Toward a Computationally-Informed, Personalized Treatment for Hallucinations

迈向基于计算的个性化幻觉治疗

• 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
• 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' 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

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.

项目总结/摘要 尽管在知觉的计算研究方面取得了进展，但在 理解知觉异常比如幻觉在最近的工作中，我们发现了 使用尖端的感知计算模型来处理幻觉背后的信息。 我们采用了分层贝叶斯框架，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， 一个人周围环境的表现。在感知过程中，这些来源中的每一个所施加的权重是依赖于 其准确性（或可靠性）。因此，在这个框架内，幻觉-在缺乏 相应的传入的感官证据-可能是由于对先前知识的过度加权而引起的， 与传入的感官证据进行比较。为了证明这一点，我们使用经典条件反射来安全地 可逆性地诱导那些有和没有听觉语言幻觉的人产生简单音调的幻觉 （AVH）。AVH患者对这种条件性幻觉效应的敏感性大约是正常人的五倍 这是因为在感知过程中，人们倾向于对先验知识的权重大于传入的感官信息。 知觉过程中先验与感觉证据的相对权重主要取决于胆碱能神经 信号传导：乙酰胆碱使知觉推理偏向感觉数据，远离先验。因此，在声音上- 对于先前体重异常高的听力者，增强胆碱能信号可能导致更少的 幻觉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。我们主要假设：1） 东莨菪碱（一种胆碱能受体拮抗剂）降低健康受试者的胆碱能张力 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。 与安慰剂相比的脑活动; 2）用IV毒扁豆碱增加胆碱能张力（可逆的， 中枢作用胆碱酯酶抑制剂）将导致患者的日常幻觉减少， 与安慰剂相比的体重、条件性幻觉和临床幻觉;以及3）这些 影响将取决于基线评估时是否存在较高的先前权重。我们的目标是利用 生成的知识，为实现计算信息化的个性化治疗迈出了第一步 接近幻觉。