, DMS-07-05209

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Keywords:

  • false discovery rate;
  • multiple comparison;
  • semiparametric test;
  • smoothing splines;
  • stimuli;
  • time resolution

Abstract

Functional magnetic resonance imaging (fMRI) is emerging as a powerful tool for studying the process underlying the working of the many regions of the human brain. The standard tool for analyzing fMRI data is some variant of the linear model, which is restrictive in modeling assumptions. In this paper, we develop a semiparametric approach, based on the cubic smoothing splines, to obtain statistically more efficient estimates of the underlying hemodynamic response function (HRF) associated with fMRI experiments. The hypothesis testing of HRF is conducted to identify the brain regions which are activated when a subject performs a particular task. Furthermore, we compare one-level and two-level semiparametric estimates of HRF in significance tests for detecting the activated brain regions. Our simulation studies demonstrate that the one-level estimates combined with a bias-correction procedure perform best in detecting the activated brain regions. We illustrate this method using a real fMRI data set and compare it with popular methods offered by AFNI and FSL. Copyright © 2007 John Wiley & Sons, Ltd.

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