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

项目总结：项目4 项目4的目的是研究计算通知的精度处理，以改进两个 早期精神病状态表征功能障碍的表现形式：1)知觉状态估计过程 输入水平，我们将通过听觉辨别训练来针对该水平；2)状态表征稳定性 听觉信息，我们将通过听觉工作记忆训练来瞄准这些信息。参赛者将被抽签 从项目3开始，他们将在基线上接受行为和EEG-fMRI测量评估 并在经过6个月的常规护理后，使其初步特征和临床轨迹 为人所知。参与者将根据状态估计过程的脑电指数(额顶区)进行分层 在DPX编码时的功率)，我们假设它存在于约60%的受试者中，并随机分配给以下其中之一 两种训练策略。我们的目标不是进行一项比较这两者的治疗效果研究 干预措施。相反，我们试图使用从吸引子网络模型得出的预测来测试 基于神经可塑性的早期针对两种不同信息处理病理的精确治疗 精神病，其最终目标是改善状态表征过程和认知。 在目标1中，我们将研究Fit吸引子网络模型在每个受试组中的参数变化，Fit to DPx和Bandit在培训后立即和3个月后的任务行为数据，我们将评估 参数更改是否反映恢复性修改或补偿性修改。我们还将检验这一假设 状态表征过程和认知表现在被试中表现出更大的改善 接收针对其状态估计参数量身定做的训练。在目标2中，我们将检查具体到什么程度 吸引子网络模型中的参数变化与测量指标中的神经生理变化相关 活动时间、兴奋-抑制平衡和系统噪声，以确定哪些变化最大 预示着认知能力的改善。将使用因果发现分析来确定因果关系 在计算参数、行为数据、神经生理学指数、治疗分配和一个- 一年的临床轨迹。

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.