Multimodal Imaging Feature Extraction with Reference Canonical Correlation Analysis Underlying Intelligence

Abstract

With neuroimaging data scientists have gained substantial information of the neuronal underpinning of intelligence. Yet how to integrate multimodal neuronal features effectively in relation to intelligence remains elusive. In this paper, we have developed a reference Canonical Correlation Analysis (RCCA) model that extracts latent, correlated multimodal features while enhancing correlation to a reference of interest. We applied RCCA to gray matter and white matter images from 7874 participants, and compared the derived features with those from Principle Components Analysis (PCA) and sparse CCA (SCCA), in terms of association with intelligence and prediction effectiveness using LASSO regression models. Eight RCCA features explained 10%, 16% and 17% variance of fluid intelligence, crystallized intelligence, and total composite score, respectively, which are similar to the percentage of variance explained by over 100 principle components. SCCA features presented the least variance of intelligence. Our results indicate RCCA model can successfully extract features of interest. The top brain regions that contribute to intelligence include the frontal regions and cingulate gyrus.