The ventral tegmental area (VTA), a brain region located on the floor of the midbrain, is composed of 60-65% dopamine (DA) neurons, 30-35% gamma aminobutyric acid (GABA) neurons and ~5% glutamate neurons, and is involved in a wide range of functions. The role of VTA DA neurons in motivated behavior has been extensively studied, and the signaling of VTA DA neurons has been canonically viewed as coding for the prediction error of reward. However, the molecular distinctive neuron populations and the complexity of structural connections of both local VTA and long range projections influence dynamic behavioral states, and require research approaches that probe multiple aspects of possible VTA functions to dissect the role of the VTA in different behaviors.
VTA DA neurons respond robustly to aversive and novelty stimuli, and controlling DA neuron activity produces behavioral disruptions on aversion processing and novelty detection. Because excitation of local GABA neurons is inhibitory to DA neurons, we hypothesized that activating VTA GABA neurons would enhance anxiety related behavior and disrupts novelty recognition. Activation of VTA GABA neurons was achieved by selective expression of excitatory DREADDs (Designer Receptors Exclusively Activated by Designer Drugs) and clozapine-N-oxide (CNO) administration during a marble burying test (MBT), open field test (OFT) and a novel object recognition (NOR) task. No significant change was found in the percentage of buried marbles or time spent in the center of the open field, indicating VTA GABA stimulation has limited effect on anxiety related behavior. In addition, there was no significant differences in novel object interaction time, which suggests VTA GABA stimulation does not change motivation to explore novel objects. Next, we sought to establish an optogenetic platform with behavior tracking in order to assess how VTA DA and GABA neurons influence positive reinforcement in an optical self-stimulation paradigm. In our preliminary experiments, optogenetic activation of VTA DA was achieved by selective expression of channelrhodopsin-2 (ChR2) and blue light stimulation, which supported intracranial self-stimulation without altering locomotor activity. Thus optogenetic control was sufficient to drive reinforcing behavior. This platform provides the basis for future studies to optically manipulate VTA DA and GABA neurons during operant tasks.
Even within the same region, individual neurons differ greatly in morphological and electrophysiological properties, which contributes to the functional diversity of neurons. Neuron classification is required to analyze neuron functions in a systemic and reproducible way. To categorize neurons with similar electrophysiological properties and to study the morphological and functional correlates of neurons, we performed unsupervised and supervised classification on a publicly available large scale neuron electrophysiology database by machine learning (ML). We identified multiple neuron subgroups that are discriminative by electrophysiology profiles, and successfully characterized two dendrite morphological features by electrophysiology profiles. ML provides a powerful tool for systemic neuron classification, and will provide insight into the functional components of VTA when more detailed VTA neuron profiles are acquired in future studies.
|Advisor:||Bass, Caroline E.|
|Commitee:||Li, Jun-Xu, Paul, Matthew|
|School:||State University of New York at Buffalo|
|School Location:||United States -- New York|
|Source:||MAI 82/3(E), Masters Abstracts International|
|Keywords:||Ventral tegmental area gaba neutrons, Anxiety detection, Novelty detection, Neuron dendrite types, Electrophysiological properties|
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