Closed loop decoding and control of Orbitofrontal Cortex and caudate nucleus ensembles during decision-making

决策过程中眶额皮层和尾状核群的闭环解码和控制

• 批准号: 9469261
• 负责人: Nina Lopatina
• 金额: $ 1.76万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-07 至 2018-01-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9469261
• 关键词: Algorithms; Animals; Behavior; Behavioral; Brain region; Choice Behavior; Classification; Corpus striatum structure; Cues; Data; Decision Making; Detection; Development; Discriminant Analysis; Discrimination; Discrimination Learning; Disease; Electrophysiology (science); Extinction (Psychology); Functional disorder; Grant; Human; Impairment; Investigation; Label; Measures; Methods; Monkeys; Mood Disorders; Motor; Neurons; Neuropsychology; Obsessive-Compulsive Disorder; Operant Conditioning; Outcome; Output; Patients; Pharmacologic Substance; Play; Population; Primates; Probability; Process; Property; Psychiatry; Reaction Time; Reversal Learning; Rewards; Rodent; Role; Schizophrenia; Signal Transduction; Stimulus; Symptoms; Testing; Time; Training; Translating; base; behavior measurement; caudate nucleus; classical conditioning; cognitive process; heavy nuclei; neuroimaging; neurophysiology; neuropsychiatric disorder; novel; novel strategies; novel therapeutics; outcome prediction; relating to nervous system; response

Project Summary/Abstract Dysfunction of fronto-striatal circuits involving orbitofrontal cortex (OFC) and caudate nucleus (CN) have been implicated in a wide array of neuropsychiatric disorders, including mood disorders [1-5], schizophrenia [6-9], and obsessive-compulsive disorder [10-15]. As development of novel pharmaceutical treatments for these disorders has stalled [16-18], focus has shifted to novel approaches, such as computational psychiatry [19], which seeks to explain neuropsychiatric diseases in terms of underlying computational processes. Several neuroimaging studies suggest that negative symptoms in schizophrenic patients are correlated with disruptions in fronto-striatal circuits involved in expected value signaling [20-22]. The current grant aims to understand which computations occur in the OFC-caudate cortico-striatal circuit as these regions translate reward- predictive cues into a decision. The proposed studies utilize electrophysiological and neural decoding methods during a behavioral task in which subjects choose between two alternatives. Our hypothesis is that, during binary choices, CN acts to integrate evidence for one or other choice response based on input from OFC ensembles as they vacillate between decodable states representing the two options. Once this evidence reaches some criterion, the choice response is implemented. We will test our hypothesis with the following two specific aims: Aim 1. Interaction between OFC and CN: translating value into action. We plan to record simultaneously from ensembles of OFC and CN neurons and use decoding algorithms to examine how the dynamics of the putative choice response signal in CN relates to OFC vacillation representing the two alternative outcomes. The value of these outcomes must be derived from two outcome properties: reward amount and reward probability. We will utilize GLM-based multi- label classification methods to examine how this value is represented, and linear discriminant analysis (LDA) to decode how a choice is signaled. Aim 2. Causal manipulation of choice response through closed-loop control. Our second aim focuses on testing the causal role of the computations output by OFC state fluctuations and CN integration in decision-making. We plan to decode OFC vacillation in real time and instruct the animal to make its response when the decoder is consistent with OFC representing either the high or low value option, and measure the behavioral effect of this manipulation on choice responses and reaction times. In other words, we will use the output of the OFC decoder to control the timing of the animal’s choice response, effectively closing the loop between neural activity and behavior.

项目概要/摘要 涉及眶额皮质 (OFC) 和尾状核的额纹状体回路功能障碍 (CN) 与多种神经精神疾病有关，包括情绪障碍 [1-5]、精神分裂症[6-9]和强迫症[10-15]。随着小说的发展 这些疾病的药物治疗已陷入停滞 [16-18]，焦点已转向新药 方法，例如计算精神病学[19]，旨在解释神经精神病学 基础计算过程方面的疾病。多项神经影像学研究表明 精神分裂症患者的阴性症状与额纹状体的破坏相关 涉及期望值信令的电路[20-22]。目前的赠款旨在了解哪些 计算发生在 OFC-尾状皮质-纹状体回路中，因为这些区域翻译奖励- 做出决定的预测线索。拟议的研究利用电生理学和神经学 受试者在两种选择之间进行选择的行为任务期间的解码方法。 我们的假设是，在二元选择过程中，CN 会整合支持一种或另一种选择的证据 当 OFC 整体在可解码状态之间摇摆时，基于来自 OFC 整体输入的响应 代表两个选项。一旦该证据达到某个标准，选择响应是 实施的。我们将通过以下两个具体目标来检验我们的假设： 目标 1. OFC 与 CN 互动：将价值转化为行动。我们计划记录 同时从 OFC 和 CN 神经元的集合中提取数据，并使用解码算法来检查 CN 中假定选择响应信号的动态如何与 OFC 波动相关 代表两种可供选择的结果。这些结果的价值必须源自 两个结果属性：奖励金额和奖励概率。我们将利用基于 GLM 的多 标签分类方法来检查该值是如何表示的，以及线性判别法 分析（LDA）来解码选择的信号方式。 目标 2. 通过闭环控制对选择响应进行因果操纵。我们的第二个目标 重点测试 OFC 状态波动和 CN 计算输出的因果作用 决策中的整合。我们计划实时解码 OFC 波动并指导 当解码器与代表任一的 OFC 一致时，动物做出响应 高价值或低价值期权，并衡量这种操纵对选择的行为影响 反应和反应时间。换句话说，我们将使用OFC解码器的输出来控制 动物选择反应的时间，有效地闭合神经活动之间的循环 和行为。