The role of diversity in data‐driven analysis of multi‐subject fMRI data: Comparison of approaches based on independence and sparsity using global performance metrics

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dc.contributor.authorLong, Qunfang
dc.contributor.authorBhinge, Suchita
dc.contributor.authorLevin‐Schwartz, Yuri
dc.contributor.authorBoukouvalas, Zois
dc.contributor.authorCalhoun, Vince D.
dc.contributor.authorAdali, Tulay
dc.date.accessioned2018-10-19T13:18:32Z
dc.date.available2018-10-19T13:18:32Z
dc.date.issued2018-09-21
dc.description.abstractData‐driven methods have been widely used in functional magnetic resonance imaging (fMRI) data analysis. They extract latent factors, generally, through the use of a simple generative model. Independent component analysis (ICA) and dictionary learning (DL) are two popular data‐driven methods that are based on two different forms of diversity—statistical properties of the data—statistical independence for ICA and sparsity for DL. Despite their popularity, the comparative advantage of emphasizing one property over another in the decomposition of fMRI data is not well understood. Such a comparison is made harder due to the differences in the modeling assumptions between ICA and DL, as well as within different ICA algorithms where each algorithm exploits a different form of diversity. In this paper, we propose the use of objective global measures, such as time course frequency power ratio, network connection summary, and graph theoretical metrics, to gain insight into the role that different types of diversity have on the analysis of fMRI data. Four ICA algorithms that account for different types of diversity and one DL algorithm are studied. We apply these algorithms to real fMRI data collected from patients with schizophrenia and healthy controls. Our results suggest that no one particular method has the best performance using all metrics, implying that the optimal method will change depending on the goal of the analysis. However, we note that in none of the scenarios we test the highly popular Infomax provides the best performance, demonstrating the cost of exploiting limited form of diversity.en_US
dc.description.sponsorshipNational Institutes of Health, Grant/Award Number: NIH P20GM103472, NIH R01EB 020407; National Science Foundation, Grant/ Award Number: NSF 1631838, NSF‐CCF 1618551en_US
dc.description.urihttps://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.24389en_US
dc.format.extent16 pagesen_US
dc.genrejournal articleen_US
dc.identifierdoi:10.13016/M2FX7425M
dc.identifier.citationQunfang Long, Suchita Bhinge , Yuri Levin‐Schwartz , Zois Boukouvalas , Vince D. Calhoun, Tülay Adalı, The role of diversity in data‐driven analysis of multi‐subject fMRI data: Comparison of approaches based on independence and sparsity using global performance metrics, HBM,2018;1–16, |https://doi.org/10.1002/hbm.24389en_US
dc.identifier.uri|https://doi.org/10.1002/hbm.24389
dc.identifier.urihttp://hdl.handle.net/11603/11602
dc.language.isoen_USen_US
dc.publisherWiley Periodicalsen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsThis is the peer reviewed version of the following article: Qunfang Long, Suchita Bhinge , Yuri Levin‐Schwartz , Zois Boukouvalas , Vince D. Calhoun, Tülay Adalı, The role of diversity in data‐driven analysis of multi‐subject fMRI data: Comparison of approaches based on independence and sparsity using global performance metrics, HBM,2018;1–16, |https://doi.org/10.1002/hbm.24389, which has been published in final form at https://doi.org/10.1002/hbm.24389. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
dc.subjectdata‐driven analysisen_US
dc.subjectdictionary learningen_US
dc.subjectdiversityen_US
dc.subjectfMRI analysisen_US
dc.subjectglobal metricen_US
dc.subjectindependenceen_US
dc.subjectperformance evaluationen_US
dc.subjectsparsityen_US
dc.titleThe role of diversity in data‐driven analysis of multi‐subject fMRI data: Comparison of approaches based on independence and sparsity using global performance metricsen_US
dc.typeTexten_US

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