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Predictive Coding as a Framework for Understanding Psychosis

预测编码作为理解精神病的框架

基本信息

批准号：
10292448
负责人：
PHILIP CORLETT
金额：
$ 67.04万
依托单位：
UNIVERSITY OF MARYLAND BALTIMORE
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-12-01 至 2023-10-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10292448
关键词：
Adopted Age Anterior Attenuated Auditory Perception Auditory area Behavior Behavioral Belief Biological Assay Brain Brain region Chronic Code Complex Computer Models Cues Data Delusions Distress Electroencephalography Electrophysiology (science)Event Exhibits Expectancy Failure Functional Magnetic Resonance Imaging Functional disorder Future Glutamates Goals Hallucinations Hearing Impairment Individual Insula of Reil Intervention Learning Light Magnetic Resonance Spectroscopy Maintenance Maps Measurement Measures Modeling National Institute of Mental Health Neuroanatomy Onset of illness Outcome Patients Perception Persons Physiology Play Prefrontal Cortex Protons Psychological reinforcement Psychoses Psychotic Disorders Public Health Reporting Research Domain Criteria Research Personnel Resistance Response to stimulus physiology Reversal Learning Rewards Role Sampling Severities Signal Transduction Specific qualifier value Stimulus Symptoms System Task Performances Testing Time Uncertainty Update Visual Motion base brain behavior cognitive process cognitive task design effective therapy experience glutamatergic signaling multisensory personalized medicine predictive test psychotic symptoms relating to nervous system response reward expectancy sensory stimulus theories visual stimulus

项目摘要

7. Project Summary This application responds to the NIMH PAR-16-136, “Using the NIMH Research Domain Criteria (RDoC) Approach to Understand Psychosis.” Psychotic symptoms, such as delusions and hallucinations, are treatment-resistant in many patients and are associated with high levels of distress and impairment. Treatment advances have been slowed by the lack of a model of how these symptoms arise and persist. Adopting the RDoC approach, we suggest that these symptoms may result from abnormalities in the neural and cognitive processes that underlie perception, action, and belief formation. Hierarchical predictive coding represents an explanatory framework that unites function and dysfunction in perception action and belief formation. We perceive, act, and believe based on our prior experiences, and we update those priors in light of new data and the prediction errors they elicit. We suggest that hallucinations and delusions form, and are maintained, via aberrant predictive coding mechanisms that vitiate perception, action and belief. We will test these hypotheses with a suite of predictive coding measures in a large sample, capturing variability in symptom severity and duration. We will use functional magnetic resonance imaging (fMRI) during tasks of perception, action, and belief, electroencephalography to measure mismatch negativity (MMN) to unexpected perceptual stimuli, and magnetic resonance spectroscopy (MRS) to measure glutamate concentrations, which may underlie the perturbed MMN and fMRI signals in people with psychosis. We will bring together behavioral and brain data with formal computational modeling that will allow us to estimate, from each individual subject's data, the strength of their priors and prediction errors across a hierarchy of representational richness from simple stimuli through more complex percepts, action choices, and beliefs. We propose four specific aims: (1) testing whether inappropriately strong top-down perceptual priors cause hallucinations; (2) testing if delusions are caused by aberrant prediction error signaling; (3) examining whether psychotic symptoms result from a failure to attribute outcomes to one's own actions appropriately; (4) and assessing whether glutamate levels are related to predictive coding phenomena assayed in Aims 1-3. In a fifth exploratory aim, we will examine whether predictive coding abnormalities change over course of illness. Our overall goal is to provide a computationally rigorous test of the predictive coding account of delusions and hallucinations. Depending on the outcome, we will either discard the theory, or use it to design and test treatment approaches more tailored to the specific, and this far unmet, needs of individuals with psychosis.

7.项目摘要此应用程序响应NIMH PAR-16-136，“使用NIMH研究领域标准（RDoC）” 理解精神病的方法。”妄想和幻觉等精神病症状，在许多患者中存在治疗抵抗性，并与高水平的痛苦和损伤相关。治疗由于缺乏这些症状如何出现和持续的模型，进展缓慢。采用 RDoC的方法，我们认为，这些症状可能是由于异常的神经和认知认知、行动和信念形成的基础过程。分层预测编码表示一个解释框架，统一的功能和功能障碍的感知行动和信念的形成。我们根据我们以前的经验来感知、行动和相信，我们根据新的数据更新这些先验知识，它们所引发的预测误差。我们认为，幻觉和妄想的形成，并保持，通过破坏感知、行动和信念的异常预测编码机制。我们将在大样本中使用一套预测编码措施来测试这些假设，症状严重程度和持续时间的变异性。我们将使用功能性磁共振成像（fMRI），感知、行动和信念任务，脑电图测量失配负波（MMN），意想不到的感知刺激和磁共振波谱（MRS）测量谷氨酸浓度，这可能是精神病患者MMN和fMRI信号紊乱的基础。我们将将行为和大脑数据与正式的计算模型结合起来，使我们能够从每个个体受试者的数据，他们的先验知识的强度和预测误差，从简单的刺激到更复杂的感知、行动选择和信念的表征丰富性。我们提出了四个具体的目标：（1）测试是否不适当的强自上而下的知觉先验导致幻觉;（2）测试妄想是否由异常的预测错误信号引起;（3）检查是否精神病症状是由于未能适当地将结果归因于自己的行为;（4）以及评估谷氨酸水平是否与目的1-3中测定的预测编码现象相关。在第五为了探索性的目的，我们将检查预测编码异常是否随疾病过程而变化。我们的总体目标是提供一个严格的计算测试的预测编码帐户的错觉和幻觉根据结果，我们要么放弃理论，要么用它来设计和测试治疗方法更适合精神病患者的具体需要，而这种需要迄今尚未得到满足。

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Factor one, familiarity and frontal cortex: a challenge to the two-factor theory of delusions.

因素一，熟悉度和额叶皮层：对妄想二因素理论的挑战。

DOI：
10.1080/13546805.2019.1606706
发表时间：
2019
期刊：
Cognitive neuropsychiatry
影响因子：
1.7
作者：
Corlett,PhilipR
通讯作者：
Corlett,PhilipR

Phenomenological and Cognitive Features Associated With Auditory Hallucinations in Clinical and Nonclinical Voice Hearers.

DOI：
10.1093/schbul/sbad083
发表时间：
2023-11-29
期刊：
SCHIZOPHRENIA BULLETIN
影响因子：
6.6
作者：
Gold, James M.;Corlett, Philip R.;Erickson, Molly;Waltz, James A.;August, Sharon;Dutterer, Jenna;Bansal, Sonia
通讯作者：
Bansal, Sonia

Aligning Computational Psychiatry With the Hearing Voices Movement: Hearing Their Voices.

将计算精神病学与聆听声音运动结合起来：聆听他们的声音。