Resting-state fMRI activity in the basal ganglia predicts unsupervised learning performance in a virtual reality environment
- Resource Type
- Conference
- Authors
- Wong, Chi Wah; Olafsson, Valur; Plank, Markus; Snider, Joseph; Halgren, Eric; Poizner, Howard; Liu, Thomas
- Source
- 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER) Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on. :1533-1536 Nov, 2013
- Subject
- Bioengineering
Basal ganglia
Magnetic resonance imaging
Correlation
Virtual reality
Time series analysis
Unsupervised learning
- Language
- ISSN
- 1948-3546
1948-3554
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.