Big Data Approaches for the Analysis of Large-Scale fMRI Data Using Apache Spark and GPU Processing: A Demonstration on Resting-State fMRI Data from the Human Connectome Project

Roland N Boubela, Klaudius Kalcher, Wolfgang Huf, Christian Našel, Ewald Moser

Research output: Journal article (peer-reviewed)Journal article

28 Citations (Scopus)

Abstract

Technologies for scalable analysis of very large datasets have emerged in the domain of internet computing, but are still rarely used in neuroimaging despite the existence of data and research questions in need of efficient computation tools especially in fMRI. In this work, we present software tools for the application of Apache Spark and Graphics Processing Units (GPUs) to neuroimaging datasets, in particular providing distributed file input for 4D NIfTI fMRI datasets in Scala for use in an Apache Spark environment. Examples for using this Big Data platform in graph analysis of fMRI datasets are shown to illustrate how processing pipelines employing it can be developed. With more tools for the convenient integration of neuroimaging file formats and typical processing steps, big data technologies could find wider endorsement in the community, leading to a range of potentially useful applications especially in view of the current collaborative creation of a wealth of large data repositories including thousands of individual fMRI datasets.

Original languageEnglish
Article number492
Pages (from-to)492
JournalFrontiers in Neuroscience
Volume9
DOIs
Publication statusPublished - 2016

Keywords

  • Apache Spark
  • Big data analytics
  • Distributed computing
  • Graph analysis
  • Machine learning
  • Scalable architecture
  • Statistical computing
  • fMRI

ASJC Scopus subject areas

  • Neuroscience (all)

Fingerprint

Dive into the research topics of 'Big Data Approaches for the Analysis of Large-Scale fMRI Data Using Apache Spark and GPU Processing: A Demonstration on Resting-State fMRI Data from the Human Connectome Project'. Together they form a unique fingerprint.

Cite this