With a linear mixing model and the assumption of statistical independence, ICA decomposes observations into maximally independent components (ICs). IVA generalizes ICA to multiple data sets and utilizes the statistical dependence across different data sets to achieve a powerful solution for data fusion [20; 24]. CCA and mCCA, on the other hand, make use of second-order statistics (correlation), and it can be shown that IVA generalizes both [21]. A number of models based on ICA, IVA, CCA, and mCCA have been developed for data fusion, such as joint ICA (jICA) [25], mCCA + jICA [26], and transposed IVA (tIVA) [15; 27]. JICA concatenates all the datasets together and then performs ICA on the joint dataset for data fusion. The jICA model assumes a common mixing matrix and order as well as equal contributions across all datasets--a set of strong constraints, especially for more than two modalities. For mCCA + jICA, the use of mCCA in the first stage relaxes the strong assumptions of identical mixing matrices; however, a common order for the different modalities is still needed. TIVA also requires a common order in different modalities and a significantly large number of subjects for enough statistical power, which in a large number of cases is not feasible in current experimental settings. Collectively, none of these methods are flexible in terms of dealing with different orders and finding \"one-to-many associations\" in real-world data. However, firstly, due to the disparate measurements in different modalities, the order of the signal subspace is very likely to be different between modalities [28; 29]. Secondly, one component in one modality might be associated with multiple components in other modalities because of the intrinsic relationships between modalities. For instance, the same WM connects multiple regions of the GM; thus, \"one-tomany associations\" between dMRI and sMRI datasets are expected. Besides, the fusion of more than two modalities of brain imaging data is expected to further facilitate exploiting joint information beyond pair-wise associations; nonetheless, most data fusion methods have been implemented to find multiple associations between only two modalities [26]. Therefore, a new framework for multi-modal data fusion is needed that is fully flexible in terms of the number of datasets combined, allowing for different orders across modalities and the discovery of \"one-to-many associations\". In this study, we propose a fully flexible data fusion framework, consecutive independence and correlation transform (C-ICT), to jointly analyze more than two datasets. By successively performing ICA and IVA, C-ICT exploits the strengths of ICA and IVA for the joint analysis of multimodal data. First, ICA is performed on individual datasets separately to obtain maximally independent components and corresponding subject profile matrices (first-level mixing matrices). Second, meaningful ICs and the corresponding subject profiles are selected for further analysis. Third, IVA with a multivariate Gaussian model (IVA-G) [30] is performed on the subject profile matrices of different datasets to obtain the source component vectors (SCVs) and the second-level mixing matrices. SCVs that show significant pair-wise correlations are chosen for further analysis. Finally, we trace back to the ICs in the first stage based on subject profiles with the highest contribution to the correlated SCVs and identify them as associated components across different modalities. Thus, C-ICT is fully flexible in terms of the number of datasets combined, the numbers of orders of the signal subspace for each dataset, and the discovery of \"one-to-many associations\", and builds on the related concept we introduced in [28] for only two datasets. We apply this new method to the fusion of data from three brain-imaging modalities (dMRI, sMRI, and resting-state fMRI) to search for possible multiple associations across these datasets. The data were collected from 86 healthy controls (HCs) and 76 patients with schizophrenia (SZs).