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• W2899161804 abstract "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. 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. 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. 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. These results demonstrate that GDMs are an effective way to compare the performances of different factorization-based methods as well as regression-based analyses." @default.
• W2899161804 created "2018-11-09" @default.
• W2899161804 creator A5027564445 @default.
• W2899161804 creator A5032850756 @default.
• W2899161804 creator A5060798483 @default.
• W2899161804 date "2019-01-01" @default.
• W2899161804 modified "2023-10-16" @default.
• W2899161804 title "A method to compare the discriminatory power of data-driven methods: Application to ICA and IVA" @default.
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• W2899161804 doi "https://doi.org/10.1016/j.jneumeth.2018.10.008" @default.
• W2899161804 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6258321" @default.
• W2899161804 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30389489" @default.
• W2899161804 hasPublicationYear "2019" @default.
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