CRCNS: Theory and Experiments to Elucidate Neural Coding in the Reward Circuit

CRCNS：阐明奖励回路中神经编码的理论和实验

基本信息

批准号：
10225560
负责人：
Daniela Witten
金额：
$ 23.59万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-15 至 2023-07-31
项目状态：
已结题

项目摘要

Dopamine (DA) neurons are fundamental to many aspects of behavior, and dysfunction of the DA system contributes to a wide range of disorders, including drug addiction. How does DA contribute to such a diversity of functions and dysfunctions? Part of the answer may relate to recent discoveries that DA neurons respond to a wide range of behavioral variables - not only to reward and reward-predicting cues, as traditionally examined, but also to other variables including position, movement, and behavioral choices. However, to date, the relative contributions of these behavioral variables to DA responses have not been examined quantitatively, in part because cellular resolution DA recordings have not been performed in behavioral settings with sufficient complexity and quantification to examine these diverse variables simultaneously. To address this gap, we propose to perform two-photon imaging from ensembles of midbrain DA neurons and their target neurons as mice learn to perform a complex decision-making task. However, characterizing the relationship between neural activity and behavior in this complex dataset presents a number of statistical challenges. These include the autocorrelation of the calcium indicator, the correlation between behavioral variables, the possibility of changes in the relationship between neural activity and behavior over time, and the need to leverage the availability of simultaneously measured neurons in order to improve statistical efficiency. Thus, we propose a suite of new statistical tools to address all of these challenges. These tools will be broadly applicable to the analysis of datasets throughout the addiction circuits (and the nervous system more broadly).

多巴胺（DA）神经元是行为许多方面的基础，DA系统的功能障碍会导致多种疾病，包括药物成瘾。 DA如何促进如此多样的功能和功能障碍？答案的一部分可能与最近发现的DA神经元对广泛的行为变量做出反应有关 - 不仅是为了奖励和奖励预测的提示，如传统上所检查的，还与其他变量有关，包括位置，运动和行为选择。但是，迄今为止，这些行为变量对DA响应的相对贡献尚未进行定量检查，部分原因是在具有足够的复杂性和量化的行为环境中尚未进行细胞分辨率DA记录，以同时检查这些不同的变量。为了解决这一差距，我们建议从中脑DA神经元的集合及其目标神经元进行两光子成像，因为小鼠学会执行复杂的决策任务。但是，表征该复杂数据集中的神经活动与行为之间的关系提出了许多统计挑战。其中包括钙指标的自相关，行为变量之间的相关性，随着时间的推移神经活动与行为之间关系变化的可能性以及利用同时测量神经元的可用性，以提高统计效率。因此，我们提出了一套新的统计工具来应对所有这些挑战。这些工具将广泛地适用于整个成瘾电路（以及更广泛的神经系统）的数据集分析。