oa RAFNI: Robust analysis of functional neuroimages with non-normal alpha-stable errors

One of the most ambitious goals of Qatar in the next few years is to become a country based on scientific and technical researches instead of being dependent on hydrocarbons. To this end, Qatar Foundation has established a number of high caliber universities and institutes. In particular, Qatar Computing Research Institue (QCRI) is forming a scientific computing multidisciplinary group with a focus on data mining machine learning, statistical modeling and bioinformatics. We now are able to satisfy the computational statistics needs of a variety of fields, especially of biomedical researchers in Qatar. Functional magnetic resonance imaging (fMRI) is a noninvasive neuroimaging method that is widely used in cognitive neuroscience. It relies on the measurement of changes in the blood oxygenation level resulting from neural activity. The technique is widely used in cognitive neuroscience. fMRI is known to be contaminated by artifacts. Artifacts are known to have fat tailed distributions and are often skewed therefore modeling the error using a Gaussian distribution is not enough. In this paper we introduce RAFNI, an extension of AFNI, which is an fMRI open source software for the analysis of functional neuroimages. We are model the error introduced by artifacts using alpha-stable distribution. We demonstrate the applicability and efficiency of stable distributions on real fMRI. We show that the alpha-stable estimator gives better results than the OLS-based estimators.