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Abstract #2271

Resting-State fMRI Activity in the Basal Ganglia Predicts Unsupervised Learning Performance in a Virtual Reality Environment

Valur Olafsson1, Chi Wah Wong1, Markus Plank2, Joe Snider2, Eric Halgren3, Howard Poizner2, Thomas Liu1

1Center for Functional MRI, UCSD, La Jolla, CA, United States; 2Institute for Neural Computation, UCSD, La Jolla, CA, United States; 3Multimodal Imaging Laboratory, UCSD, La Jolla, CA, United States

Learning without feedback is often referred to as unsupervised learning. Prior work suggests that structures in the basal ganglia have a role in unsupervised learning performance. In this study we ran a resting state fMRI study on 10 subjects that had gone through an unsupervised learning task in a complex virtual reality environment and had been assessed on their performance to learn without feedback. We found that there was significant correlation between the performance measures and resting-state fMRI measures from basal ganglia structures. Our results indicate that resting-state measures could be used to predict individual differences in unsupervised learning.