Visual perception as a window onto prediction anomalies in schizophrenia

视觉感知作为精神分裂症预测异常的窗口

• 批准号: 10558745
• 负责人: Katharine Natasha Thakkar
• 金额: $ 43.93万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10558745
• 关键词: Animals; Bayesian Analysis; Bayesian Modeling; Behavior; Behavioral Paradigm; Belief; Biological; Brain; Chronic; Chronic Schizophrenia; Clinical; Cognitive; Complex; Computer Models; Conscious; Data; Delusions; Dependence; Detection; Distress; Environment; Face; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Hallucinations; Human; Image; Individual; Link; Literature; Measures; Methodology; Modeling; Motivation; Nature; Neurons; Neurophysiology - biologic function; Onset of illness; Patients; Pattern; Perception; Perceptual disturbance; Pharmacology; Population; Process; Property; Psyche structure; Psychotic Disorders; Reporting; Schizophrenia; Sensory; Severities; Statistical Models; Structure; Symptoms; Synapses; System; Testing; Update; Vision; Visual; Visual Cortex; Visual Perception; Visual System; Volatilization; Work; clinically relevant; clinically significant; disorder risk; effective therapy; experience; experimental study; functional outcomes; indexing; insight; neural; neuroadaptation; neuroimaging; neuromechanism; neurophysiology; neurotransmission; phenomenological models; psychotic-like experiences; response; schizophrenia spectrum disorder; sensory input; theories; tool; visual adaptation; visual processing; visual stimulus

Abstract What are the aberrant brain processes that lead to symptoms of schizophrenia—to the distressing and disabling hallucinations, delusions, disorganization, and loss of motivation? Answering this question is central to developing targeted and effective treatments. Prominent mechanistic accounts of schizophrenia hinge on the notion of the brain as a “prediction machine” that maintains and updates a mental model of the probabilistic structure of the environment, which it uses in concert with sensory input to make sense of the world. Schizophrenia has been associated with an abnormality in this interpretative process, leading to abnormal perceptions, beliefs, and behaviors. What is currently lacking, however, is an empirical basis for delineating the specific nature of prediction abnormalities in schizophrenia, at the level of both behavior and brain. The general aim of the current project is to understand how visual perception is influenced by experience-based predictions in the schizophrenia. The visual system is a uniquely suited system for understanding prediction abnormalities in clinical populations for several reasons. First, robust behavioral paradigms can quantify the influence of predictions on visual perception. Second, parallel neurophysiology and neuroimaging work provides a basis for interpretation at the level of neuronal populations. Understanding these abnormalities in vision, then, may provide a scalable framework for understanding symptoms more generally. Furthermore, visual distortions are observed in schizophrenia before illness onset, and they relate to important clinical factors. Understanding prediction in the visual system can help explain specific clinical phenomenology. The current project proposes to investigate the influence of prior experience on visual processing by measuring visual aftereffects and their neural concomitants. Aftereffects are illusory perception of the “opposite” that arises after prolonged viewing of a image. Characterizing visual aftereffects in the schizophrenia spectrum can provide important insights into the computational and biological underpinnings of abnormal prediction. The substantial existing literature on the neural and computational origins of aftereffects means that different aftereffects can reveal at what level of the sensory hierarchy, and in which specific component processes, prediction abnormalities emerge. Specific study goals are to 1) characterize visual aftereffects in the schizophrenia spectrum; 2) determine whether they are present across illness stages and in individuals at-risk for the disorder; 3) evaluate the clinical relevance of altered visual aftereffects; and 4) to measure neuroimaging concomitants of altered aftereffects and link them to computational model components. Visual aftereffects can provide a tool with which to empirically test a canonical computational mechanism as the basis for both altered visual experience and symptom genesis generally, namely an inappropriate use of prior experience in generating the contents of consciousness.

摘要 什么是异常的大脑过程，导致精神分裂症的症状-痛苦和 使人丧失能力的幻觉，妄想，混乱，和失去动力？探讨这个问题是核心 发展出有针对性的有效治疗方法精神分裂症的主要机制解释 大脑作为“预测机器”的概念，它维护和更新概率的心理模型， 环境的结构，它与感官输入一起使用，使世界有意义。 精神分裂症与这种解释过程的异常有关，导致异常 感知、信念和行为。然而，目前缺乏的是一个经验基础， 精神分裂症在行为和大脑水平上预测异常的特定性质。 当前项目的总体目标是了解视觉感知如何受到 基于经验的预测视觉系统是一个独特的适合系统， 了解临床人群的预测异常有几个原因。第一，稳健的行为 范例可以量化预测对视觉感知的影响。第二，平行神经生理学和 神经成像工作为在神经元群体水平上的解释提供了基础。了解这些 因此，视觉异常可能为更普遍地理解症状提供一个可扩展的框架。 此外，在精神分裂症发病前观察到视觉扭曲，它们与重要的 临床因素。理解视觉系统中的预测可以帮助解释特定的临床现象学。 目前的项目建议调查的影响，先前的经验对视觉处理， 测量视觉后遗症及其神经伴随物。后效是一种错觉， 在长时间观看图像后出现的“相反”。视觉后效的特征 精神分裂症谱可以提供重要的见解的计算和生物学基础， 异常预测关于后效的神经和计算起源的大量现有文献 这意味着不同的后效可以揭示在感觉层次的哪个层次，以及在哪个特定层次。 组件过程中，预测异常出现。具体的研究目标是：1）表征视觉 精神分裂症谱中的后遗症; 2）确定它们是否存在于疾病的各个阶段， 3）评估视觉后遗症改变的临床相关性;以及4） 测量改变的后遗症的神经成像伴随物，并将它们与计算模型组件联系起来。 视觉后效可以提供一种工具，用它来经验性地测试一种规范的计算机制， 一般来说，改变视觉体验和症状发生的基础，即不适当地使用 产生意识内容的先验经验。