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Discussion on “distributional independent component analysis for diverse neuroimaging modalities” by Ben Wu, Subhadip Pal, Jian Kang, and Ying Guo
Author(s) -
Keeratimahat Kan,
Nichols Thomas E.
Publication year - 2022
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13591
Subject(s) - initialization , diffusion mri , range (aeronautics) , computer science , reliability (semiconductor) , neuroimaging , tensor (intrinsic definition) , psychology , econometrics , artificial intelligence , mathematics , statistics , medicine , neuroscience , physics , magnetic resonance imaging , pure mathematics , power (physics) , materials science , quantum mechanics , composite material , radiology , programming language
Wu et al. have made an important contribution to the methodology for data‐driven analysis of MRI data. However, we wish to challenge the authors on new potential applications of their approach beyond diffusion tensor imaging data, and to think carefully about the impact of random initialization implicit in their method. We illustrate the variability found from re‐analyzing the supplied demonstration data multiple times, finding that the discovered independent components have a wide range of reliability, from nearly perfect overlap to no overlap at all.