Resting-state fMRI activity in the basal ganglia predicts unsupervised learning performance in a virtual reality environment
Neural Engineering(2013)
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.
MoreTranslated text
Key words
biomedical mri,medical image processing,unsupervised learning,virtual reality,basal ganglia,internal environment representation,intrinsic fmri signal fluctuations,resting-state fmri activity,resting-state fmri signal amplitudes,resting-state functional magnetic resonance imaging,self-exploration,unsupervised learning performance,virtual reality unsupervised spatial learning environment
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined