MMN deficits in psychotic disorders: Neurobiological and computational mechanisms and predictive utility

项目摘要 精神障碍（PD）影响3.5%的人口。它们是有害的、慢性的，并导致减少 伤残调整寿命年和过早死亡。PD的评估和治疗进展缓慢， 需要识别和机械理解的生物标志物，症状的出现和持续。 失匹配负波（MMN）是一种由期望违背引起的事件相关电位 作为PD的生物标志物：其可靠的减少与精神病和认知症状相关。然而，在这方面， MMN与这些症状或指数相关的机制，随着时间的推移， 这限制了这种效应的临床应用。预测编码理论（PC）试图通过建立 神经生物学和临床现象之间的联系。PC假定大脑功能的层次组织 由此感觉输入和先前期望（先验）被整合以告知感知。当输入发散时 根据先验知识，这种不匹配会产生预测误差（PE），从而导致信念更新。体育赤字 被认为可以解释精神病症状和认知功能的重要方面;然而，经验 支持是稀疏的，以及这种PE减少是否与对先验的过度依赖或过度依赖有关。 还不清楚。计算工作表明，MMN是感觉PE的神经表征， 理解MMN和症状之间的机械联系的框架。此外，基于 有效的连接性是一种关键的神经信息路由机制，通过该机制，自上而下的先验信息和 自下而上的PE被传送，并且可以使用MMN下的振荡活性来量化。PE没有 从MMN在PD，和有效的连接性基础MMN减少没有得到很好的理解。 重要的是，情绪在精神病症状的发展和维持中也起着关键作用， 会损害认知量化PE从情绪MMN（eMMN）允许我们阐明的机制， 情绪加剧症状，贷款解释价值和效用，在这方面的研究。最后， 了解疾病的过程是至关重要的通知干预，MMN和PE的预测效用 疾病的轨迹是未知的。因此，该项目旨在阐明神经生物学和计算 MMN减少指标PD临床和认知症状随时间推移的机制。本研究 利用一个大型（N=220）的PD跨诊断队列和一个从未有过精神病的组（N=252）， 超过3个时间点。计算模型将用于从MMN和eMMN导出PE，并且有效地 连接性来表征前馈（PE）和反馈（先验）振荡信息流。总的来说，这 这项研究使用了一个重复性很好的、基于生物学的测量和理论框架来阐明计算 和神经生物学机制的基础精神病和认知症状，并提供深入了解他们的 轨迹这个项目将有利于培训申请人在1）计算建模，2）时间频率 分析和有效的连接，3）纵向分析，以及4）专业发展活动。

PROJECT SUMMARY Psychotic disorders (PD) affect 3.5% of the population. They are impairing, chronic, and result in reduced disability-adjusted life years and premature death. Advances in assessment and treatment of PD are slowed by the need for identification and mechanistic understanding of biomarkers by which symptoms arise and persist. The mismatch negativity (MMN), an event-related potential elicited by expectancy violations, has been proposed as a biomarker in PD: its reliable reduction is associated with both psychotic and cognitive symptoms. However, mechanisms through which MMN is associated with such symptoms or indexes their course over time are not clear, limiting the clinical utility of this effect. Predictive coding theory (PC) attempts to rectify this by establishing links between neurobiological and clinical phenomena. PC posits a hierarchical organization of brain function whereby sensory input and prior expectations (priors) are integrated to inform perception. When inputs diverge from priors, the mismatch gives rise to prediction error (PE), which contributes to belief updating. Deficits in PE are thought to explain important aspects of psychotic symptoms and cognitive functioning; however, empirical support is sparse, and whether such PE reductions are associated with overly strong or weak reliance on priors is not clear. Computational work suggesting that MMN is a neural representation of sensory PE provides a framework for understanding mechanistic links between MMN and symptoms. Furthermore, oscillatory-based effective connectivity is a critical neural information-routing mechanism through which top-down priors and bottom-up PEs are conveyed, and can be quantified using oscillatory activity underlying MMN. PE has not been derived from MMN in PD, and effective connectivity underlying MMN reduction is not well understood. Importantly, emotion also plays a critical role in the development and maintenance of psychotic symptoms and impairs cognition. Quantifying PE from emotion-MMN (eMMN) allows us to elucidate mechanisms through which emotion exacerbates symptoms, lending explanatory value and utility to research in this area. Finally, though knowledge regarding course of illness is crucial to informing intervention, the utility of MMN and PE in predicting illness trajectories is unknown. Thus, this project aims to elucidate the neurobiological and computational mechanisms through which MMN reduction indexes clinical and cognitive symptoms in PD over time. This study capitalizes on a large (N=220), transdiagnostic cohort with PD and a never-psychotic group (N=252), followed over 3 timepoints. Computational models will be used to derive PE from MMN and eMMN, and effective connectivity to characterize feedforward (PE) and feedback (priors) oscillatory information flow. Overall, this study uses a well-replicated, biologically-based measure and theoretical framework to elucidate computational and neurobiological mechanisms underlying psychotic and cognitive symptoms and provide insight into their trajectories. This project will facilitate training for the applicant in 1) computational modeling, 2) time-frequency analyses and effective connectivity, 3) longitudinal analyses, and 4) activities for professional development.