Resting-state brain activity predicts neurofeedback training aptitude

Abstract

Neurofeedback (NF) training has been developed as a promising novel treatment of brain psychiatric disorders. However, NF aptitude, an individual's ability to change brain activity through NF training, has been reported to vary significantly among different individuals. In the present study, we applied machine learning to resting-state functional magnetic resonance imaging (fMRI) data for the prediction of NF aptitude. We trained the multiple regression models to predict the individual NF aptitude scores from the resting-state functional brain connectivity (FC) data. As result, we identified six resting-state FCs that predicted NF aptitude and succeeded in the prediction of NF aptitude. The identified FC model revealed that the posterior cingulate cortex and posterior insular cortex were the functional hub and formed predictive resting-state FCs, suggesting that NF aptitude may be involved in the attentional mode-orientation modulation system's characteristics in task-free resting-state brain activity.