Back

Cortico-Striatal Beta-Oscillations as a Marker of Learned Reward Value

Koloski, M. F.; Hulyalkar, S.; Tang, T.; Wu, X.; Fakhraei, L.; Mishra, J.; Ramanathan, D. S.

2022-10-25 animal behavior and cognition
10.1101/2022.10.24.513264 bioRxiv
Show abstract

Single neuron correlates of reward value have been observed in brain regions along the cortico-striatal pathway including ventral striatum, orbital, and medial prefrontal cortex. Brain imaging studies in humans further validate these findings and suggest that value is represented in a network of brain regions opposed to a particular area. Neural activity oscillates at periodic frequencies to coordinate long-range communication in widespread, dynamic networks. To explore how oscillatory dynamics across brain regions may represent reward value, we measured local field potentials of male Long-Evans rats during three distinct behavioral tasks, each probing a different aspect of reward processing. Our goal was to use a data-driven approach to identify a common electrophysiology property associated with reward value. We found that reward-locked oscillations at beta frequencies, in both single units and local field potentials, were markers of positive reward valence. More importantly, Reward-locked beta-oscillations scaled with expected reward value on specific trial types and in a behaviorally relevant way across tasks. Oscillatory signatures of reward processing were observed throughout the cortico-striatal network including electrodes placed in orbitofrontal cortex, anterior insula, medial prefrontal cortex, ventral striatum, and amygdala. These data suggests that beta-oscillations reflect learned reward value in a distributed network, and this may serve as a stable and robust bio-marker for future studies.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.