Non-linear Fourier time series analysis for human brain mapping by functional magnetic resonance imaging

Nicholas Lange, Scott L. Zeger

Research output: Contribution to journalArticle

Abstract

A non-linear parametric model for brain activation detection by functional magnetic resonance imaging (FMRI) is proposed. The effects of a designed temporal stimulus on the FMRI signal at each brain location in a 36 × 60 spatial grid are estimated from discrete Fourier transforms of the observed time series at each location. The frequency domain regression model accommodates unobservable and spatially varying haemodynamic response functions through their estimated convolutions with the global stimulus. This approach generalizes an existing method for human brain mapping. An experiment to detect focal cortical activation during primary visual stimulation demonstrates the usefulness of the method.

Original languageEnglish (US)
Pages (from-to)1-29
Number of pages29
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume46
Issue number1
DOIs
StatePublished - Jan 1 1997

Keywords

  • Convolution
  • Correlogram
  • Discrete Fourier transform
  • Functional neuroanatomy
  • Image analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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