Resting-state fMRI activity in the basal ganglia predicts unsupervised learning performance in a virtual reality environment

Abstract

In unsupervised spatial learning, an individual develops internal representations of the environment through self-exploration without explicit feedback or instruction. In this study, we used resting-state functional magnetic resonance imaging (fMRI) to examine whether intrinsic fluctuations of the fMRI signal in the basal ganglia can be used to predict an individual's ability to learn in a virtual-reality unsupervised spatial learning environment. We found that better performers have higher resting-state fMRI signal amplitudes in the basal ganglia.