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

项目总结 精神障碍(PD)影响3.5%的人口。它们是损害的、慢性的，并导致减少 伤残调整生命年和过早死亡。帕金森病的评估和治疗进展因 对引起症状并持续存在的生物标记物的识别和机理了解的必要性。 失配负性(MMN)是一种由违反预期行为引发的事件相关电位，已被提出 作为帕金森病的生物标记物：其可靠的降低与精神病和认知症状有关。然而， MMN与这些症状或指标随着时间的推移而变化的机制不同 明确地说，限制了这种效果的临床应用。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假设了大脑功能的分层组织 由此，感觉输入和先前的预期(先验)被结合起来，以告知知觉。当投入出现分歧时 从先验信息来看，失配会导致预测误差(PE)，从而导致信念更新。体育教育中的赤字 被认为可以解释精神病症状和认知功能的重要方面；然而，经验性的 支持是稀少的，以及这种市盈率的降低是否与对先例的过度依赖或弱依赖有关 目前还不清楚。计算工作表明MMN是感觉PE的神经表示提供了 了解MMN和症状之间的机制联系的框架。此外，基于振荡的 有效连接是一种关键的神经信息路由机制，通过它，自上而下的先验和 自下而上的PE被传递，并且可以使用MMN下面的振荡活动来量化。体育还没到过 在PD中源于MMN，而MMN减少背后的有效连通性还不是很清楚。 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 会损害认知能力。从情绪-MMN(EMMN)中量化PE使我们能够阐明 情绪加剧了症状，为这一领域的研究提供了解释价值和实用价值。不过，最终还是 有关病程的知识对于告知干预、MMN和PE在预测中的作用至关重要 疾病的轨迹是未知的。因此，本项目旨在阐明神经生物学和计算 随着时间的推移，MMN减少指示帕金森病患者临床和认知症状的机制。本研究 利用一个大型(N=220)跨诊断PD队列和一个从未患精神病的组(N=252)，紧随其后 超过3个时间点。将使用计算模型从MMN和eMMN推导出PE，并且有效 描述前馈(PE)和反馈(PRIORS)振荡信息流的连通性。总体而言，这 这项研究使用了一种重复性很好的、基于生物学的测量和理论框架来解释计算 和神经生物学机制的精神病和认知症状，并提供了洞察其 轨迹。该项目将促进培训的申请人在1)计算模型，2)时间-频率 分析和有效的联系，3)纵向分析，4)职业发展活动。

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.