Meeting Banner
Abstract #3684

Wavelet Shrinkage Versus Gaussian Spatial Filtering of Functional MRI Data

Ruxandra Mutihac1, Radu Mutihac2

1Experimental Psychology, University of Oxford, Oxford, UK; 2Electricity and Biophysics, University of Bucharest, Bucharest, Romania

Wavelets provide orthonormal bases for multiresolution analysis and decorrelation of nonstationary, scaling, scale-invariant, and fractal processes in time, space, or both, which is the case in neuroimaging. Scale-varying wavelet-based methods for hypothesis testing of brain activation maps circumvent the need to specify a priori the size of signals expected and, therefore, the optimal choice of smoothing kernel required by Gaussian filtering. Wavelet-based methods are likely to provide an overall richer characterization of distributed brain activation. Discrete wavelet transform also exhibits decorrelating properties, which amounts to mutually independence of the hypothesis tests on the wavelet coefficients and yields potential benefits in the optimal control of false positives.