Large-scale functional MRI study on a production grid
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 this 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) are compared. The study is performed on a data set of 11 subjects, sweeping a significant range of parameters. 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 that optimal parameter values for detecting activation in the amygdalae deviate from the default typically adopted in such studies. Moreover, robust results indicate no significant difference between brain activation maps obtained with the two echo times.
production grid fmri large-scale experiment