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。项目摘要该应用程序对NIMH PAR-16-136做出了响应，“使用NIMH研究领域标准（RDOC）理解精神病的方法。”精神病符号，例如妄想和幻觉，是许多患者的治疗性耐药性，与高水平的困扰和障碍有关。治疗缺乏这些符号如何出现和持续存在的模型，使进步放慢了。采用 RDOC方法，我们建议这些症状可能是由于神经和认知异常引起的构成了感知，行动和信仰形成的过程。分层预测编码代表将单位功能和功能障碍在感知行动和信念形成中起作用的剥夺框架。我们根据我们以前的经验感知，采取行动和相信，我们根据新数据和他们引起的预测错误。我们建议形成幻觉和妄想，并通过充实感知，行动和信念的异常预测编码机制。我们将在大型样本中使用一套预测编码度量来测试这些假设，并捕获症状严重程度和持续时间的差异。我们将在期间使用功能性磁共振成像（fMRI）感知，行动和信念的任务，脑电图，以衡量不匹配谈判（MMN）意外的感知刺激和磁共振光谱（MRS）测量谷氨酸浓度可能是精神病患者中扰动的MMN和fMRI信号的基础。我们将通过正式的计算建模将行为和大脑数据汇总在一起，可以从每个受试者的数据，先验的优势以及在一个层次结构中的预测错误从简单刺激到更复杂的知觉，行动选择和信仰的代表性丰富。我们提出了四个具体目的：（1）测试是否不恰当地强大自上而下的感知先验会导致幻觉；（2）测试是否是由异常预测误差信号引起的；（3）检查是否精神病症状是由于未能将结果归因于自己的行为而导致的；（4）和评估谷氨酸水平是否与目标1-3中测定的预测编码现象有关。在五分之一探索目的，我们将检查预测性编码异常在疾病过程中是否发生变化。我们的总体目标是对妄想的预测编码说明提供计算严格的测试和幻觉。根据结果，我们要么丢弃理论，要么使用它来设计和测试治疗方法更适合精神病患者的特定和未满足的需求。

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）

Predicting to Perceive and Learning When to Learn.

DOI：
10.1016/j.tics.2019.12.005
发表时间：
2020-04
期刊：
Trends in cognitive sciences
影响因子：
19.9
作者：
Corlett P
通讯作者：
Corlett P

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

Studying Healthy Psychosislike Experiences to Improve Illness Prediction.

研究类似精神病的健康经历以改善疾病预测。