Journal Article

Identification of multimodal brain imaging association via a parameter decomposition based sparse multi-view canonical correlation analysis method

Du, Lei, Guo, Lei, Zhang, Jin, Wang, Huiai, Zhao, Ying

2022

Overview

Background With the development of noninvasive imaging technology, collecting different imaging measurements of the same brain has become more and more easy. These multimodal imaging data carry complementary information of the same brain, with both specific and shared information being intertwined. Within these multimodal data, it is essential to discriminate the specific information from the shared information since it is of benefit to comprehensively characterize brain diseases. While most existing methods are unqualified, in this paper, we propose a parameter decomposition based sparse multi-view canonical correlation analysis (PDSMCCA) method. PDSMCCA could identify both modality-shared and -specific information of multimodal data, leading to an in-depth understanding of complex pathology of brain disease. Results Compared with the SMCCA method, our method obtains higher correlation coefficients and better canonical weights on both synthetic data and real neuroimaging data. This indicates that, coupled with modality-shared and -specific feature selection, PDSMCCA improves the multi-view association identification and shows meaningful feature selection capability with desirable interpretation. Conclusions The novel PDSMCCA confirms that the parameter decomposition is a suitable strategy to identify both modality-shared and -specific imaging features. The multimodal association and the diverse information of multimodal imaging data enable us to better understand the brain disease such as Alzheimer’s disease.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Alzheimer Disease - diagnostic imaging / Alzheimer's disease / Bioinformatics / Biomedical and Life Sciences / Brain / Brain - diagnostic imaging / Brain research / Canonical Correlation Analysis / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Correlation analysis / Correlation coefficient / Correlation coefficients / Datasets / Decomposition / Diagnosis / Diagnostic imaging / Feature selection / Humans / Life Sciences / Machine learning / Magnetic resonance imaging / Magnetic Resonance Imaging - methods / Medical imaging / Methodology / Methods / Microarrays / Multi-view canonical correlation analysis / Neurodegenerative diseases / Neuroimaging / Neuroimaging - methods / Parameter decomposition / Parameter identification / Pathogenesis / Performance evaluation / Simulation / Sparse learning / Technology application / Tomography