Dysfunctional State Representations in Psychosis: From Neurophysiology to Neuroplasticity-based Treatment

精神病中的功能障碍状态表征：从神经生理学到基于神经可塑性的治疗

• 批准号: 10377362
• 负责人: A DAVID REDISH
• 金额: $ 305.47万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10377362
• 关键词: Ablation; Affect; Behavior; Behavioral; Brain; Characteristics; Clinical; Computer Models; Data; Data Analyses; Electroencephalography; Environment; Equilibrium; Expectancy; Failure; Feedback; Functional Magnetic Resonance Imaging; Functional disorder; Generations; Heterogeneity; Human; Leadership; Learning; Mediating; Mental disorders; Methodology; Modeling; Modification; Mus; N-Methylaspartate; Neuronal Plasticity; Noise; Outcome; Participant; Patient Recruitments; Patients; Pattern; Performance; Persons; Physiology; Precision therapeutics; Process; Psychopathology; Psychoses; Randomized; Regimen; Reversal Learning; Role; Signal Transduction; Slice; Structure; Subgroup; Synapses; System; Testing; Training; Translating; Variant; animal data; base; behavior measurement; cell type; cognitive training; computational neuroscience; data acquisition; data modeling; early psychosis; experimental study; functional magnetic resonance imaging/electroencephalography; genetic manipulation; genetic variant; lens; network models; neural information processing; neurophysiology; nonhuman primate; novel; relating to nervous system; sensory system; treatment as usual

PROJECT SUMMARY: OVERALL To respond adaptively to the environment, the brain must process information to develop accurate and stable representations of the current state of the environment (“state representation”). This requires precise neural activity timing synchrony between prefrontal and sensory systems and within prefrontal networks. Our Center focuses on the unifying hypothesis that processes underlying state representation dysfunction are relevant to psychosis, providing a window into pathophysiologic heterogeneity and precision treatment. Four Projects span three species (nonhuman primates, mice, and humans) and eight methodologies (genetic manipulations, slice physiology, ensemble recordings, LFP, behavior, EEG, fMRI, cognitive training). We use a central computational perspective to translate and integrate across species and methodologies: Changes in neural information processing affect parameters underlying attractor dynamics and influence state representation processes. Such changes create observable effects in behavior and neurophysiology, which we can study through the lens of attractor network models to inform our understanding of pathophysiologic heterogeneity, clinical trajectories, and precision treatment. Each Project: 1) Uses the same behavioral tasks to probe components of state representation across species and experiments; 2) Accesses parallel neurophysiologic metrics, with a focus on neural system activity timing, excitatory-inhibitory balance, and noise; 3) Uses advanced data-driven causal discovery analyses to facilitate cross-paradigm integration and novel hypothesis generation. The Projects are supported by a Translational Neurophysiology Core, a Computational Core, and an Administrative Core. Aim 1 investigates behavior and neurophysiology of state representation dysfunctions characteristic of psychosis in a nonhuman primate model of prefrontal network failure in psychosis mediated through NMDA-R signaling (PROJECT 1); in mice with cell type-specific ablation of NMDA-R function and carrying psychosis- associated genetic variants (PROJECT 2); and from an EEG-fMRI study of healthy controls and people with early psychosis (PROJECT 3). Aim 2 develops attractor network models of state representation at multiple levels of detail, incorporating behavioral, synaptic, and cellular microcircuit data from animal neurophysiology studies (PROJECTS 1 & 2) to identify parameters that account for state representation dysfunctions characteristic of psychosis and the behavioral and neurophysiological observations made in humans (PROJECTS 3 & 4). Aims 3 and 4 focus on reliability and predictive significance of state representation dysfunctions in early psychosis, and precision treatment approaches targeting specific dysfunctions.

项目概要：总体 要对环境作出适应性反应，大脑必须对信息进行处理，以发展准确和稳定 环境的当前状态的表示（“状态表示”）。这需要精确的神经 前额叶和感觉系统之间以及前额叶网络内部的活动时间同步。 我们的中心专注于统一的假设，即潜在的状态表征功能障碍是 与精神病相关，为病理生理异质性和精确治疗提供了一个窗口。四 项目涵盖三个物种（非人类灵长类动物、小鼠和人类）和八种方法（遗传学）。 操作，切片生理学，整体记录，LFP，行为，EEG，fMRI，认知训练）。我们使用一个 跨物种和方法学翻译和整合的中心计算视角： 神经信息处理影响吸引子动力学和影响状态的参数 代表过程。这些变化在行为和神经生理学上产生了可观察到的影响， 可以通过吸引子网络模型的透镜来研究，以告知我们对病理生理学的理解。 异质性、临床轨迹和精确治疗。 每个项目：1）使用相同的行为任务来探测跨物种的状态表征组件 2）访问并行神经生理学度量，重点是神经系统活动定时， 兴奋抑制平衡和噪音; 3）使用先进的数据驱动的因果发现分析，以促进 跨范式整合和新假设生成。该项目由翻译支持 神经生理学核心、计算核心和管理核心。 目的1：研究状态表征功能障碍的行为和神经生理学特征， NMDA-R介导的非人灵长类精神病前额叶网络故障模型 信号传导（项目1）;在具有细胞类型特异性NMDA-R功能消融和携带精神病的小鼠中- 相关的遗传变异（项目2）;以及来自健康对照组和患有 早期精神病（项目3）。目的2开发了多个状态表示的吸引子网络模型 详细程度，结合动物神经生理学的行为，突触和细胞微电路数据 研究（项目1和2），以确定解释状态表征功能障碍的参数 精神病的特征以及对人类的行为和神经生理学观察 （项目3和4）。目标3和4关注状态表示的可靠性和预测意义 早期精神病的功能障碍，以及针对特定功能障碍的精确治疗方法。