ArticleJournal of neuroscience methods2019
A method to compare the discriminatory power of data-driven methods: Application to ICA and IVA.
Article in Journal of neuroscience methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Denoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude-only fMRI data.Human brain mapping · 2023Article
- Identification of Homogeneous Subgroups from Resting-State fMRI Data.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundThe 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
methodWe 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
methodsWe 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.
resultsThrough 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.
conclusionsThese results demonstrate that GDMs are an effective way to compare the performances of different factorization-based methods as well as regression-based analyses.
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