Statistical analysis of functional MRI data in the wavelet domain

Urs E. Ruttimann, Michael Unser, Robert R. Rawlings, Daniel Rio, Nick F. Ramsey, Venkata S. Mattay, Daniel W. Hommer, Joseph A. Frank, Daniel R. Weinberger

Research output: Contribution to journalArticlepeer-review

116 Scopus citations


The use of the wavelet transform is explored for the detection of differences between brain functional magnetic resonance images (fMRI's) acquired under two different experimental conditions. The method benefits from the fact that a smooth and spatially localized signal can be represented by a small set of localized wavelet coefficients, while the power of white noise is uniformly spread throughout the wavelet space. Hence, a statistical procedure is developed that uses the imposed decomposition orthogonality to locate wavelet-space partitions with large signal-to-noise ratio (SNR), and subsequently restricts the testing for significant wavelet coefficients to these partitions. This results in a higher SNR and a smaller number of statistical tests, yielding a lower detection threshold compared to spatial-domain testing and, thus, a higher detection sensitivity without increasing type I errors. The multiresolution approach of the wavelet method is particularly suited to applications where the signal bandwidth and/or the characteristics of an imaging modality cannot be well specified. The proposed method was applied to compare two different fMRI acquisition modalities. Differences of the respective useful signal bandwidths could be clearly demonstrated; the estimated signal, due to the smoothness of the wavelet representation, yielded more compact regions of neuroactivity than standard spatial-domain testing.

Original languageEnglish (US)
Pages (from-to)142-154
Number of pages13
JournalIEEE transactions on medical imaging
Issue number2
StatePublished - 1998
Externally publishedYes


  • Functional magnetic resonance imaging
  • Multiresolution analysis
  • Statistical models
  • Wavelet transform

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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