An important part of addiction recovery is degrading high value associations between drug cues and the drugs themselves. Dopamine plays a crucial role in learning, and is specifically implicated in the prefrontal cortex (PFC) and reversal learning - learning to update and change behavior when it is no longer being rewarded. Past studies have reported elevations in dopamine during contingency reversal, but the timescale of how activity of PFC dopamine neurons maps to reversal learning remains unclear. Here we investigated the activity of PFC dopamine during reversal learning in a longitudinal fiber photometry study, recording dopamine signal on a timescale of seconds. Mice were trained on a reversal learning task where they initially learned that two of four presented odors precipitated a sucrose reward in 85% of the trials while the remaining two odors precipitated the reward for only 15% of the trials. Once the learning was stable, reward probability flipped for two odors (one 85% odor and one 15% odor) and the mice had to update their behavior to the new odor/reward structure. Fiber photometry recordings were conducted during pre-reversal, reversal, and post-reversal stages of the study. Our data replicate findings demonstrating elevated dopamine release during the reversal period, centered around the 15-85 cue. Analysis of the relationship between the dopamine signal and behavior also revealed significant cue, reward prediction error, and 15-85 reversal coding in the majority of animals, suggesting a multi-faceted role for dopamine in the PFC. Given this, dopamine in the PFC may play an important mediating role in the enhancement of associations between drugs and drug cues, but does not play a clear role in contingency degradation.

The ventral tegmental area (VTA) is a key region in the brain’s mesolimbic circuitry playing a role in regulating reward processing, aversion, motivation, and stress-related behaviors, housing both dopamine (DA) and GABA-expressing neurons. GABA neurons in the VTA form direct connections with DA neurons, modulating dopamine and influencing reward-related behaviors. This study aims to characterize two distinct VTA GABA populations marked by expression of the nociceptin gene (Pnoc) and corticotropin-releasing hormone binding protein (Crhbp). Using double transgenic mice and viral targeting, we aim to map the projection patterns of these two populations. We anticipate that Crhbp-GABA expressing populations will innervate the Ventral Pallidum and Lateral Habenula brain areas as these neurons also coexpress Vglut2, a marker for glutamate neurons which are known to project to these regions, whereas, Pnoc-GABA expressing populations might represent the local GABA population that synapses onto DA neurons within the VTA. To assess their functional role, we will optically activate these populations during a real-time place preference task (RTPT) using channelrhodopsin-2 (ChR2). We hypothesize that activation of Pnoc-GABA neurons will result in a negative valence response and support real time place aversion. On the other hand, Crhbp-GABA neurons may have a more varied effect on valence response, based on their coexpression of Vglut2, during the RTPP task. By understanding the roles of VTA GABAergic populations marked by Pnoc and Crhbp expression, we can gain insights into the neural mechanisms involved in reward processing, motivation, and stress, often dysregulated in psychiatric disorders. This understanding could ultimately inform the development of targeted therapeutic interventions for psychiatric conditions characterized by maladaptive behavioral responses to stress and other stimuli.

Fractal dimension, a measure of geometric complexity, finds application in image analysis, biology and medicine, neuroscience, geology and various other fields, yet existing methods often lack adaptability to finite data sets. Using ideas rooted in geometric measure theory, such as Hausdorff measure and Frostman’s Lemma, this research introduces a novel approach to compute fractal dimensions for finite sets, addressing limitations of traditional methods. Using Python, we developed and tested an algorithm to validate known sets such as the unit interval, square, cube, and fractal objects including the Cantor set and Sierpinski triangle. Comparative analysis was also conducted on established methods, including box-counting and correlation integral algorithms, to demonstrate the algorithm's accuracy in determining fractal dimensions. Pivoting towards data sets, we expect to use the computed fractal dimension of real data as a tool for assessing data and optimizing data compression. Our methods offer an improvement as most existing techniques use statistical methods that are limited to integer dimensions. In addition, recent studies have shown that fractal dimension values can be useful as features in machine learning. We also improve upon the calculation of the local dimension of regions in a data set, allowing for additional insights into complex data sets. This includes identifying regions of high complexity, and we expect to show that this allows for the more effective use of algorithms such as principal component analysis. All of these are increasingly important in our society due to the abundance of high-dimensional datasets in both the physical and social sciences. Overall, the benefits of studying novel ways of calculating the dimension of large data sets include efficient representation of data, improved interpretability, and decreased computational burden, as well as detecting certain features in data such as regions of high complexity.