Description:Functional Magnetic Resonance Imaging
Abstract:Functional magnetic resonance imaging (fMRI) can be used to characterize brain physiological activity, usually presented as 3D volumes in function of time. The traditional approach to analyze fMRI series is based on statistical parametric mapping (SPM). In SPM it is assumed that through structured paradigms with clearly distinguishable states (usually activation/no activation paradigms related with the studied brain function) it is possible to infer related brain areas to oxyhemoglobin changes through general linear models.<BR/>Following our previous work in non-linear association studies in electroencephalogram (EEG) time series, we use a similar approach on fMRI to find time dependencies between a specific brain area and other brain regions. The extension to 3D fMRI time series analysis is straight forward through a voxel time series pair-wise association analysis. The drawback of this extension is that in 3D volumes time series, the number of pairs to analyze increases, increasing both the computational complexity and results management requirements. This is a model-free approach, without a priori assumptions. Such time series analysis imposes challenging requirements regarding computational power and medical image management.<BR/>The goal is to design and implement a GRID-enabled computational framework for demanding non-parametric Brain fMRI Analysis, to be used by researchers in brain imaging.