Prediction of meditation experience using fMRI functional connectivity and multivariate pattern analysis

Mindfulness meditation is a complex psychological process that involves emotional, attentional and awareness practices. In this work, we predict the years of meditation experience of Buddhist monks using Multivariate Pattern Regression and functional connectivity analysis. 12 Buddhist monks underwent a meditation session according to two meditation styles while BOLD fMRI at 1.5 T was recorded. Functional connectivity matrices were calculated between functionally subdivided ROIs. Correlations were used as features for a Support Vector Regression with target the monks' experience. Regression weights highlighted prominent connections and differentiated meditation styles.