A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fractional Splines and Wavelets
SIAM Review
Neural modeling and functional brain imaging: an overview
Neural Networks - Special issue on the global brain: imaging and modelling
Exploratory analysis and data modeling in functional neuroimaging
Theories, data analysis, and simulation models in neuroimaging: an overview
Exploratory analysis and data modeling in functional neuroimaging
Wavelet-based Rician noise removal for magnetic resonance imaging
IEEE Transactions on Image Processing
WSEAS TRANSACTIONS on SYSTEMS
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Wavelet-based analysis versus Gaussian smoothing in statistical parametric mapping (SPM) for detecting and analyzing brain activity from functional magnetic resonance imaging (fMRI) data is presented. Detection of activation in fMRI data can be performed in the wavelet domain by a coefficient-wise statistical t-test. The link between the wavelet analysis and SPM is based on two observations: (i) the low-pass analysis filter of the discrete wavelet transform (DWT) can be similarly shaped to a Gaussian filter in SPM, (ii) the subsampling scheme provides means to define the number of coefficients in the low-pass subband of the wavelet decomposition [52]. Analysis of an fMRI block-based visual stimulation paradigm was comparatively performed by wavelet analysis and statistical parametric mapping (SPM) [13] based on the Random Field Theory (RFT). The voxels were isotropic and the same general linear model (GLM) was employed in both the image space and the wavelet domain. Consequently, an equivalent spline degree for which the low-pass part of the wavelet analysis is basically equivalent to SPM can be computed. The processing of neuroimaging data in the wavelet domain was carried out by means of two different biorthogonal transforms: 3D fractional-spline wavelets and 2D+Z fractional quincunx wavelets [51], resulting in activation patterns similar to the activation maps obtained by linear regression analysis in SPM.