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: 10376280
• 负责人: Katharine Natasha Thakkar
• 金额: $ 46.26万
• 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/10376280
• 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; Lead; Link; Literature; Measures; Methodology; Modeling; Motivation; Nature; Neurons; Neurophysiology - biologic function; Onset of illness; Overweight; 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 structure; Work; base; brain behavior; clinically relevant; clinically significant; disorder risk; effective therapy; experience; experimental study; functional outcomes; indexing; insight; neuroadaptation; neuroimaging; neuromechanism; neurophysiology; neurotransmission; phenomenological models; psychotic-like experiences; relating to nervous system; 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 是什么异常的大脑过程导致精神分裂症的症状-令人痛苦和 导致幻觉、妄想、组织混乱和动力丧失？回答这个问题是关键 发展有针对性和有效的治疗方法。精神分裂症的突出机制描述取决于 大脑的概念是“预测机器”，它维护和更新概率的心理模型 环境的结构，它与感官输入相结合来理解世界。 精神分裂症与这一解释过程中的异常有关，导致异常 感知、信念和行为。然而，目前所缺乏的是一个经验基础来描述 精神分裂症预测异常的特殊性质，在行为和大脑水平上。 当前项目的总体目标是了解视觉感知是如何受到 精神分裂症患者的经验预测。视觉系统是一种唯一适合于 理解临床人群中的预测异常有几个原因。第一，强健的行为 范式可以量化预测对视觉感知的影响。第二，平行的神经生理学和 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，视力异常可能为更广泛地理解症状提供一个可扩展的框架。 此外，精神分裂症患者在发病前就观察到视觉扭曲，它们与重要的 临床因素。了解视觉系统中的预测有助于解释特定的临床现象学。 目前的项目建议通过以下方式调查先前经验对视觉处理的影响 测量视觉后效和它们的神经伴随。后遗症是一种幻觉 在长时间观看图像后出现的“相反”。描述视觉后效的特征 精神分裂症谱系可以提供对精神分裂症的计算和生物学基础的重要见解 不正常的预测。关于后效的神经和计算起源的大量现有文献 意味着不同的后遗症可以揭示出感觉层次结构的哪个层次，以及在哪个特定的层次上 组件过程中，预测出现异常。具体的研究目标是：1)描述视觉特征 精神分裂症谱系中的后遗症；2)确定它们是否存在于疾病的各个阶段和 3)评估视力后遗症改变的临床相关性；以及4) 测量伴随改变的后遗症的神经成像，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，用来对规范的计算机制进行经验测试，如 视觉体验改变的基础和症状产生的一般基础，即不适当地使用 在产生意识内容方面的先前经验。