Wavelets and statistical analysis of functional magnetic resonance images of the human brain.
Wavelets provide an orthonormal basis for multiresolution analysis and decorrelation or 'whitening' of nonstationary time series and spatial processes. Wavelets are particularly well suited to analysis of biological signals and images, such as human brain imaging data, which often have fractal or scale-invariant properties. We briefly define some key properties of the discrete wavelet transform (DWT) and review its applications to statistical analysis of functional magnetic resonance imaging (fMRI) data. We focus on time series resampling by 'wavestrapping' of wavelet coefficients, methods for efficient linear model estimation in the wavelet domain, and wavelet-based methods for multiple hypothesis testing, all of which are somewhat simplified by the decorrelating property of the DWT.
Brain Mapping Unit and Wolfson Brain Imaging Centre, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.
Ed
E
Bullmore
Jalal
J
Fadili
Michael
M
Breakspear
Raymond
R
Salvador
John
J
Suckling
Michael
M
Brammer
D001921:Brain / Q000033:anatomy & histology / Q000503:physiopathology*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods / Q000706:statistics & numerical data*; D015233:Models, Statistical; D012044:Regression Analysis; D013997:Time Factors