Yuri Levin-Schwartz1, Vince D Calhoun2, Tülay Adalı3. 1. Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, United States. Electronic address: ylevins1@umbc.edu. 2. The Mind Research Network, Albuquerque, NM 87106, United States; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, United States. 3. Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, United States.
Abstract
BACKGROUND: The widespread application of data-driven factorization-based methods, such as independent component analysis (ICA), to functional magnetic resonance imaging data facilitates the study of neural function and how it is disrupted by psychiatric disorders such as schizophrenia. While the increasing number of these methods motivates a comparison of their relative performance, such a comparison is difficult to perform on real fMRI data, since the ground truth is, relatively, unknown and the alignment of factors across different methods is impractical and imprecise. NEW METHOD: We present a novel method, global difference maps (GDMs), to compare the results of different fMRI analysis techniques on real fMRI data, quantify their relative performances, and highlight the differences between the decompositions visually. COMPARISON WITH EXISTING METHODS: We apply this method to compare the performances of two different factorization-based methods, ICA and its multiset extension independent vector analysis (IVA), for the analysis of fMRI data from 109 patients with schizophrenia and 138 healthy controls during the performance of three tasks. RESULTS: Through this application of GDMs, we find that IVA can determine regions that are more discriminatory between patients and controls than ICA, though IVA is less effective at emphasizing regions found in only a subset of the tasks. CONCLUSIONS: These results demonstrate that GDMs are an effective way to compare the performances of different factorization-based methods as well as regression-based analyses.
BACKGROUND: The widespread application of data-driven factorization-based methods, such as independent component analysis (ICA), to functional magnetic resonance imaging data facilitates the study of neural function and how it is disrupted by psychiatric disorders such as schizophrenia. While the increasing number of these methods motivates a comparison of their relative performance, such a comparison is difficult to perform on real fMRI data, since the ground truth is, relatively, unknown and the alignment of factors across different methods is impractical and imprecise. NEW METHOD: We present a novel method, global difference maps (GDMs), to compare the results of different fMRI analysis techniques on real fMRI data, quantify their relative performances, and highlight the differences between the decompositions visually. COMPARISON WITH EXISTING METHODS: We apply this method to compare the performances of two different factorization-based methods, ICA and its multiset extension independent vector analysis (IVA), for the analysis of fMRI data from 109 patients with schizophrenia and 138 healthy controls during the performance of three tasks. RESULTS: Through this application of GDMs, we find that IVA can determine regions that are more discriminatory between patients and controls than ICA, though IVA is less effective at emphasizing regions found in only a subset of the tasks. CONCLUSIONS: These results demonstrate that GDMs are an effective way to compare the performances of different factorization-based methods as well as regression-based analyses.
Authors: Shijie Zhao; Junwei Han; Jinglei Lv; Xi Jiang; Xintao Hu; Yu Zhao; Bao Ge; Lei Guo; Tianming Liu Journal: IEEE Trans Med Imaging Date: 2015-04-01 Impact factor: 10.048
Authors: Vince D Calhoun; Tulay Adali; Kent A Kiehl; Robert Astur; James J Pekar; Godfrey D Pearlson Journal: Hum Brain Mapp Date: 2006-07 Impact factor: 5.038
Authors: Andrew M Michael; Stefi A Baum; Jill F Fries; Beng-Choon Ho; Ronald K Pierson; Nancy C Andreasen; Vince D Calhoun Journal: Hum Brain Mapp Date: 2009-08 Impact factor: 5.038