Social Cognition and Suicide in Psychotic Disorders

项目总结/摘要 该行政补充资金申请与NIMH支持的R 01 MH 116902相关联， 响应NOT-OD-21-094，支持合作以改善NIH支持数据的AI/ML就绪性。 补充项目与伊利诺伊大学的计算机科学家团队建立了新的合作关系 专注于自然语言处理的芝加哥。该项目的核心目标是创建和共享一个 一种新颖的数据库资源，用于从独特、大型和多样化的音频样本中进行多模态语音分析 记录了标准化的社会互动。社会过程是严重精神障碍的一个关键方面。 精神疾病严重精神疾病和自杀的语言和非语言障碍标志是 一个活跃的研究领域，与父母研究对自杀社会过程的关注一致， 精神病然而，小数据集和缺乏标准化的数据资源极大地阻碍了现存的 语音分析工作，使观察到的模式和鲁棒性的潜在机制的调查无法进行 模型的潜在偏差来源。在拟议的补充，我们将产生一个新的语料库， 超过1200例具有标准化语音分析数据的病例，特征丰富， 不同的关键人口变量，包括少数民族地位。我们的重点是社会技能 绩效评估是最广泛使用的基于绩效的社会功能衡量标准， 包括专家级的、录音的、模拟的社交互动， 场景在拟议的补充中，我们将为自然语言生成可共享的、去身份化的转录本。 语言处理，根据传统的自然语言特征进行额外的注释， 更新颖的对话动作。我们还将创建相应的去识别音频文件，其中包含 频率、振幅和其他类语言注释。该项目扩展和扩展了并行工作， 由我们的新合作者团队领导的老化研究。事实上，来自语音分析的可共享数据来自 标准化测试在其他领域也有很大的影响力，包括老龄化和痴呆症研究， 目前，在大量精神病患者样本中，还没有这样的数据资源。该项目的目标 是创建，处理和注释数据集，生成用于分析的工具包和源代码，检查新的 与母研究目标相关的标记，并将数据共享给NIMH国家数据档案馆和 科学界。

PROJECT SUMMARY/ABSTRACT This administrative supplemental funding request is linked with NIMH Supported R01MH116902 and is responsive to NOT-OD-21-094 to Support Collaborations to Improve AI/ML-Readiness of NIH Supported Data. The supplement project builds a new collaboration with a computer scientist team at University of Illinois Chicago that focuses on natural language processing. The central aim of the project is to create and share a novel database resource for multi-modal speech analysis from a unique, large, and diverse sample of audio recorded standardized social interactions. Social processes are a key dimension of dysfunction in serious mental illness. The linguistic and paralinguistic markers of impairment in serious mental illness and suicide are an active area of research, consistent with the Parent Study’s focus on social processes in suicide and psychosis. However, small datasets and lack of standardization of data resources greatly hamper extant speech analysis work, disenabling investigation of mechanisms underlying observed patterns and robustness of models across potential sources of bias. In the proposed supplement, we will generate a new corpus of over 1200 cases who have standardized data for speech analysis, are richly characterized, and who are diverse across key demographic variables including minority status. Our focus is on the Social Skills Performance Assessment, which is the most widely used performance-based measure of social function, entailing expert-rated, audio recorded, simulated social interactions that involve affiliative and confrontational scenarios. In the proposed supplement, we will generate sharable, de-identified transcripts for natural language processing, with additional annotation according to conventional natural language features and also more novel dialogue actions. We will also create corresponding de-identified audio files that contain frequency, amplitude and other para-linguistic annotations. This project extends and expands parallel work in aging research led by our new collaborator team. Indeed, sharable data from speech analysis derived from standardized testing has been highly impactful in other fields, including aging and dementia research, but currently no such data resource exists in large samples of people with mental illnesses. The Aims of the project are to create, process and annotate the dataset, generate toolkits and source code for analysis, examine new markers in relation to the Parent Study Aims, and share the data to the NIMH National Data Archive and the scientific community.