This work proposes a novel generative multimodal approach to jointly analyze multimodal data while linking the multimodal information to colors. We apply our proposed framework, which disentangles multimodal data into private and shared sets of features from pairs of structural (sMRI), functional (sFNC and ICA), and diffusion MRI data (FA maps). With our approach, we find that heterogeneity in schizophrenia is potentially a function of modality pairs. Results show (1) schizophrenia is highly multimodal and includes changes in specific networks, (2) non‐linear relationships with schizophrenia are observed when interpolating among shared latent dimensions, and (3) we observe a decrease in the modularity of functional connectivity and decreased visual‐sensorimotor connectivity for schizophrenia patients for the FA‐sFNC and sMRI‐sFNC modality pairs, respectively. Additionally, our results generally indicate decreased fractional corpus callosum anisotropy, and decreased spatial ICA map and voxel‐based morphometry strength in the superior frontal lobe as found in the FA‐sFNC, sMRI‐FA, and sMRI‐ICA modality pair clusters. In sum, we introduce a powerful new multimodal neuroimaging framework designed to provide a rich and intuitive understanding of the data which we hope challenges the reader to think differently about how modalities interact.
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
- NSF-PAR ID:
- 10397028
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Biometrics
- Volume:
- 78
- Issue:
- 3
- ISSN:
- 0006-341X
- Format(s):
- Medium: X Size: p. 1109-1112
- Size(s):
- p. 1109-1112
- Sponsoring Org:
- National Science Foundation
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