Flexible large-scale fMRI analysis: A survey

Seung Jun Kim, Vince Daniel Calhoun, Tulay Adali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Functional magnetic resonance imaging (fMRI) has provided a window into the brain with wide adoption in research and even clinical settings. Data-driven methods such as those based on latent variable models and matrix/tensor factorizations are being increasingly used for fMRI data analysis. There is increasing availability of large-scale multi-subject repositories involving 1,000+ individuals. Studies with large numbers of data sets promise effective comparisons across different conditions, groups, and time points, further increasing the utility of fMRI in human brain research. In this context, there is a pressing need for innovative ideas to develop flexible analysis methods that can scale to handle large-volume fMRI data, process the data in a distributed and policy-compliant manner, and capture diverse global and local patterns leveraging the big pool of fMRI data. This paper is a survey of some of the recent research in this direction.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6319-6323
Number of pages5
ISBN (Electronic)9781509041176
DOIs
StatePublished - Jun 16 2017
Externally publishedYes
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: Mar 5 2017Mar 9 2017

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
CountryUnited States
CityNew Orleans
Period3/5/173/9/17

Keywords

  • data-driven analysis
  • Functional MRI
  • large-scale data

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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