A graph theoretical approach for performance comparison of ICA for fMRI analysis

Qunfang Long, Suchita Bhinge, Yuri Levin-Schwartz, Vince Daniel Calhoun, Tülay Adali

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

Abstract

Due to its relatively few assumptions, independent component analysis (ICA) has become a widely-used tool for the analysis of functional magnetic resonance imaging (fMRI) data. In its application, Infomax, has been by far the most frequently used ICA algorithm, primarily because it is the first ICA algorithm applied to fMRI analysis. However, now there are a number of more flexible ICA algorithms, which can exploit multiple types of statistical properties of the signals with fewer assumptions. In this work, we investigate the performance of Infomax and two of the more recent ICA algorithms, entropy bound minimization (EBM) and entropy rate bound minimization (ERBM), on resting state fMRI data derived from a large number of patients with schizophrenia (SZs) and healthy controls (HCs). In order to overcome the difficulty of directly comparing the performances of different ICA algorithms on real fMRI data, we propose the use of graph theoretic (GT) metrics to assess the quality of an ICA decomposition by measuring an algorithm's ability to capture the inherent differences between SZs and HCs. Our results show that ERBM, the algorithm which incorporates the greatest number of statistical properties of the signals, provides the best performance for fMRI analysis.

Original languageEnglish (US)
Title of host publication2017 51st Annual Conference on Information Sciences and Systems, CISS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509047802
DOIs
StatePublished - May 10 2017
Externally publishedYes
Event51st Annual Conference on Information Sciences and Systems, CISS 2017 - Baltimore, United States
Duration: Mar 22 2017Mar 24 2017

Other

Other51st Annual Conference on Information Sciences and Systems, CISS 2017
CountryUnited States
CityBaltimore
Period3/22/173/24/17

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems and Management
  • Computer Networks and Communications
  • Information Systems

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  • Cite this

    Long, Q., Bhinge, S., Levin-Schwartz, Y., Calhoun, V. D., & Adali, T. (2017). A graph theoretical approach for performance comparison of ICA for fMRI analysis. In 2017 51st Annual Conference on Information Sciences and Systems, CISS 2017 [7926108] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISS.2017.7926108