A Distributed Computing Platform for fMRI Big Data Analytics.

Journal: IEEE transactions on big data
Published Date: Mar 6, 2018

Abstract

Since the BRAIN Initiative and Human Brain Project began, a few efforts have been made to address the computational challenges of neuroscience Big Data. The promises of these two projects were to model the complex interaction of brain and behavior and to understand and diagnose brain diseases by collecting and analyzing large quanitites of data. Archiving, analyzing, and sharing the growing neuroimaging datasets posed major challenges. New computational methods and technologies have emerged in the domain of Big Data but have not been fully adapted for use in neuroimaging. In this work, we introduce the current challenges of neuroimaging in a big data context. We review our efforts toward creating a data management system to organize the large-scale fMRI datasets, and present our novel algorithms/methods for the distributed fMRI data processing that employs Hadoop and Spark. Finally, we demonstrate the significant performance gains of our algorithms/methods to perform distributed dictionary learning.

Authors

  • Milad Makkie
    Computer Science Department, University of Georgia, Athens, GA, United States.
  • Xiang Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • Shannon Quinn
    Department of Computer Science, University of Georgia, Athens, GA 30602.
  • Binbin Lin
    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Ml 48109.
  • Jieping Ye
    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Ml 48109.
  • Geoffrey Mon
    Department of Computer Science, University of Georgia, Athens, GA 30602.
  • Tianming Liu
    School of Computing, University of Georgia, Athens, GA, United States.

Keywords

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