ICA-fNORM: Spatial Normalization of fMRI Data Using Intrinsic Group-ICA Networks.

A common pre-processing challenge associated with group level fMRI analysis is spatial registration of multiple subjects to a standard space. Spatial normalization, using a reference image such as the Montreal Neurological Institute brain template, is the most common technique currently in use to achieve spatial congruence across multiple subjects. This method corrects for global shape differences preserving regional asymmetries, but does not account for functional differences. We propose a novel approach to co-register task-based fMRI data using resting state group-ICA networks. We posit that these intrinsic networks (INs) can provide to the spatial normalization process with important information about how each individual's brain is organized functionally. The algorithm is initiated by the extraction of single subject representations of INs using group level independent component analysis (ICA) on resting state fMRI data. In this proof-of-concept work two of the robust, commonly identified, networks are chosen as functional templates. As an estimation step, the relevant INs are utilized to derive a set of normalization parameters for each subject. Finally, the normalization parameters are applied individually to a different set of fMRI data acquired while the subjects performed an auditory oddball task. These normalization parameters, although derived using rest data, generalize successfully to data obtained with a cognitive paradigm for each subject. The improvement in results is verified using two widely applied fMRI analysis methods: the general linear model and ICA. Resulting activation patterns from each analysis method show significant improvements in terms of detection sensitivity and statistical significance at the group level. The results presented in this article provide initial evidence to show that common functional domains from the resting state brain may be used to improve the group statistics of task-fMRI data.