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

摘要 情感表达和社交行为困难是多种精神疾病和 对儿童发展产生负面影响。然而，现有的用于索引社会情绪的测量工具 功能不精确和主观，或需要昂贵和时间密集的专门培训，禁止 广泛实施。现有工具的不精确性不仅对研究有重大的负面影响， 而是评估和治疗有心理健康问题的人的能力--特别是在服务不足的人中 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 使用源自计算机视觉的新生物行为标记的行为(面部表情分析)和 计算语言学(社会/情绪分析)。我们的团队已经成功地使用这些标记来预测 对自闭症谱系障碍(ASD)的诊断准确率为91%。在这份提案中，我们确定了 我们的标记物可以作为社会行为和负面情绪的持续衡量标准 临床表型和干预措施。该提案将两项高带宽临床研究结合在一起 费城儿童医院和贝勒医学院的项目收集750名 青少年(12-17岁包括在内)患有自闭症、原发性焦虑症或抑郁症，或没有任何 发育/精神状态。在一个单一的评估中，所有的年轻人都将参加一项广泛的临床 由有效的临床访谈和儿童/父母报告量表评估组成的表型组合 趋同和发散的心理健康结构，以及引发积极/消极情绪、社会 压力和轻微的挫折感。对150名青少年的子样本将在6-10周后重新评估，以允许 重新测试/稳定性分析。一种新的摄像设备将捕捉到自然主义的、同步的语言和非语言 来自对偶体的信号。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--包括面部情感识别方法，可与几种常用的方法相媲美 另类选择。这项建议的最终目标是制定有效和客观的社会和社会保护措施 在大量青年诊断样本中使用新的生物行为标记物的负价系统。 次要目标是开发易于遵循的方法来广泛传播我们的工具和程序，并 通过年龄、性别、种族/民族和诊断来描述这些关键RDoC指标中的个体差异。这个 这些目标的实现将为研究人员提供急需的社会和情感测量 并为临床医生提供一套新的工具来识别和跟踪需要心理健康的年轻人 治疗。

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.