Large scale fMRI parameter study on a production grid 1

Functional magnetic resonance imaging (fMRI) analysis is usually carried out with standard software packages (e.g., FSL and SPM) implementing the General Linear Model (GLM) computation. Yet, the validity of an analysis may still largely depend on the parameterization of those tools, which has, however, received little attention from researchers. In th is paper, we study the influence of three of those parameters, namely (i) the size of the spatial smoothing kernel, (ii) the hemodynamic response function delay and (iii) the degrees of freedom of the fMRI-to-anatomical scan registration. In addition, two different values of acquisition parameters (echo times) ar e compared. The study is performed on a data set of 11 subjects, sweeping a significant range of parameter s. It involves almost one CPU year and produces 1.4 Terabytes of data. Thanks to a grid deployment of the FSL FEAT application, this compute and data intensive problem can be handled and the execution time is reduced to less than a week. Results suggest optimal parameter values for detecting amygdala activation, as well as the robustness of results obtained for the difference between two studied echo times.

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