Neural mechanisms of sensory predictions in schizophrenia with hallucinations

描述（由申请人提供）：精神分裂症中的活动性精神病是世界上最严重和最繁重的医疗条件之一。然而，这种障碍的精神病症状的机制，如幻觉（即，在缺乏外部感官刺激的情况下的异常感知），仍然是难以捉摸的。这个K23申请提供了一个研究和培训计划，将支持申请人成为美国国立卫生研究院资助的独立研究者，专注于功能神经影像学在精神分裂症精神病症状研究中的应用。本应用程序中的活动建立在候选人之前的培训基础上，并设置在一个资源丰富的环境中，这将促进他在以下方面的专业知识的发展：(1)功能磁共振成像（fMRI）研究的高级分析方法和研究行为；(2)计算神经科学；(3)感知与认知研究；(4)精神分裂症的病理生理及临床评价；(5)在针对弱势群体的科学研究中采取负责任和合乎道德的行为。结合功能磁共振成像（fMRI）和计算模型，目前的研究计划旨在(1)定义精神分裂症中产生幻觉的神经机制；(2)告知基于预测编码的幻觉计算模型的发展，这是一个经验验证的理论框架，支持感觉系统在学习和预测外部环境规律中的作用。最重要的假设是，基于异常预测的感觉皮质功能衰减导致感觉皮质过度活动，从而产生幻觉。为了验证这一假设，本研究将采用一种新的言语辨别功能磁共振成像范式，两组精神分裂症患者，一组有活跃的、频繁的听觉言语幻觉，一组没有明显的幻觉史，第三组是健康对照。这种设计将允许测试感觉预测编码机制的功能障碍与精神分裂症患者的在线幻觉体验之间的直接联系，从而将告知该疾病精神病症状的神经生物学基础。总之，这个培训和研究计划将促进候选人过渡到一个独立的研究生涯，并将有助于确定难治性精神病的新治疗靶点。相关性：预测编码框架和基于模型的fMRI在精神病症状研究中的新应用将为精神病症状的产生机制提供新的思路，从而填补精神分裂症研究的重要空白。该项目将有助于开发一种解释幻觉的模型，该模型可用于为未来在人类和非人类动物模型中进行的神经科学研究产生特定的、可测试的假设，并发现可能通过学习或药物治疗来改变的新目标（感觉预测缺陷）。

DESCRIPTION (provided by applicant): Active psychosis in schizophrenia is among the most severe and burdensome medical conditions worldwide. However, the mechanisms of psychotic symptoms in this disorder, such as hallucinations (i.e., abnormal percepts in the absence of external sensory stimuli), remain elusive. This K23 application presents a research and training program that will support the applicant on a path towards becoming an NIH-funded independent investigator focused on the application of functional neuroimaging to the study of psychotic symptoms in schizophrenia. The activities in this application build on the candidate's prior training and are set in a resource-rich environment that will foster his development of expertise in (1) advanced analytic methods and study conduct for functional magnetic resonance imaging (fMRI) research; (2) computational neuroscience; (3) perception and cognition research; (4) pathophysiology and clinical assessment of schizophrenia; and (5) responsible and ethical conduct in scientific research with vulnerable populations. Combining functional magnetic resonance imaging (fMRI) and computational modeling, the current research proposal seeks to (1) define the neural mechanisms that generate hallucinations in schizophrenia; and (2) inform the development of a computational model of hallucinations based on predictive coding, an empirically-validated theoretical framework that supports a role of sensory systems in learning and predicting regularities in the external environment. The overarching hypothesis is that abnormal prediction-based attenuation of sensory cortical function produces excessive activity in the sensory cortex that generates hallucinations. To test this hypothesis, the present study will employ a novel speech discrimination fMRI paradigm, two groups of patients with schizophrenia, those with active, frequent auditory verbal hallucinations and those without a significant history of hallucinations, and a third group of healthy controls. This design will allo for testing a direct link between dysfunction in sensory predictive-coding mechanisms and the online experience of hallucinations in patients with schizophrenia, and will thus inform the neurobiological basis of psychotic symptoms in this disorder. Together, this training and research program will facilitate the candidate's transition to an independent research career and will help identify new therapeutic targets for refractory psychosis. RELEVANCE: The novel application of the predictive-coding framework and model-based fMRI to the study of psychotic symptoms will shed new light on the mechanisms of generation of psychotic symptoms, thus filling an important gap in schizophrenia research. This project will serve to develop an explanatory model of hallucinations that can be used to generate specific, testable hypotheses for future neuroscience research in both humans and non-human animal models, and to uncover novel targets (sensory prediction deficits) likely modifiable by treatment via learning or pharmacotherapy.