)1 Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA; [EMAIL_ADDRESS] (M.A.B.S.A. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (T.A. )1 Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA; [EMAIL_ADDRESS] (M.A.B.S.A. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (T.A. )1 Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA; [EMAIL_ADDRESS] (M.A.B.S.A. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (T.A. )1 Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA; [EMAIL_ADDRESS] (M.A.B.S.A. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (T.A. )1 Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA; [EMAIL_ADDRESS] (M.A.B.S.A. ); [EMAIL_ADDRESS] (Q.L.) [EMAIL_ADDRESS] (T.A. )1 Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, Baltimore, MD 21250, USA; [EMAIL_ADDRESS] (M.A.B.S.A. ); [EMAIL_ADDRESS] (Qbased on the differential diffusion rates of water molecules in different brain tissues. SMRI provides information about the morphology of brain tissues including WM, grey matter (GM), and cerebrospinal fluid (CSF). FMRI detects neuronal activations by measuring the blood oxygenation level-dependent response in the brain. Each of these three imaging techniques reveals different yet complementary information about brain structures or activities. Because of the intrinsic relationships between the structure and activity of the brain, multiple associations between these modalities are expected. This motivates the increasing popularity of collecting data from the same subjects using multiple modalities [8; 9]. A joint analysis of such multimodal data should be useful for finding multiple associations across modalities, revealing the mechanisms of brain functions and potentially improving the predictive power to diagnose diseases compared with unimodal analyses. Data fusion is concerned with the simultaneous analyses of such joint datasets to obtain a global view of a problem under study or a system under observation by leveraging the information that is available across multiple datasets so that further analyses (e.g., detection, classification) can be improved [10; 11; 12]. However, since the relationships between different modalities are largely unknown, data-driven methods are particularly attractive. In this regard, data-driven methods based on blind source separation (BSS) have proven to be particularly useful for data fusion due to their ability to decompose the observed data into a set of latent variables, also known as components, through a simple generative model [13; 14; 15; 16]. The components obtained from different datasets separately or jointly through the BSS techniques could be used to reveal potential relationships between different datasets. For example, components obtained from the datasets of sMRI and fMRI through the BSS techniques show locations and intensities of separated source signals in the brain (i.e., spatial maps) and allow us to find how the brain activities (fMRI) are related to the brain structures (sMRI). Importantly, due to the existence of systematic noise and interference in the real-world data, performing data fusion in the signal subspace with proper numbers of components (i.e., orders of signal subspace) effectively reduces the noise and interference and thus enables a better generalization ability. Among various BSS techniques, independent component analysis (ICA) [17] and canonical correlation analysis (CCA) [18; 19], as well as their extensions to multiple datasets--independent vector analysis (IVA) [20; 21] and multi-set CCA (mCCA) [22; 23]--have proven to be especially useful for data fusion.