Optimized Affective Computing Measures of Social Processes and Negative Valence in Youth Psychopathology

青年精神病理学中社会过程和负价的优化情感计算措施

• 批准号: 10594051
• 负责人: JOHN David HERRINGTON
• 金额: $ 75.93万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-02 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10594051
• 关键词: Acceleration; Achievement; Address; Adolescence; Adolescent; Age; Anxiety; Anxiety Disorders; Behavior; Behavior assessment; Behavioral Sciences; Child; Child Development; Clinical; Clinical Research; Clinical Sciences; Computational Linguistics; Computer Vision Systems; Computers; Data; Data Collection; Depressive disorder; Development; Diagnosis; Diagnostic; Dimensions; Elements; Emotional; Emotions; Ethnic Origin; Face; Facial Expression; Factor Analysis; Frustration; Funding; Gender; Goals; Grain; Hour; Human; Individual; Individual Differences; Intervention; Interview; Investments; Judgment; Laboratories; Linguistics; Machine Learning; Maps; Measurement; Measures; Medicine; Mental Depression; Mental Health; Methodology; Methods; Modeling; Mood Disorders; Moods; Multivariate Analysis; NIH Program Announcements; National Institute of Mental Health; Negative Valence; Parents; Participant; Pediatric Hospitals; Phenotype; Philadelphia; Play; Population; Predictive Analytics; Procedures; Psychiatric Diagnosis; Psychopathology; Quality of life; Race; Reporting; Research; Research Domain Criteria; Research Personnel; Resources; Sampling; Signal Transduction; Site; Smiling; Social Behavior; Social Processes; Specialist; System; Testing; Thinness; Time; Training; Translating; Trier Social Stress Test; Variant; Youth; affective computing; aged; autism spectrum disorder; autistic children; behavior measurement; behavioral health; biobehavior; clinical phenotype; college; cost; digital; emotional behavior; emotional functioning; experimental study; indexing; individual variation; natural language; non-verbal; novel; programs; repetitive behavior; response; showing emotion; social; social anxiety; social deficits; social metrics; social stress; tool; verbal

ABSTRACT Difficulties with emotion expression and social behavior characterize multiple psychiatric conditions and negatively impact child development. However, existing measurement tools for indexing social-emotional function are imprecise and subjective, or require specialized training that is costly and time-intensive, prohibiting widespread implementation. The imprecision of existing tools has a major negative impact not only on research, but on the ability to assess and treat individuals with mental health concerns – especially among underserved and under-resourced populations. Here, we propose to address this problem by quantifying social and emotional behavior using novel biobehavioral markers derived from computer vision (facial expression analysis) and computational linguistics (social/sentiment analysis). Our team has successfully used these markers to predict the presence of autism spectrum disorder (ASD) with 91% accuracy. In this proposal, we determine the extent to which our markers can serve as continuous measures of social behavior and negative emotion to advance clinical phenotyping and interventions. The proposal brings together two high-bandwidth clinical research programs at the Children’s Hospital of Philadelphia and Baylor College of Medicine to collect data on 750 adolescents (ages 12-17 inclusive) with ASD, a primary anxiety or depressive disorder, or without any developmental/psychiatric condition. At a single assessment, all youth will participate in an extensive clinical phenotyping battery consisting of validated clinical interviews and child-/parent-report scales assessing converging and diverging mental health constructs, and three tasks eliciting positive/negative emotion, social stress, and mild frustration. A subsample of 150 adolescents will be reassessed 6-10 weeks later to allow retest/stability analyses. A novel camera apparatus will capture naturalistic synchronized verbal and nonverbal signals from dyads. Our analytic approach combines state-of-the-art machine learning, computational linguistics, and computer vision – including facial emotion recognition methods that rival several commonly used alternatives. The ultimate goal of this proposal is to develop valid and objective measures of the Social and Negative Valence Systems using novel biobehavioral markers in a large transdiagnostic sample of youth. Secondary goals are to develop easy-to-follow methods to widely disseminate our tools and procedures, and to characterize individual variability in these key RDoC metrics by age, gender, race/ethnicity, and diagnosis. The achievement of these goals will provide researchers with sorely needed measures of social and emotional behavior, and provide clinicians with a new set of tools for identifying and tracking youth in need of mental health treatment.

摘要 情感表达和社会行为困难是多种精神疾病的特征， 对儿童发展产生负面影响。然而，现有的社会情感指数测量工具， 功能不精确和主观，或需要专门的培训，这是昂贵的和时间密集的，禁止 广泛实施。现有工具的不精确性不仅对研究产生了重大的负面影响， 而是评估和治疗有心理健康问题的个人的能力--特别是在服务不足的人群中 和资源不足的人口。在这里，我们建议通过量化社会和情感来解决这个问题。 使用来自计算机视觉的新的生物行为标记（面部表情分析）的行为， 计算语言学（社会/情感分析）。我们的团队已经成功地利用这些标记来预测 自闭症谱系障碍（ASD）的准确率为91%。在本提案中，我们确定 我们的标记物可以作为社会行为和负面情绪的持续测量， 临床表型分析和干预。该提案汇集了两项高带宽临床研究 费城儿童医院和贝勒医学院的项目收集了750名儿童的数据， 患有ASD、原发性焦虑或抑郁障碍或没有任何 发展/精神状况。在一次评估中，所有青少年都将参加一次广泛的临床评估， 表型组合，包括经验证的临床访谈和儿童/父母报告量表， 聚合和发散的心理健康结构，以及三个任务，引发积极/消极情绪，社会 压力和轻微的挫败感150名青少年的子样本将在6-10周后重新评估， 复检/稳定性分析。一种新颖的摄像装置将捕捉自然同步的语言和非语言 来自二分体的信号我们的分析方法结合了最先进的机器学习，计算语言学， 和计算机视觉-包括面部表情识别方法，可以与几种常用的 替代品.该提案的最终目标是制定有效和客观的社会和经济措施， 负价系统使用新的生物行为标记在一个大的transdiagnosis样本的青年。 次要目标是开发易于遵循的方法，以广泛传播我们的工具和程序， 通过年龄、性别、人种/种族和诊断来表征这些关键RDoC指标的个体变异性。的 这些目标的实现将为研究人员提供迫切需要的社会和情感措施， 行为，并为临床医生提供一套新的工具，用于识别和跟踪需要心理健康的青少年 治疗