## 4 Discussion  
In this study, we propose a new data fusion framework, C-ICT, to uncover relationships across multiple brain imaging modalities to reveal the underlying mechanisms of brain function as well as their dysfunction in diseases such as schizophrenia. Specifically, C-ICT uses ICA to extract independent brain networks from each modality and then explores possible associations across the modalities using IVA. Compared with other existing data fusion frameworks, C-ICT is flexible in many ways. First, unlike jICA, mCCA + jICA, tIVA, and parallel ICA [15; 25; 26; 78; 27], C-ICT allows for different orders from the different datasets. Datasets of different modalities are different in nature and might also have different levels of signal-to-noise ratios (SNRs). Therefore, different orders would be expected for each modality. Second, C-ICT has no constraint on the number of datasets, like parallel ICA. This freedom is important because a greater number of data modalities can be collected from the same subjects. Thus, a framework that is capable of jointly analyzing all modalities at the same time can provide valuable insight into both the structural and functional aspects of networks responsible for brain functions. Third, C-ICT is unique in that it is able to discover \"one-to-many associations\" between datasets--a feature not available for any other data fusion method. This is possible for C-ICT because one IC with the highest contribution to one SCV might also have the highest contribution to other SCVs. The finding of such \"one-to-many associations\" suggests that one brain region can be recruited by multiple other regions for the performance of complex functions--an important neurobiological mechanism that is also supported by other studies [79; 80; 81]. In addition to being highly flexible, another advantage of the C-ICT framework is a critical step that eliminates ICs that might originate from artifacts such as heartbeats, respiratory movement, and non-brain signals. In doing so, we have observed more interpretable triplets of components, where each of the ICs from the three modalities are associated with each other, and stronger associations between modalities within each triplet, when compared with an implementation that skips this step. We have applied the C-ICT framework to brain imaging data that measure structural and functional aspects of both the WM and GM through dMRI, sMRI, and fMRI in both HCs and SZs. As shown in the results, C-ICT uncovered six IC triplets. Importantly, within each triplet, the three associated components are observed to be all closely functionally related, thus validating our approach. The first triplet includes anterior thalamic radiation and anterior corona radiata (ATR-ACR), postcentral gyrus and precentral gyrus (POG-PCG), and thalamus from dMRI, sMRI, and fMRI, respectively. The ATR is a WM bundle that connects the thalamus with the prefrontal cortex [82]. The thalamus is a hub that relays sensory information to the cerebral cortex [83]. The ACR carries somatotopically arranged motor fibers away from the cerebral cortex. The POG is where the primary somatosensory cortex is located, and PCG is where the primary motor cortex is located. This triplet represents a multimodal brain network that involves nodes that are all related to sensory-motor functions. The second triplet includes the corpus callosum, superior temporal gyrus (STG), and paracentral lobule and postcentral gyrus (PCL-POG) from dMRI, sMRI, and fMRI, respectively. The corpus callosum is an important fiber bundle that connects the left and