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

项目摘要/摘要 早期识别临床高危精神病患者对于最大限度地提高预后至关重要。 对于那些皈依宗教的人。然而，预测在很大程度上依赖于主观的症状报告。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的转化。 在最近与我的主要导师(Corlett博士)完成并发表在《科学》杂志上的研究中，我研究了 幻觉是否可能是由于过重了感知中的先验知识而产生的。我们用感官 产生幻觉体验的条件反射。参与者接触到重复配对的视觉和 听觉刺激，并随后在只有视觉存在的情况下感知听觉刺激。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，都有(P+H+) 并且没有(P+H-)幻觉、健康的听音者(P-H+)和健康的对照组(P-H-)。条件性的 幻觉者(P+H+和P-H+)的幻觉频率明显高于那些 谁没有(P+H-，P-H-)。 这些行为数据被用来估计分层高斯滤波器(HGF)的参数 与斯蒂芬博士(共同导师)的实验室进行建模。区分两个不同的模型参数 有或没有幻听的个体，以及有或没有可诊断的幻听的个体 精神错乱。在功能成像分析中，大脑中编码低水平知觉信念的区域的活动 (例如，脑岛、颞上沟)区分有幻觉和无幻觉的患者。脑内活动 编码变化敏感性的区域(例如，小脑)区分那些有精神病和没有精神病的人。这些 计算和成像度量可以加速检测到CHR中的转换。然而，更多的工作是 必填项。我们建议1)确定这些标记是否与CHR的转换风险有关；以及2) 确定它们是否随时间推移而随症状严重程度而变化。本研究将在以下方面提供培训 知觉、CHR评估和纵向数据计算模型的临床应用 分析。我们的工作将得到正式教学论和研讨会的支持，这些研讨会的重点是 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，这样我就可以捕捉到信息处理的微妙异常 这比坦率的幻觉和精神病的发展更早。这项建议将为我提供 成为一名完全独立的调查员所需的额外培训和指导研究经验 世卫组织将计算神经科学的工具应用于精神病的早期发现。

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.