Seed-based Correlation Analysis (SCA) of fMRI data has been used to create brain connectivity networks. With close to a million unique voxels in a fMRI dataset, the number of calculations involved in SCA becomes high. With the emergence of the dynamic functional connectivity analysis, and the studies relying on real-time neurological feedback, the need for rapid processing methods becomes even more critical. This work aims to develop a new approach which produces high-resolution brain connectivity maps rapidly. Preliminary results show that this process can improve processing by a factor of 27 or more over that of a conventional PC workstation.
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