title: The role of diversity in complex ICA algorithms for fMRI analysis.
abstract: BACKGROUND
  The widespread use of data-driven methods, such as independent component analysis (ICA), for the analysis of functional magnetic resonance imaging data (fMRI) has enabled deeper understanding of neural function. However, most popular ICA algorithms for fMRI analysis make several simplifying assumptions, thus ignoring sources of statistical information, types of "diversity," and limiting their performance.
  
  
  METHODS
  We propose the use of complex entropy rate bound minimization (CERBM) for the analysis of actual fMRI data in its native, complex, domain. Though CERBM achieves enhanced performance through the exploitation of the three types of diversity inherent to complex fMRI data: noncircularity, non-Gaussianity, and sample-to-sample dependence, CERBM produces results that are more variable than simpler methods. This motivates the development of a minimum spanning tree (MST)-based stability analysis that mitigates the variability of CERBM.
  
  
  METHODS
  In order to validate our method, we compare the performance of CERBM with the popular CInfomax as well as complex entropy bound minimization (CEBM).
  
  
  RESULTS
  We show that by leveraging CERBM and the MST-based stability analysis, we are able to consistently produce components that have a greater number of activated voxels in physically meaningful regions and can more accurately classify patients with schizophrenia than components generated using simpler models.
  
  
  CONCLUSIONS
  Our results demonstrate the advantages of using ICA algorithms that can exploit all inherent types of diversity for the analysis of fMRI data when coupled with appropriate stability analyses.
authors.affiliation: Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA.
authors.forename: Wei
authors.initials: W
authors.lastname: Du
authors.affiliation: Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA. Electronic address: ylevins1@umbc.edu.
authors.forename: Yuri
authors.initials: Y
authors.lastname: Levin-Schwartz
authors.affiliation: Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA.
authors.forename: Geng-Shen
authors.initials: GS
authors.lastname: Fu
authors.affiliation: Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA.
authors.forename: Sai
authors.initials: S
authors.lastname: Ma
authors.affiliation: The Mind Research Network, Albuquerque, NM 87106, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA.
authors.forename: Vince D
authors.initials: VD
authors.lastname: Calhoun
authors.affiliation: Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA.
authors.forename: Tülay
authors.initials: T
authors.lastname: Adalı
mesh_terms: D000465:Algorithms; D059907:Functional Neuroimaging / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*
keywords: AOD task; Complex fMRI; ICA