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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.

项目摘要/摘要