Using nonlinear sparse Bayesian learning model to identify the correlation between multiple clinical cognitive scores and neuroimaging measurements

• Published in: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) pp. 2548 - 2553
• Main Authors: Wu, Jie, Hu, Yibo, Fan, Biyue, Chen, Wei, Sun, Deyan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.12.2020
• Subjects: Bayes methods; Biological system modeling; Clinical cognitive scores; Correlation; Magnetic resonance imaging; Neuroimaging; Neuroimaging measures; Prediction algorithms; Schizophrenia; Sparse Bayesian learning; Volume measurement
• DOI: 10.1109/BIBM49941.2020.9313366

Schizophrenia (SZ) is a complex human disease. It is a neurodegenerative disease characterized by the gradual loss of brain function, especially memory and cognitive ability. For many years, MRI has been widely used in schizophrenia studies because it can recognize structural and functional abnormalities in the brain region. In recent years, the most important research topic in the study of mental illness is to predict the cognitive performance of subjects from magnetic resonance imaging (MRI) measurements, and also include the recognition of related imaging biomarkers. Traditionally, this task has been a linear regression problem, but most existing studies cannot capture the relation-ship between the complex nonlinear cognitive properties and MRI measures. Inspired by these observations, we propose a Nonlinear Sparse Bayesian Learning (NSBL) model, and construct a sparse multivariate algorithm. Unlike the existing sparse algorithm, in our proposed model, the nonlinear function of the prediction matrix is responded by extending the block structure. The results show that the nonlinear sparse regression model can obtain better prediction ac-curacy. The model can use the correlation coefficient vector between vectors, it can also use the intra-block correlation in each regression coefficient.