Discussion on “distributional independent component analysis for diverse neuroimaging modalities” by Ben Wu, Subhadip Pal, Jian Kang, and Ying Guo

• Published in: Biometrics Vol. 78; no. 3; pp. 1109 - 1112
• Main Author: Mejia, Amanda F.
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.09.2022
• Subjects: Algorithms; brain; Brain - diagnostic imaging; Brain mapping; Brain Mapping - methods; Functional magnetic resonance imaging; Image processing; Independent component analysis; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; magnetism; Medical imaging; Mixtures; Neuroimaging; Principal Component Analysis
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/biom.13592

I applaud the authors on their innovative generalized independent component analysis (ICA) framework for neuroimaging data. Although ICA has enjoyed great popularity for the analysis of functional magnetic resonance imaging (fMRI) data, its applicability to other modalities has been limited because standard ICA algorithms may not be directly applicable to a diversity of data representations. This is particularly true for single‐subject structural neuroimaging, where only a single measurement is collected at each location in the brain. The ingenious idea of Wu et al. (2021) is to transform the data to a vector of probabilities via a mixture distribution with K components, which (following a simple transformation to RK−1$\mathbb {R}^{K-1}$) can be directly analyzed with standard ICA algorithms, such as infomax (Bell and Sejnowski, 1995) or fastICA (Hyvarinen, 1999). The underlying distribution forming the basis of the mixture is customized to the particular modality being analyzed. This framework, termed distributional ICA (DICA), is applicable in theory to nearly any neuroimaging modality. This has substantial implications for ICA as a general tool for neuroimaging analysis, with particular promise for structural modalities and multimodal studies. This invited commentary focuses on the applicability and potential of DICA for different neuroimaging modalities, questions around details of implementation and performance, and limitations of the validation study presented in the paper.