Toward a Computationally-Informed, Personalized Treatment for Hallucinations

迈向基于计算的个性化幻觉治疗

基本信息

批准号：
10159329
负责人：
Albert R Powers
金额：
$ 18.38万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-05 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10159329
关键词：
Acetylcholine Adopted Alzheimer&apos s Disease Anterior Antiemetic Effect Antipsychotic Agents Auditory Hallucination Auditory Perception Bayesian Modeling Behavior Behavioral Belief Brain Brain region Chemosensitization Cholinergic Agents Cholinergic Antagonists Cholinergic Receptors Cholinesterase Inhibitors Clinical Cochlea Computer Models Data Development Distress Environment Exhibits Exposure to Frequencies Functional Magnetic Resonance Imaging Functional disorder Gaussian model Goals Hallucinations Hearing Individual Insula of Reil Knowledge Learning Link Mathematics Mediating Modeling Neurosciences Neurotransmitters Organ Overweight Participant Patients Perception Pharmaceutical Preparations Pharmacology Physiological Physostigmine Placebos Process Psychiatry Psychoses Psychotic Disorders Recording of previous events Retina Saline Scopolamine Sensory Signal Transduction Source Symptoms System Time Verbal Auditory Hallucinations Visual Voice Weight Work auditory discrimination auditory stimulus base cholinergic classical conditioning computer studies computerized tools conditioning experience individualized medicine information processing novel patient subsets personalized medicine psychotic symptoms relating to nervous system response sensory input sound suicidal risk therapy development visual stimulus

项目摘要

PROJECT SUMMARY / ABSTRACT Despite advances in the computational study of perception, there has been remarkably little progress in understanding perceptual abnormalities like hallucinations. In recent work, we identified abnormalities in information processing that underlie hallucinations using cutting-edge computational modeling of perception. We adopted a hierarchical Bayesian framework, which views perception as a constructive process wherein prior knowledge of the environment is combined with incoming sensory information to build an internal representation of one’s surroundings. The weight each of these sources exerts during perception is dependent upon its precision (or reliability). Thus, within this framework, hallucinations—percepts in the absence of corresponding incoming sensory evidence—may arise from an over-weighting of prior knowledge in comparison to incoming sensory evidence. To demonstrate this, we used classical conditioning to safely and reversibly induce hallucinations of simple tones in those both with and without auditory verbal hallucinations (AVH). Those with AVH were roughly five times more susceptible to this Conditioned Hallucinations effect because of a tendency to weigh prior knowledge more than incoming sensory information during perception. This relative weighting of priors versus sensory evidence during perception depends critically on cholinergic signaling: acetylcholine biases perceptual inference toward sensory data and away from priors. Thus, in voice- hearers with abnormally high prior weighting, enhancing cholinergic signaling could result in fewer hallucinations. We propose to characterize the effects of cholinergic signaling on the perceptual, computational, physiological, and clinical signatures of hallucinations. Principally, we hypothesize that: 1) Decreasing cholinergic tone with scopolamine (an M1 cholinergic receptor antagonist) in healthy participants will result in exhibit higher prior weighting, more conditioned hallucinations, and more prior-related brain activity compared to placebo; 2) Increasing cholinergic tone with IV physostigmine (a reversible, centrally-acting cholinesterase inhibitor) in patients with daily hallucinations will result in decreases in prior weighting, conditioned hallucinations, and clinical hallucinations compared to placebo; and 3) These effects will depend on the existence of high prior weighting at baseline assessment. Our goal is use the knowledge generated to take the first steps toward a computationally-informed, personalized treatment approach to hallucinations.

项目概要/摘要尽管感知的计算研究取得了进展，但在这方面却进展甚微。理解幻觉等知觉异常在最近的工作中，我们发现了异常。使用尖端的感知计算模型进行幻觉背后的信息处理。我们采用了分层贝叶斯框架，它将感知视为一个建设性的过程。环境的先验知识与传入的感官信息相结合，构建内部这些来源在感知过程中所施加的权重是相关的。因此，在这个框架内，幻觉——在缺乏事实的情况下的感知。相应的传入感官证据——可能是由于对先验知识的过度重视而产生的为了证明这一点，我们使用经典条件反射来安全且可靠地进行比较。可逆地诱发有或没有幻听的人产生简单音调的幻觉 (AVH) 患有 AVH 的人受到条件性幻觉影响的可能性大约是其他人的五倍。因为在感知过程中，人们倾向于更重视先验知识而不是传入的感官信息。感知过程中先验与感官证据的相对权重主要取决于胆碱能信号传导：乙酰胆碱使知觉推理偏向于感觉数据并远离先验，因此，在语音中。对于先前权重异常高的听者，增强胆碱能信号可能会导致更少的我们建议描述胆碱能信号对知觉的影响，幻觉的计算、生理和临床特征主要是：1）使用东莨菪碱（一种 M1 胆碱能受体拮抗剂）降低健康参与者的胆碱能张力将导致表现出更高的先验权重、更多的条件性幻觉以及更多的先验相关性与安慰剂相比，大脑活动；2) 静脉注射毒扁豆碱（一种可逆的、中枢作用的胆碱酯酶抑制剂）每日出现幻觉的患者将导致先前的幻觉减少与安慰剂相比的体重、条件性幻觉和临床幻觉；以及 3) 这些效果将取决于基线评估时是否存在高先验权重。生成的知识是迈向基于计算的个性化治疗的第一步处理幻觉的方法。