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IPM
30
YEARS OLD

“School of Cognitive Sciences”

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Paper   IPM / Cognitive Sciences / 9545
School of Cognitive Sciences
  Title:   Nonparametric trend estimation in the presence of fractal noise: Application to fMRI time-series analysis
  Author(s): 
1.  Babak Afshinpour
2.  Gholam-Ali Hossein-Zadeh
3.  Hamid Soltanian-Zadeh
  Status:   Published
  Journal: Journal of Neuroscience Methods
  Vol.:  171
  Year:  2008
  Pages:   340-348
  Supported by:  IPM
  Abstract:
Unknown low frequency fluctuations called ?trend? are observed in noisy time-series measured for different applications. In some disciplines, they carry primary information while in other fields such as functional magnetic resonance imaging (fMRI) they carry nuisance effects. In all cases, however, it is necessary to estimate them accurately. In this paper, a method for estimating trend in the presence of fractal noise is proposed and applied to fMRI time-series. To this end, a partly linear model (PLM) is fitted to each time-series. The parametric and nonparametric parts of PLM are considered as contributions of hemodynamic response and trend, respectively. Using the whitening property ofwavelet transform, the unknown components of the model are estimated in the wavelet domain. The results of the proposed method are compared to those of other parametric trend-removal approaches such as spline and polynomial models. It is shown that the proposed method improves activation detection and decreases variance of the estimated parameters relative to the other methods.

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