Neuroplasticity-Based Treatment to Address State Representation Failures in People with Early Psychosis

基于神经可塑性的治疗来解决早期精神病患者的状态表征失败问题

• 批准号: 10597078
• 负责人: Sophia Vinogradov
• 金额: $ 40.94万
• 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/10597078
• 关键词: Address; Affect; Auditory; Auditory system; Behavioral; Characteristics; Clinical; Cognition; Data; Electroencephalography; Equilibrium; Failure; Functional disorder; Goals; Hour; Impairment; Individual; Intervention; Investigation; Maintenance; Measures; Modification; Neuronal Plasticity; Noise; Output; Parameter Estimation; Participant; Pathology; Patients; Persons; Precision therapeutics; Process; Psychoses; Randomized; Short-Term Memory; System; Testing; Training; Treatment Efficacy; auditory discrimination; cognitive performance; cognitive training; computational neuroscience; design; disability; early psychosis; efficacy study; functional magnetic resonance imaging/electroencephalography; improved; indexing; information processing; network models; neural; neurophysiology; recruit; response; treatment as usual

PROJECT SUMMARY: PROJECT 4 The purpose of PROJECT 4 is to investigate computationally-informed precision treatments to improve two forms of state representation dysfunction in early psychosis: 1) State estimation processes at the perceptual input level, which we will target through auditory discrimination training; 2) State representation stability of auditory information, which we will target through auditory working memory training. Participants will be drawn from PROJECT 3, where they will have been assessed with behavioral and EEG-fMRI measures at baseline and after 6 months of usual care, so that their initial characteristics and clinical trajectory will be known. Participants will be stratified on an EEG index of state estimation processes (fronto-parietal theta power at DPX encoding), which we posit to be present in ~60% of subjects, and randomly assigned to one of the two training strategies. Our goal is not to perform a treatment efficacy study comparing these two interventions. Rather, we seek to use predictions derived from attractor network models to test the effects of neuroplasticity-based precision treatments targeting two distinct information processing pathologies in early psychosis, with the ultimate goal of improving state representation processes and cognition. In Aim 1, we will investigate parameter changes in the fit attractor network models in each subject group, fit to DPX and Bandit Task behavioral data immediately after training and 3 months later, and we will assess whether parameter changes reflect restorative or compensatory modifications. We will also test the hypothesis that state representation processes and cognitive performance show greater improvement in subjects who received training tailored to their state estimation parameter. In Aim 2, we will examine how specific parameter changes in attractor network models relate to neurophysiological changes in measures indexing activity timing, excitatory-inhibitory balance, and system noise, in order to identify which changes are the most predictive of improved cognition. Causal discovery analyses will be employed to identify causal relationships among computational parameters, behavioral data, neurophysiologic indices, treatment assignment, and one- year clinical trajectories.

项目摘要：项目4 项目4的目的是研究计算信息的精确处理以改善两个 早期精神病中国家表示功能障碍的形式：1）感知的状态估计过程 输入水平，我们将通过听觉歧视培训来定位； 2）状态表示的稳定性 听觉信息，我们将通过听觉工作记忆训练来定位。参与者将被吸引 从项目3中，将通过行为和EEG-FMRI措施对其进行评估 经过6个月的通常护理后，它们的最初特征和临床轨迹将是 已知。参与者将按国家估计过程的脑电图指数进行分层 DPX编码的功率），我们认为，这是在约60％的受试者中存在的，然后随机分配给一个 两种培训策略。我们的目标不是进行治疗效率研究，以比较这两个 干预措施。相反，我们试图使用吸引者网络模型得出的预测来测试 基于神经可塑性的精确治疗，针对早期的两种不同信息处理病理 精神病，其最终目标是改善国家代表性过程和认知。 在AIM 1中，我们将研究每个主题组中拟合吸引子网络模型的参数变化，适合 训练后和3个月后，DPX和BUNDIT任务行为数据立即进行，我们将评估 参数变化是反映的修复或补偿性修改。我们还将检验假设 国家代表性过程和认知表现对受试者的改善有所改善 接受了针对其状态估计参数量身定制的培训。在AIM 2中，我们将研究如何具体 吸引力网络模型的参数变化与索引的神经生理学变化有关 活动时机，兴奋性抑制平衡和系统噪声，以确定哪些变化是最大的变化 预测认知的改善。因果发现分析将用于确定因果关系 在计算参数，行为数据，神经生理指数，治疗分配和一项 年临床轨迹。