Flexible large-scale fMRI analysis: A survey

Seung Jun Kim; Vince Daniel Calhoun; Tulay Adali

doi:10.1109/ICASSP.2017.7953372

Flexible large-scale fMRI analysis: A survey

Seung Jun Kim, Vince Daniel Calhoun, Tulay Adali

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6319-6323
Number of pages	5
ISBN (Electronic)	9781509041176
DOIs	https://doi.org/10.1109/ICASSP.2017.7953372
State	Published - Jun 16 2017
Externally published	Yes
Event	2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States Duration: Mar 5 2017 → Mar 9 2017

Other

Other	2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Country	United States
City	New Orleans
Period	3/5/17 → 3/9/17

Keywords

data-driven analysis
Functional MRI
large-scale data

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP.2017.7953372

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Cite this

Flexible large-scale fMRI analysis : A survey. / Kim, Seung Jun; Calhoun, Vince Daniel; Adali, Tulay.

2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 6319-6323 7953372.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, SJ, Calhoun, VD & Adali, T 2017, Flexible large-scale fMRI analysis: A survey. in 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings., 7953372, Institute of Electrical and Electronics Engineers Inc., pp. 6319-6323, 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017, New Orleans, United States, 3/5/17. https://doi.org/10.1109/ICASSP.2017.7953372

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