Implementation of High-Performance Correlation and Mapping Engine for Rapid Generation of Brain Connectivity Networks from Big fMRI Data. Academic Article

abstract

• 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. Seed-based Correlation Analysis (SCA) of fMRI data has been used to create brain connectivity networks. With close to a million voxels in a fMRI dataset, the number of calculations involved in SCA becomes high. This work aims to demonstrate a new approach which produces high-resolution brain connectivity maps rapidly. The results show that HPCME with four FPGAs can improve the SCA processing speed by a factor of 40 or more over that of a PC workstation with a multicore CPU.

authors

• Ji, Jim
• Orr, Joseph

published proceedings

• Annu Int Conf IEEE Eng Med Biol Soc

author list (cited authors)

• Lusher, J. D., Ji, J. X., & Orr, J. M.

citation count

• 4

complete list of authors

• Lusher, John D||Ji, Jim X||Orr, Joseph M

publication date

• July 2018

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference  Journal

keywords

• Brain
• Brain Mapping
• Magnetic Resonance Imaging

PubMed Central ID

• 30440567

Digital Object Identifier (DOI)

• 10.1109/EMBC.2018.8512413

start page

• 1032

end page

• 1036

volume

• 2018

URL

• http://dx.doi.org/10.1109/embc.2018.8512413