Functional connectivity analysis of fMRI data using parameterized regions-of-interest

Connectivity analysis of fMRI data requires correct specification of regions-of-interest (ROIs). Selection of ROIs based on outcomes of a GLM analysis may be hindered by conservativeness of the multiple comparison correction, while selection based on brain anatomy may be biased due to inconsistent structure-to-function mapping. To alleviate these problems we propose a method to define functional ROIs without the need for a stringent multiple comparison correction. We extend a flexible framework for fMRI analysis (Activated Region Fitting, Weeda et al. 2009) to connectivity analysis of fMRI data. This method describes an entire fMRI data volume by regions of activation defined by a limited number of parameters. Therefore a less stringent multiple comparison procedure is required. The regions of activation from this analysis can be directly used to estimate functional connectivity. Simulations show that Activated Region Fitting can recover the connectivity of brain regions. An application to real data of a Go/No-Go experiment highlights the advantages of the method.