Cortico-striatal beta oscillations as a reward-related signal.

The value associated with reward is sensitive to external factors, such as the time between the choice and reward delivery as classically manipulated in temporal discounting tasks. Subjective preference for two reward options is dependent on objective variables of reward magnitude and reward delay. Single neuron correlates of reward value have been observed in regions, including ventral striatum, orbital, and medial prefrontal cortex. Brain imaging studies show cortico-striatal-limbic network activity related to subjective preferences. To explore how oscillatory dynamics represent reward processing across brain regions, we measured local field potentials of rats performing a temporal discounting task. Our goal was to use a data-driven approach to identify an electrophysiological marker that correlates with reward preference. We found that reward-locked oscillations at beta frequencies signaled the magnitude of reward and decayed with longer temporal delays. Electrodes in orbitofrontal/medial prefrontal cortex, anterior insula, ventral striatum, and amygdala individually increased power and were functionally connected at beta frequencies during reward outcome. Beta power during reward outcome correlated with subjective value as defined by a computational model fit to the discounting behavior. These data suggest that cortico-striatal beta oscillations are a reward signal correlated, which may represent subjective value and hold potential to serve as a biomarker and potential therapeutic target.

Mapping Large-Scale Networks Associated with Action, Behavioral Inhibition and Impulsivity.

A key aspect of behavioral inhibition is the ability to wait before acting. Failures in this form of inhibition result in impulsivity and are commonly observed in various neuropsychiatric disorders. Prior evidence has implicated medial frontal cortex, motor cortex, orbitofrontal cortex (OFC), and ventral striatum in various aspects of inhibition. Here, using distributed recordings of brain activity [with local-field potentials (LFPs)] in rodents, we identified oscillatory patterns of activity linked with action and inhibition. Low-frequency (δ) activity within motor and premotor circuits was observed in two distinct networks, the first involved in cued, sensory-based responses and the second more generally in both cued and delayed actions. By contrast, θ activity within prefrontal and premotor regions (medial frontal cortex, OFC, ventral striatum, and premotor cortex) was linked with inhibition. Connectivity at θ frequencies was observed within this network of brain regions. Interestingly, greater connectivity between primary motor cortex (M1) and other motor regions was linked with greater impulsivity, whereas greater connectivity between M1 and inhibitory brain regions (OFC, ventral striatum) was linked with improved inhibition and diminished impulsivity. We observed similar patterns of activity on a parallel task in humans: low-frequency activity in sensorimotor cortex linked with action, θ activity in OFC/ventral prefrontal cortex (PFC) linked with inhibition. Thus, we show that δ and θ oscillations form distinct large-scale networks associated with action and inhibition, respectively.

Open-source raspberry Pi-based operant box for translational behavioral testing in rodents.

BACKGROUND: Rodents have been used for decades to probe neural circuits involved in behavior. Increasingly, attempts have been developed to standardize training paradigms across labs; and to use visual/auditory paradigms that can be also tested in humans. Commercially available systems are expensive and thus do not scale easily, and are not optimized for electrophysiology. NEW METHOD: Using the rich open-source technology built around Raspberry Pi, we were able to develop an inexpensive (<$1000) visual-screen based operant chamber with electrophysiological and optogenetic compatibility. The chamber is operated within MATLAB/Simulink, a commonly used scientific programming language allowing for rapid customization. RESULTS: Here, we describe and provide all relevant details needed to develop and produce these chambers, and show examples of behavior and electrophysiology data collected using these chambers. We also include all of the tools needed to allow readers to build and develop their own boxes (CAD models for 3D printing and laser-cutting; PCB-board design; all bill of materials for required parts and supplies, and some examples of Simulink models to operate the boxes). COMPARISON WITH EXISTING METHODS: The new boxes are far more cost-effective than commercially available environments and allow for the combination of automated behavioral testing with electrophysiological read-outs with high temporal precision. CONCLUSION: These open-source boxes can be used for labs interested in developing high-throughput visual/auditory behavioral assays for ∼ 10th the cost of commercial systems.