Consumption of highly reinforcing drugs, such as cocaine, can result in an escalation of drug consumption. Escalation is related to the most severe consequences associated with substance use disorder (SUD), including overdose. Chronic substance use leads to neurobiological changes including the signaling of the stress-related peptide corticotropin-releasing factor (CRF). Previous work has implicated CRF dysregulation in alcohol, psychostimulant, nicotine, and opioid dependence. CRF release in extrahypothalamic regions contributes to anxiety-like symptoms of withdrawal that can motivate individuals to consume drugs. There is limited evidence addressing whether CRF receptor (CRFR) activation alters cocaine consumption in individuals who have escalated their cocaine consumption. Additionally, the majority of the previous work has primarily been conducted in male subjects. The present study examines the underlying neurobiology of escalated cocaine consumption in male and female rats. Wistar rats were trained on long access (6hr) cocaine self-administration paradigm in which a subset demonstrated an escalation in their cocaine consumption. At the end of this paradigm, rats were subject to systemic administration of one of two CRFR1 antagonists, antalarmin (25mg/kg, i.p.) or N,N-bis(2-methoxyethyl)-3-(4-methoxy-2-methylphenyl)-2,5-dimethyl-pyrazolo[1,5a]pyrimidin-7-amine (MPZP; 10 and 27.5 mg/kg, s.c.). I hypothesize, following previous work, that antalarmin and MPZP will both significantly reduce escalated cocaine consumption in rats classified as escalators but not non-escalators. These results would indicate that CRFR1 activation mediates escalated cocaine consumption in both male and female rats and may be a valuable target of clinical investigation into the neurobiological underpinnings of this dangerous facet of SUDs.

Substance use disorder (SUD) takes a tremendous toll on human life every year, which makes understanding the underlying neural circuitry behind SUD a very high priority. A key neurotransmitter in the discussion surrounding SUD is dopamine, which has been implicated in mediating reward-seeking and motivational behavior. More specifically, these behaviors are believed to be mediated by the dopaminergic projection from the ventral tegmental area (VTA) to the nucleus accumbens (NAc). Using contemporary technologies like fiber photometry paired with dopamine biosensors, live dopamine dynamics can be viewed in real time and aligned to certain behavior events collected during tasks performed by animals to further understand dopamine dynamics relating to SUD. With this in mind, we injected a cohort of male and female Wistar rats with a viral vector containing the dopamine biosensor dLight, which fluoresces when bound to dopamine, into the NAc and implanted fiber optic cannulas directly above the injection site to monitor the transmission of dopamine in real time during drug consumption. From there, we ran the cohort through a drug self administration cycle consisting of a training period, short access sessions to establish baseline drug consumption (1hr), and finally long access sessions (6hr) which is known to produce escalation of drug consumption, an SUD-like phenotype. Dopamine dynamics were recorded during several time points across this task. We then performed data analysis to assess various relationships between the behavioral and photometry data, along with immunohistochemistry to confirm the injection. Understanding the dopamine dynamics underlying drug consumption and how they change across changing behavior, such as escalation of drug consumption, is essential to building our understanding of SUD, and our current research helps to illuminate the inner workings of that relationship.

For this research, I am developing an algorithm for reconstructing particle trajectories using data from the proposed MATHUSLA (Massive Timing Hodoscope for Ultra Stable Neutral Particles) detector at CERN’s (the European Council for Nuclear Research) Large Hadron Collider. The MATHUSLA detector is specifically designed to capture LLPs (Long-Lived Particles), a class of theoretical neutral high-energy particles that are undetectable by current CERN detectors due to their large decay times, potentially emitted during particle collisions using a series of tracking layers. My objective is to identify potential LLP candidates using the data collected by these highly precise trackers. The algorithm I developed determines the most probable particle trajectories that occurred corresponding to the data collected by the trackers. The algorithm has demonstrated a path detection accuracy exceeding 90% in sample point clouds with reasonably high efficiency. Future work includes examining how to take into account and accurately detect the non-linear paths frequently taken by real-world particles, in addition to better fitting the algorithm to the more realistic and cluttered data expected from the detector. The study of LLPs is a fast growing subfield in particle physics; finally detecting and analyzing them could play a massive role in taking the field beyond the Standard Model.