Predicting Emotional Intelligence Scores from Multi-session Functional Brain Connectomes

In this study, we aim to predict emotional intelligence scores from functional connectivity data acquired at different timepoints. To enhance the generalizability of the proposed predictive model to new data and accurate identification of most relevant neural correlates with different facets of the human intelligence, we propose a joint support vector machine and support vector regression (SVM+SVR) model. Specifically, we first identify most discriminative connections between subjects with high vs low emotional intelligence scores in the SVM step and then perform a multi-variate linear regression using these connections to predict the target emotional intelligence score in the SVR step. Our method outperformed existing methods including the Connectome-based Predictive Model (CPM) using functional connectivity data simultaneously acquired with the intelligence scores. The most predictive connections of intelligence included brain regions involved in processing of emotions and social behaviour.