STATISTICAL MODEL FOR THE PREDICTION OF PREFRONTAL CORTEX NEURONAL SIGNALING

Open Access
Author:
Bourcier, Alexandre Jose
Area of Honors:
Biobehavioral Health
Degree:
Bachelor of Science
Document Type:
Thesis
Thesis Supervisors:
  • Nicole A Crowley, Thesis Supervisor
  • Helen Marie Kamens, Honors Advisor
Keywords:
  • Prefrontal Cortex
  • AUD
  • Anxiety Disorders
  • Statistical Model
  • Neural Signaling
  • Precision Medicine
Abstract:
Alcohol Use Disorder (AUD) is a public health issue with high costs to the United States. Though no comprehensive model of why individuals develop AUD exist, a combination of genetic, environmental, and dysregulation of neuronal signaling are thought to play a role. In addition to the lack of clear understanding of the causes of AUD, there is a desperate need for targeted treatment through behavioral and pharmaceutical approaches to treat this devastating disorder. The behavioral predictors of neural states in AUD are mostly studied through univariate analysis in varied AUD animal models instead of recognizing the dynamic relationship of behavior and neural signaling through multivariate analysis. Therefore, this project investigated the neurophysiological basis of anxiety-like behavior, via the development of a multivariate statistical and behavioral model capable of predicting the neuronal signaling underlying high and low anxiety behaviors. In future experiments we hope to expand this to high- and low- alcohol binge drinking mice. A particular region of interest for our model is the prefrontal cortex (PFC), in which layers 2/3 pyramidal neurons have reduced GABAergic inputs following varied models of ethanol exposure. This project was divided into three parts: 1) Behavioral experiments to measure for anxiety-like behavior, 2) Electrophysiological measure of the mPFC, 3) Statistical analysis for correlations between behavior and brain physiology. Behavioral experiments include the use of Elevated Plus Maze (EPM), Open Field (OF), and Sucrose Preference (SP) tests on C57BL/6J male and female mice. Following behavioral assessments, whole cell patch clamp electrophysiology was performed to assess synaptic drive in the prelimbic cortex (a measurement of overall glutamate and γ-aminobutyric acid, GABA, balance). Then, in collaboration with The Department of Human Development and Family Studies, a linear regression was used to identify correlations between behavior and brain physiology, as well as to identify the key behaviors which best predict anxiety-like behavior. The results showed correlations between electrophysiology and behavior observed in OF, but not SP or EPM. The correlations were observed between spontaneous inhibitory postsynaptic current frequency and distance in OF, as well as spontaneous excitatory postsynaptic current amplitude and velocity in OF. This finding is the first step towards the creation of a statistical model for the prediction of neural signaling in the PFC.