A Hearing Test for Hallucinations: Toward Development of Computational Markers for Early Diagnosis

幻觉听力测试：开发用于早期诊断的计算标记

• 批准号: 9975898
• 负责人: Albert R Powers
• 金额: $ 19.66万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-23 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9975898
• 关键词: Address; Adopted; Auditory Hallucination; Behavioral; Belief; Biological Markers; Brain; Brain region; Cerebellum; Clinical; Clinical assessments; Cochlea; Complement; Computer Models; Data; Data Analyses; Deltastab; Detection; Development; Diagnosis; Diagnostic; Disease Marker; Early Diagnosis; Early identification; Early treatment; Environment; Evaluation; Exhibits; Exposure to; Frequencies; Functional Imaging; Functional disorder; Gaussian model; Goals; Hallucinations; Hearing; Hearing Tests; Hospitalization; Image; Individual; Insula of Reil; Knowledge; Laboratories; Learning; Link; Medical; Mentors; Modeling; Neurotransmitters; Organ; Outcome; Overweight; Participant; Patient Care; Patient Self-Report; Patients; Perception; Physiology; Predisposition; Process; Psychopathology; Psychotic Disorders; Publishing; Quality of life; Reporting; Research; Research Personnel; Retina; Risk; Schizophrenia; Science; Selection for Treatments; Sensory; Services; Severities; Structure of superior temporal sulcus; Suicide attempt; Symptoms; System; Testing; Therapeutic; Time; Training; Uncertainty; Visual; Voice; Weight; Work; auditory stimulus; base; care outcomes; career; clinical application; computational neuroscience; computerized tools; conditioning; experience; high risk; improved; improved outcome; information model; information processing; neuroimaging; research clinical testing; sensory input; symposium; theories; visual stimulus

PROJECT SUMMARY / ABSTRACT Early identification of those at clinical high risk of psychosis (CHR) is critical for maximizing outcomes for those who convert. However, prediction relies largely on subjective symptom reports. Objective biomarkers are essential. My career goal is to use objective computational neuroscience to predict conversion in CHR. In work recently completed with my primary mentor (Dr. Corlett) and published in Science, I examined whether hallucinations might arise from an over-weighting of prior knowledge in perception. We used sensory conditioning to elicit hallucinatory experiences. Participants were exposed to repeated pairings of visual and auditory stimuli and subsequently perceived the auditory stimulus when only the visual was present. We applied this Conditioned Hallucinations paradigm to four groups: participants with psychosis both with (P+H+) and without (P+H-) hallucinations, healthy voice-hearers (P-H+), and healthy controls (P-H-). Conditioned hallucinations were markedly more frequent in those who hallucinate (P+H+ and P-H+) compared with those who do not (P+H-, P-H-). These behavioral data were used to estimate parameters of a Hierarchical Gaussian Filter (HGF) model with the laboratory of Dr. Stephan (co-mentor). Two different model parameters discriminated between groups of individuals with and without auditory hallucinations and, orthogonally, with and without a diagnosable psychotic disorder. On functional imaging analysis, activity in brain regions encoding low-level perceptual belief (e.g., insula, superior temporal sulcus) differentiated those with and without hallucinations. Activity in brain regions encoding change sensitivity (e.g., cerebellum) differentiated those with and without psychosis. These computational and imaging metrics may hasten the detection of conversion in CHR. However, more work is required. We propose 1) to determine whether these markers relate to risk of conversion in CHR; and 2) to determine whether they change with symptom severity over time. This research will provide training in the clinical application of computational models of perception, the evaluation of CHR, and longitudinal data analysis. Our work will be supported by formal didactics and symposia focused on the theory and practice of computational modeling. To meet my career goal, I must understand more deeply how to construct, alter, and utilize computational models of perception so that I may capture the subtle abnormalities of information processing that predate the development of frank hallucinations and psychosis. This proposal will provide me with the additional training and mentored research experiences necessary to become a fully independent investigator who brings the tools of computational neuroscience to the service of the early detection of psychosis.

项目摘要 /摘要 早期鉴定精神病临床高风险（CHR）对于最大化结果至关重要 对于那些转换的人。但是，预测在很大程度上依赖于主观症状报告。客观生物标志物 是必不可少的。我的职业目标是使用客观的计算神经科学来预测CHR中的转化。 在最近与我的主要导师（Corlett博士）完成的工作中，我研究了 幻觉是否可能是由于感知中的先验知识过度加权而产生的。我们使用了感官 调理以引起幻觉经验。参与者接触了反复的视觉和 听觉刺激并随后在仅存在视觉时就会感知听觉刺激。我们 将这种条件幻觉范式应用于四组：精神病参与者（P+H+） 没有（P+H-）幻觉，健康的语音听觉者（P-H+）和健康的对照（P-H-）。条件 与那些相比 谁不（p+h-，p-h-）。 这些行为数据用于估计分层高斯滤波器（HGF）的参数 斯蒂芬博士（Co-Incertor）实验室的模型。区分两个不同的模型参数 有和没有听觉幻觉的人群以及正交，有和没有可诊断的人 精神病障碍。在功能成像分析上，编码低级感知信念的大脑区域的活性 （例如，绝缘，颞上沟）区分了有或没有幻觉的人。大脑的活动 编码变化敏感性的区域（例如小脑）有和没有精神病的区域。这些 计算和成像指标可能会加速Chr中转化的检测。但是，更多的工作是 必需的。我们建议1）确定这些标记是否与CHR中转化的风险有关；和2）到 确定它们是否随着时间的推移而随着症状的严重程度而改变。这项研究将在 感知计算模型，CHR评估和纵向数据的临床应用 分析。我们的工作将得到正式的教学和座谈会的支持 计算建模。 为了满足我的职业目标，我必须更深入地了解如何构建，更改和利用 感知的计算模型，以便我可以捕获信息处理的微妙异常 这早于坦率的幻觉和精神病的发展。该建议将为我提供 成为完全独立的研究者所需的其他培训和指导的研究经验 谁将计算神经科学的工具带入了早期发现精神病的服务。