Functional magnetic resonance imaging (fMRI) is a fast-developing technique for studying physiological processes in the brains of conscious human subjects. Changes in neuronal activity can be measured by an MR scanner. Volumes acquired for functional analysis have a rather poor signal-to-noise ratio and suffer from several other degradations (e.g., movements artifacts). SPM (Statistical Parametric Map) incorporates many of the algorithms necessary for neurologists to analyze their data properly. Our group has proposed wavelets-based approaches.
Recently, we have developed a new integrated framework for statistical testing of fMRI data using wavelets. This promising approach has been implemented into a "Wavelets Toolbox" for SPM.
Our technique has many degrees of freedom which deserves to be explored. The aim of this project is to establish a protocol to properly evaluate the performance. Secondly, as function of the evaluation results, the toolbox can also be extended using complex-valued wavelets.