ArticleBiometrics2022
Discussion on "distributional independent component analysis for diverse neuroimaging modalities" by Ben Wu, Subhadip Pal, Jian Kang, and Ying Guo.
Article in Biometrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- A group distributional ICA method for decomposing multi-subject diffusion tensor imaging.Biometrics · 2025Article
- Unveiling hidden sources of dynamic functional connectome through a novel regularized blind source separation approach.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- LOCUS: A REGULARIZED BLIND SOURCE SEPARATION METHOD WITH LOW-RANK STRUCTURE FOR INVESTIGATING BRAIN CONNECTIVITY.The annals of applied statistics · 2023Article
- Rejoinder to discussions of "distributional independent component analysis for diverse neuroimaging modalities".Biometrics · 2022Article
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Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.