High-Performance Correlation and Mapping Engine for Rapid Generating Brain Connectivity Networks from Big fMRI Data
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Brain connectivity networks help physicians better understand the neurological effects of certain diseases and make improved treatment options for patients. Voxel-to-Voxel Correlation Analysis (VVCA) of functional magnetic resonance imaging (fMRI) data has been used to create the individual brain connectivity networks. However, an outstanding issue is the long processing time to generate full brain connectivity maps. With close to a million individual voxels, with each having hundreds of samples, in a typical fMRI dataset, the number of calculations involved in a voxel-byvoxel CCA becomes very high. With the emergence of the dynamic time-varying functional connectivity analysis, the population-based studies, and the studies relying on real-time neurological feedbacks, the need for rapid processing methods becomes even more critical. This research dissertation describes a new method which produces high-resolution brain connectivity maps rapidly. This new method accelerates the correlation processing by using an architecture that includes clustered FPGAs and an efficient memory pipeline, which is termed the High-Performance Correlation and Mapping Engine (HPCME). The method has been tested with various datasets from the Human Connectome Project. The results show that HPCME with four FPGAs can improve the VVCA processing speed by a factor of 40 or more over that of a PC workstation with a multicore CPU.
SubjectBrain Functional Connectivity
Seed-based Correlation Analysis
FPGA-based Parallel Computing
Human Connectome Project
Lusher II, John David (2018). High-Performance Correlation and Mapping Engine for Rapid Generating Brain Connectivity Networks from Big fMRI Data. Doctoral dissertation, Texas A & M University. Available electronically from