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.

项目摘要 /摘要 尽管感知计算研究的进步，但进展很少 了解幻觉等知觉异常。在最近的工作中，我们确定了异常 信息处理是使用感知的尖端计算建模构成幻觉的基础。 我们采用了一个等级制的贝叶斯框架，该框架将感知视为一个建设性过程 对环境的先验知识与传入的感官信息结合在一起，以构建内部 代表一个人周围的环境。这些来源在感知期间的重量取决于 以其精度（或可靠性）。在这个框架内，幻觉 - 在没有 相应的传入感官证据 - 可能是由于对先验知识的过度加权而产生的 与传入的感觉证据进行比较。为了证明这一点，我们使用经典条件安全和 有或没有听觉口头幻觉的人，可逆地引起简单音调的幻觉 （AVH）。那些AVH的人大约容易受到这种条件幻觉效果的五倍 因为在感知期间，比传入的感觉信息更重要的是先验知识的趋势。 在感知期间，先验与感觉证据的相对权重取决于胆碱能 信号传导：乙酰胆碱对感官数据的感知推断并远离先验。那在声音中 - 具有绝对高位加权的听众，增强胆碱能信号可能会导致更少 幻觉。我们建议表征胆碱能信号对感知的影响， 幻觉的计算，物理和临床特征。主要是，我们假设：1） 健康参与者中西孢胺（一种M1胆碱能受体拮抗剂）降低胆碱能的张力 将导致更高的先前加权，更多的条件幻觉和更多的事先相关 与安慰剂相比，大脑活动； 2）用静脉注射剂增加胆碱能张力（可逆的， 每日幻觉患者的集中作用胆碱酯酶抑制剂）将导致先前的下降 与安慰剂相比，加权，条件幻觉和临床幻觉； 3）这些 效果将取决于基线评估时的高位加权。我们的目标是使用 生成的知识是为了采取第一步迈向计算信息的个性化治疗 幻觉的方法。