Structured and Sparse Canonical Correlation Analysis as a Brain-Wide Multi-Modal Data Fusion Approach

• Published in: IEEE transactions on medical imaging Vol. 36; no. 7; pp. 1438 - 1448
• Main Authors: Mohammadi-Nejad, Ali-Reza, Hossein-Zadeh, Gholam-Ali, Soltanian-Zadeh, Hamid
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: ADNI; Algorithms; Alzheimer's disease; Brain; Brain Mapping; canonical correlation analysis (CCA); Collinearity; Computer simulation; Correlation; Correlation analysis; Covariance matrices; Data integration; Disease control; Functional magnetic resonance imaging; Humans; Image processing; Magnetic Resonance Imaging; magnetic resonance imaging (MRI); Mapping; Medical imaging; Modal data; multi-modal data fusion; Multisensor fusion; multivariate analysis; Neurodegenerative diseases; Neuroimaging; Neurology; NMR; Nuclear magnetic resonance; Patients; Spatial data
• DOI: 10.1109/TMI.2017.2681966
• ISSN: 0278-0062; 1558-254X

Multi-modal data fusion has recently emerged as a comprehensive neuroimaging analysis approach, which usually uses canonical correlation analysis (CCA). However, the current CCA-based fusion approaches face problems like high-dimensionality, multi-collinearity, unimodal feature selection, asymmetry, and loss of spatial information in reshaping the imaging data into vectors. This paper proposes a structured and sparse CCA (ssCCA) technique as a novel CCA method to overcome the above problems. To investigate the performance of the proposed algorithm, we have compared three data fusion techniques: standard CCA, regularized CCA, and ssCCA, and evaluated their ability to detect multi-modal data associations. We have used simulations to compare the performance of these approaches and probe the effects of non-negativity constraint, the dimensionality of features, sample size, and noise power. The results demonstrate that ssCCA outperforms the existing standard and regularized CCA-based fusion approaches. We have also applied the methods to real functional magnetic resonance imaging (fMRI) and structural MRI data of Alzheimer's disease (AD) patients (n = 34) and healthy control (HC) subjects (n = 42) from the ADNI database. The results illustrate that the proposed unsupervised technique differentiates the transition pattern between the subject-course of AD patients and HC subjects with a p-value of less than 1 × 10 -6 . Furthermore, we have depicted the brain mapping of functional areas that are most correlated with the anatomical changes in AD patients relative to HC subjects.