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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要早期识别临床精神病高危人群 (CHR) 对于最大化结果至关重要对于那些皈依的人。然而，预测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 的转换。在最近与我的主要导师（Corlett 博士）完成并发表在《科学》杂志上的工作中，我研究了幻觉是否可能是由于过分重视感知中的先验知识而产生的。我们用感官调节以引发幻觉体验。参与者反复接触视觉和视觉配对听觉刺激，并随后在仅存在视觉时感知听觉刺激。我们将这种条件性幻觉范式应用于四组：患有精神病的参与者都患有（P+H+）没有（P+H-）幻觉，健康的声音听到者（P-H+）和健康的对照（P-H-）。有条件的与那些产生幻觉的人（P+H+ 和 P-H+）相比，产生幻觉的频率明显更高谁不这样做（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 Stephan 博士（共同导师）实验室的模型。区分两个不同的模型参数有或没有幻听的个体群体，以及有或没有可诊断的正交个体精神障碍。在功能成像分析中，编码低水平感知信念的大脑区域的活动（例如岛叶、颞上沟）区分有幻觉和无幻觉的人。大脑活动编码变化敏感性的区域（例如小脑）区分了患有和不患有精神病的人。这些计算和成像指标可能会加速 CHR 中转化的检测。然而，更多的工作是必需的。我们建议 1) 确定这些标记是否与 CHR 中的转化风险相关； 2) 至确定它们是否随着时间的推移而随着症状严重程度而变化。这项研究将提供培训感知计算模型、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研讨会的支持，重点关注理论和实践计算建模。为了实现我的职业目标，我必须更深入地了解如何构建、改变和利用感知的计算模型，以便我可以捕捉信息处理的微妙异常这早于明显的幻觉和精神病的发展。该提案将为我提供成为完全独立的研究者所需的额外培训和指导研究经验他将计算神经科学的工具用于精神病的早期检测。