Date of Original Version




Abstract or Description

We present an approach to integrate multiple fMRI datasets in the context of predictive fMRI data analysis. The approach utilizes canonical correlation analysis (CCA) to find common dimensions among the different datasets, and it does not require that the multiple fMRI datasets be spatially normalized. We apply the approach to the task of predicting brain activations for unseen concrete-noun words using multiple-subject datasets from two related fMRI studies. The proposed approach yields better prediction accuracies than those of an approach where each subject’s data is analyzed separately.



Published In

Proceedings of the MICCAI 2009 Workshop: Statistical modeling and detection issues in intra- and inter-subject functional MRI data analysis.