Social Cognition and Suicide in Psychotic Disorders

项目摘要/摘要 此行政补充资金申请与NIMH支持的R01MH116902相关联，并且 响应NOT-OD-21-094以支持协作，以提高NIH支持的数据的AI/ML就绪性。 该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大学。该项目的中心目标是创建和共享 用于从唯一、大容量和多样化的音频样本中进行多模式语音分析的新数据库资源 记录标准化的社交互动。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副语言障碍的标志是 一个活跃的研究领域，与父母研究的重点一致，即自杀和 精神错乱。然而，数据集很小，数据资源缺乏标准化，极大地阻碍了现有的数据资源 语音分析工作，无法调查观察到的模式和健壮性背后的机制 所有可能的偏向来源的模型。在拟议的补充中，我们将生成一个新的语料库 1200多个拥有语音分析标准化数据的案例，具有丰富的特征，谁是 不同的关键人口统计变量，包括少数群体的地位。我们的重点是社交技能 绩效评估，这是使用最广泛的以绩效为基础的社会职能衡量标准， 需要专家评级的、录音的、模拟的社交互动，涉及附属和对抗性 场景。在拟议的附录中，我们将为Natural生成可共享的、未识别的抄本 语言处理，具有根据常规自然语言特征的附加注释，还 更新颖的对话动作。我们还将创建相应的已取消识别的音频文件，其中包含 频率、幅度和其他非语言注释。该项目将并行工作扩展到 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于 标准化测试在其他领域影响很大，包括衰老和痴呆症研究，但 目前，在精神疾病患者的大样本中没有这样的数据来源。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 与父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.