PT  - JOURNAL ARTICLE
AU  - Raphael T. Gerraty
AU  - Madeleine E. Sharp
AU  - Amanda Buch
AU  - Danielle S. Bassett
AU  - Daphna Shohamy
TI  - Dopamine modulates learning-related changes in dynamic striatal-cortical connectivity in Parkinson’s disease
AID  - 10.1101/619478
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 619478
4099  - http://biorxiv.org/content/early/2019/04/26/619478.short
4100  - http://biorxiv.org/content/early/2019/04/26/619478.full
AB  - Learning from reinforcement is thought to depend on striatal dopamine inputs, which serve to update the value of actions by modifying connections in widespread cortico-striatal circuits. While considerable research has described the activity of individual striatal and midbrain regions in reinforcement learning, the broader role for dopamine in modulating network-level processes has been difficult to decipher. To examine whether dopamine modulates circuit-level dynamic connectivity during learning, we characterized the effects of dopamine on learning-related dynamic functional connectivity estimated from fMRI data acquired in patients with Parkinson’s disease. Patients with Parkinson’s disease have severe dopamine depletion in the striatum and are treated with dopamine replacement drugs, providing an opportunity to compare learning and network dynamics when patients are in a low dopamine state (off drugs) versus a high dopamine state (on drugs). We assessed the relationship between dopamine and dynamic connectivity while patients performed a probabilistic reversal learning task. We found that reversal learning altered dynamic network flexibility in the striatum and that this effect was dependent on dopaminergic state. We also found that dopamine modulated changes in connectivity between the striatum and specific task-relevant visual areas of inferior temporal cortex, providing empirical support for theories stipulating that value is updated through changes in cortico-striatal circuits. These results suggest that dopamine exerts a widespread effect on neural circuitry and network dynamics during reinforcement learning.