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Data-driven haemodynamic response function extraction using Fourier-wavelet regularised deconvolution.

Alle Meije Wink1, Hans Hoogduin, Jos B T M Roerdink

  • 1Robert Steiner MR Unit, Imaging Sciences Department Imperial College, and MRC Clinical Sciences Centre, Hammersmith Hospital, London, UK. amwink@imperial.ac.uk

BMC Medical Imaging
|April 12, 2008
PubMed
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A new method called Fourier-wavelet regularised deconvolution (ForWaRD) efficiently extracts haemodynamic response functions (HRFs) from fMRI data. Using subject-specific HRFs improves the detection power and accuracy of fMRI analyses.

Area of Science:

  • Neuroimaging
  • Signal Processing

Background:

  • Functional magnetic resonance imaging (fMRI) requires accurate haemodynamic response function (HRF) data for various applications.
  • Extracting subject-specific and region-specific HRFs is crucial for detailed fMRI analysis.
  • Existing methods for HRF extraction can be complex and computationally intensive.

Purpose of the Study:

  • To present a simple, data-driven method for extracting HRFs from fMRI time series.
  • To introduce the Fourier-wavelet regularised deconvolution (ForWaRD) technique for HRF extraction.
  • To demonstrate the utility of subject-specific HRFs in enhancing fMRI analysis.

Main Methods:

  • The ForWaRD technique utilizes wavelet-based methods to remove low-frequency trends from fMRI time signals.
  • HRF data are extracted for each voxel, resulting in a time series of volumes.

Related Experiment Videos

  • The method assumes separability of signal and noise in frequency and wavelet domains, and employs the general linear model.

    • Main Results:

    • ForWaRD is a fast HRF extraction algorithm, comparable in speed to spatial resampling.
    • The method was tested on simulated event-related activation signals corrupted with real MRI noise.
    • A continuous-time HRF was obtained by fitting a nonlinear function to discrete coefficients.

    Conclusions:

    • The ForWaRD extraction method is robust to variations in signal properties.
    • Subject-specific, regional HRFs significantly enhance detection power in fMRI analyses compared to canonical HRFs.
    • Improved sensitivity and specificity are observed not only in the HRF extraction region but also in other regions of interest.