Extraction of common task signals and spatial maps from group fMRI using a PARAFAC-based tensor decomposition technique

Bhaskar Sen, Keshab K Parhi

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

8 Scopus citations

Abstract

Blind source separation (BSS) using independent component based analysis (e.g., probabilistic ICA and infomax ICA) have been studied in-depth to extract common hemodynamic sources for a group of functional magnetic resonance images (fMRI). The inherent assumption here is that the sources must be non-Gaussian. For most of the real world data, the decomposition is non-unique. Furthermore, there is no quantitative way to determine the component(s) of interest common for the group. This paper shows that using a novel constrained Parallel Factor Analysis (PARAFAC)-based tensor decomposition, one can extract the common task signals and spatial maps from a group of noisy fMRI as rank-1 tensors. The extracted hemodynamic signals have very high correlation with ideal hemodynamic response. A quantitative algorithm to extract common components for a group of subjects is also presented. The modified decomposition preserves the uniqueness under mild conditions which is the most attractive feature for any PARAFAC-based tensor decomposition approach.

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.
Pages1113-1117
Number of pages5
ISBN (Electronic)9781509041176
DOIs
StatePublished - Jun 16 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: Mar 5 2017Mar 9 2017

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

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

Keywords

  • PARAFAC
  • fMRI
  • spatial map
  • task signal
  • tensor decomposition

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