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Peptidergic neurons in error computations and behavioral flexibility

错误计算和行为灵活性中的肽能神经元

基本信息

批准号：
10721319
负责人：
EMILY L SYLWESTRAK
金额：
$ 175.76万
依托单位：
UNIVERSITY OF OREGON
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-01 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10721319
关键词：
Address Affect Animals Anti-Anxiety Agents Antidepressive Agents Anxiety Area Attention deficit hyperactivity disorder Automobile Driving Axon Behavior Behavioral Brain Cells Clinical Data Decision Making Development Disease Electrophysiology (science)Elements Environment Exhibits Failure Fiber Future Genetic Genetic Markers Genetic Transcription Goals Habenula Joints Lateral Lead Learning Maps Measures Mental Depression Modeling Monitor Motivation Mus Neural Pathways Neurons Neuropeptides Organism Outcome Outcome Measure Output Pathway interactions Population Probability Psychological reinforcement Recording of previous events Research Reversal Learning Rewards Role Schizophrenia Signal Transduction Stream Stress Structure Substance P Synapses Synaptic Transmission TAC1 gene TACR1 gene Testing Theoretical model Therapeutic Typology Work antagonist cell type expectation experimental study flexibility in vivo insight maladaptive behavior neural neuropsychiatric disorder new therapeutic target optogenetics programs response side effect targeted treatment tool translational impact transmission process

项目摘要

PROJECT SUMMARY Survival in dynamic environments demands that behaviors are flexible and adaptive. An organism must make predictions about which actions lead to rewards, calculate how outcomes differ from those predictions (prediction errors), and adapt a behavioral strategy accordingly. Neurons encoding prediction errors can be found throughout many reward-related brain structures, with the highest densities in the VTA and the lateral habenula (LHb). Transcriptional analyses have found the LHb contains a diverse set of intermingled cell types, but at present, it is unknown how prediction error-encoding neurons map onto transcriptionally-defined habenular cell types. Therefore, we lack a genetic cell type framework to identify and selectively manipulate them to better understand their role in behavior. To address this need, this proposal aims to determine how Tac1LHb neurons encode reward parameters, adapt their responses in dynamic environments, and impact behavioral flexibility. Our central hypothesis is that prediction errors trigger activity in Tac1LHb neurons and promote flexible behavior. In Aim 1, we will use population level and single unit recordings to determine how different reward parameters (reward probability, reward history, reward interval, and reward size) modulate Tac1LHb activity at unexpected outcomes. In Aim 2, we will determine the circuit- and synaptic-level mechanisms by which Tac1LHb activity changes with reward history. In Aim 3, we propose to manipulate Tac1LHb activity in a reversal learning task to determine if it is necessary for behavioral flexibility, and to test a theoretical model describing the computational role of Tac1LHb activity in decision-making. The Tac1 gene encodes the neuropeptide Substance P and our proposal will determine the role of peptidergic signaling in synaptic transmission in this circuit, as well as its impact on behavior. Completion of the proposed research program will establish a cell-type resolved view of error-encoding neurons and demonstrate a key role for Tac1LHb neurons in behavioral flexibility. Deficits in behavioral flexibility are a hallmark of several neuropsychiatric disorders, including schizophrenia, ADHD, OCD, and depression. Joint information on cell typology and function has the potential to advance new therapeutic targets to treat maladaptive behavior in these disorders.

项目总结