Nonparametric trend estimation in the presence of fractal noise: application to fMRI time-series analysis.
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 of wavelet 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.
Afshinpour
Babak
B
Control and Intelligent Processing Center of Excellence, Electrical and Computer Engineering Department, Faculty of Engineering, University of Tehran, Tehran, Iran. babak_afshinpoor@yahoo.com
Hossein-Zadeh
Gholam-Ali
GA
Soltanian-Zadeh
Hamid
H
D001921:Brain / Q000033:anatomy & histology / Q000098:blood supply*; D001931:Brain Mapping; D017709:Fractals; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging; D009483:Neuropsychological Tests; D009622:Noise; D010100:Oxygen / Q000097:blood; D018709:Statistics, Nonparametric; D013997:Time Factors
D010100:Oxygen