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Abstract #2393

High-Performance Correlation and Mapping Engine for Brain Connectivity Networks from High Resolution fMRI Data

John David Lusher II1, Jim Xiuquan Ji1, and Joseph Orr2

1Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, United States, 2Department of Psychological and Brain Sciences, Texas A&M Institute for Neuroscience, Texas A&M University, College Station, TX, United States

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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