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Wu et al. present a distributional independent component analysis (DICA), providing a generalized framework that has the ability to extract features from different imaging modalities. We commend the authors on this highly innovative and useful development. ICA is at the forefront of neuroimaging analyses, recognized for its ability to evaluate the hidden spatiotemporal structure contained within brain imaging data (Calhoun et al., 2009). However, as Wu et al. point out, integrating information across different imaging modalities is nontrivial. The framework that Wu et al. present has the flexibility to extract source signals from diverse types of data by taking a fundamentally different approach compared to that of classical ICA. Instead of performing source separation directly on the observed data, theymodel the observed datawith amixture distribution where the component distributions are tailored to the data representations from a particular imaging modality. The mixture distribution model contains the component distributions, as well as a set of weight parameters that correspond to the loading on each component distribution. DICA then performs the ICA decomposition on the posterior weights. This setup allows for the component distribution to depend on the imaging modality, with the weights being comparable across modalities. There are several exciting advantages to this newmodeling approach, one of which is that it is the first ICAmethod that allows for source separations for diffusion tensors that are based on single subject diffusion-weighted magnetic resonance imaging (DWI) scans. Diffusion tensor magnetic resonance imaging (DTI) is a popular approach for studying normal brain development and aging, as well as changes in various brain disorders, due to its unique ability to identifymicrostructural abnormalities. To date, much of the DTI studies focus on multiple-subject group comparisons. This is largely due to the lack of statistical techniques available to perform single-subject analyses. Although a group level analysis is sufficient when the effects of interest are located in the same anatomic structures across subjects, it is not ideal in situations in which effects are expected to be focal with spatial distributions that are specific to individual subjects (Chung et al., 2008). For example, the study of traumatic brain injury (TBI) has been slow moving in recent decades. Although DTI has provided an important means to study the pathophysiology of TBI, challenges in proposing effective treatment strategies largely stem from the heterogeneity of pathology, which make it difficult to understand the effects of TBI on an individual basis in clinical settings (Ware et al., 2017). Therefore, statistical techniques that can be applied to DTI data on an individual basis are needed for the discovery of reliable biomarkers of injury. The innovative DICA approach proposed byWu et al. is an important step in this direction. As briefly mentioned earlier, the other major advantage of DICA is its ability to analyze data from different imaging modalities. Often, it is of interest to compare findings obtained frommultiple types of data (e.g., EEG, MEG,