Wei Du1, Yuri Levin-Schwartz2, Geng-Shen Fu1, Sai Ma1, Vince D Calhoun3, Tülay Adalı1. 1. Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA. 2. Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA. Electronic address: ylevins1@umbc.edu. 3. The Mind Research Network, Albuquerque, NM 87106, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA.
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. NEW METHOD: 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. COMPARISON WITH EXISTING 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.
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. NEW METHOD: 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. COMPARISON WITH EXISTING 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: Mert R Sabuncu; Benjamin D Singer; Bryan Conroy; Ronald E Bryan; Peter J Ramadge; James V Haxby Journal: Cereb Cortex Date: 2010-01 Impact factor: 5.357
Authors: José M Soares; Ricardo Magalhães; Pedro S Moreira; Alexandre Sousa; Edward Ganz; Adriana Sampaio; Victor Alves; Paulo Marques; Nuno Sousa Journal: Front Neurosci Date: 2016-11-10 Impact factor: 4.677