Uncovering the neural architecture underlying decisions abstracted from movements

揭示从运动中抽象出来的决策背后的神经架构

• 批准号: 10337282
• 负责人: Stephan Bickel
• 金额: $ 41.09万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-13 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10337282
• 关键词: Address; Affect; Animals; Architecture; Area; Behavior; Biophysics; Brain; Brain Diseases; Brain region; Characteristics; Classification; Cognition; Cognitive deficits; Complex; Coupled; Data; Decision Making; Development; Diagnosis; Disease; Divorce; Electrocorticogram; Electrophysiology (science); Epilepsy; Event-Related Potentials; Exhibits; Eye Movements; Functional Magnetic Resonance Imaging; Future; Goals; Hand; Health; Homologous Gene; Human; Impaired cognition; Investigation; Joints; Knowledge; Lateral; Link; Macaca; Macaca mulatta; Mathematics; Measures; Medial; Methodology; Methods; Modality; Modeling; Monkeys; Motor; Movement; Neural Network Simulation; Neurons; Parietal; Parietal Lobe; Patients; Physiological; Play; Population; Prefrontal Cortex; Preparation; Process; Reporting; Research; Response to stimulus physiology; Role; Saccades; Scalp structure; Sensory; Signal Transduction; Source; Techniques; Testing; Time; Ursidae Family; analog; base; biophysical model; cognitive capacity; cognitive function; flexibility; innovation; insight; intraparietal sulcus; mathematical model; multimodality; neural circuit; neural network; neuroimaging; neuromechanism; neurophysiology; neurosurgery; neurotransmission; nonhuman primate; novel; relating to nervous system; response; temporal measurement; thought control

Project Abstract Decision making is a core component of normal and abnormal cognitive function. Understanding the neural mechanisms of decision-making will lead to advances in the diagnosis, classification and future treatments of disorders affecting thought and control. Mathematical models of the decision process, based on bounded evidence accumulation, have been developed over decades and are being increasingly leveraged to gain deeper insights into the origins of cognitive deficits arising from a range of brain disorders. However, major gaps remain in our understanding of the neural mechanisms responsible for decision-making, thereby limiting the validity and utility of the models. A successful line of research on perceptual decision-making has established that neurons in the parietal and prefrontal cortex of the rhesus monkey (Macaca mulatta) encode the accumulating evidence bearing on the alternatives. These observations are mainly from neurons in areas of the macaque cortex that are associated with preparation of the actions (e.g. hand or eye movements) for reporting the decision alternatives. However, decisions are often formed without knowledge of what actions they might call for, and under such conditions, effector-selective neural activity does not appear to reflect accumulation dynamics. Recent studies, have identified a novel ‘abstract’ decision signal in non-invasive electrophysiological (EEG) recordings from human decision makers. The signal, termed the central parietal positivity (CPP), represents the accumulation of evidence for decisions irrespective of the sensory or motor requirements of the task, hence the designation, abstract. The neural circuits that give rise to the CPP are likely to explain the capacity to flexibly link decisions to various actions depending on context and goals. However, because the signal has thus far only been observed in EEG recordings from humans, its neural basis is unknown. The proposed aims will (1) establish the neural underpinnings of the CPP by establishing its analogues in single-neuron, multi-neuron, local field potentials and EEG of the macaque and (2) localizing its source in humans through the use of neuroimaging, and electrocorticography (ECoG) from patients undergoing neurosurgery. Both aims draw on an integrated computational effort that combines biophysical modeling, neural networks, and mathematical characterization of the decision process. The knowledge gained through these investigations will increase our understanding of core cognitive capacities whose deficiency contributes to major brain disorders while bridging long-standing methodological gaps in human versus non-human animal investigations.

项目摘要 决策是正常和异常认知功能的核心组成部分。了解 决策的神经机制将导致诊断、分类和未来的进步 治疗影响思维和控制的疾病。决策过程的数学模型， 基于有限的证据积累，已经发展了数十年，并且正在被 越来越多地利用来更深入地了解一系列认知缺陷的根源 脑部疾病。然而，我们对神经机制的理解仍然存在重大差距 负责决策，从而限制了模型的有效性和实用性。一条成功的线路 对知觉决策的研究表明，顶叶和前额叶的神经元 恒河猴（Macaca mulatta）的皮质编码了与 替代方案。这些观察结果主要来自猕猴皮层区域的神经元，这些区域 与报告决定的行动准备（例如手或眼的运动）相关 替代方案。然而，决策往往是在不知道他们可能采取什么行动的情况下做出的 因为，在这种条件下，效应器选择性神经活动似乎并不反映积累 动力学。最近的研究在非侵入性中发现了一种新颖的“抽象”决策信号 来自人类决策者的电生理（EEG）记录。该信号称为中央信号 顶叶积极性（CPP），代表决策证据的积累，无论 任务的感觉或运动要求，因此称为抽象。神经回路 CPP 的产生可能解释了将决策与各种行动灵活联系起来的能力 取决于背景和目标。然而，由于迄今为止仅在脑电图上观察到该信号 来自人类的录音，其神经基础尚不清楚。拟议的目标将（1）建立神经网络 通过在单神经元、多神经元、局部领域建立类似物来支撑 CPP 猕猴的电位和脑电图，以及（2）通过使用将其来源定位于人类 接受神经外科手术的患者的神经影像学和皮质电图（ECoG）。两个目标 利用综合计算工作，结合生物物理建模、神经网络和 决策过程的数学表征。通过这些获得的知识 调查将增加我们对核心认知能力的理解，其缺陷会导致 解决主要的脑部疾病，同时弥合人类与非人类之间长期存在的方法论差距 动物调查。