Leveraging Machine Learning Techniques to Elucidate Risk for Callous-Unemotional Traits

利用机器学习技术来阐明冷酷无情特征的风险

• 批准号: 10597077
• 负责人: Nicholas J Wagner
• 金额: $ 19.74万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10597077
• 关键词: 8 year old; Address; Adult; Age; Aggressive behavior; Anxiety; Behavioral; Biological; Child; Child Development; Child Health; Child Rearing; Childhood; Code; Cognitive; Communities; Computational Linguistics; Crime; Data; Data Reporting; Development; Diagnostic; Disruptive Behavior Disorder; Emotions; Empathy; Etiology; Exploratory/Developmental Grant; Eye; Family; Future; Goals; Guilt; Individual Differences; Influentials; Intervention; Knowledge; Laboratories; Language; Linguistics; Link; Literature; Machine Learning; Measures; Mental Depression; Mental disorders; Methods; Modeling; National Institute of Mental Health; Negative Valence; Outcome; Parent-Child Relations; Parents; Participant; Psychopathology; Public Health; Questionnaires; Race; Reporting; Research; Research Domain Criteria; Research Personnel; Risk; Risk Factors; Sampling; Scheme; Social Processes; Socioeconomic Status; Strategic Planning; Substance abuse problem; Techniques; Temperament; Testing; Theft; Violence; adverse outcome; affection; callous unemotional trait; career; early childhood; gaze; high risk; improved; individualized medicine; innovation; mentalization; middle childhood; modifiable risk; personalized intervention; personalized medicine; preempt; prevent; psychopathic personality; risk prediction; social; social communication; standard care; systems research; treatment strategy; verbal; violence exposure

PROJECT SUMMARY/ABSTRACT Callous-unemotional (CU) traits, defined by low empathy, guilt, and prosociality, predict very high risk for childhood disruptive behavior disorders (DBD) and adverse adult outcomes, including violence, psychopathy, and crime. Standard treatments for DBDs are not as effective for children with CU traits. To inform personalized treatments for DBDs, a better understanding is needed about the specific risk factors for CU traits beginning in early childhood. Prior studies are limited by focusing only risk factors within a single risk domain or at a single age point. Thus, based on extant literature, we do not know which risk factors for DBDs and CU traits matter the most nor at what age they matter the most, including the possibility that the most influential mechanisms are characterized by interactions and nonlinear associations across domains and ages. Moreover, while prior studies have begun to identify risk factors for CU traits within the Cognitive and Negative Valence Systems of the Research Domain Criteria (RDoC), there is a major knowledge gap and fewer available measures focusing on links between the Social Processes domain and CU traits. To address these knowledge gaps, the objectives of this R21 proposal are to: (1) Implement a newly-developed behavioral coding paradigm that assesses affiliation (e.g., verbal and physical displays of affection) and social communication (e.g., eye-gaze, engagement, synchrony) during parent-child interactions; (2) Employ automated methods to identify objective linguistic markers of these domains; and (3) Use machine learning (ML) approaches to identify the domain-specific and age-specific precision risk factors that best predict CU traits across early childhood and middle childhood. We achieve these objectives using existing data from the Durham Child Health and Development Study (DCHDS) (n=206), which includes extensive observational, biological, and questionnaire report data on a diverse sample of children and their families assessed 7 times during early childhood (18, 24, 30, and 36 months) and middle childhood (5, 6, and 7 years), with parent-report measures CU traits at 7-8 years old. We will test child-, parent- , and context-level risk factors for CU traits across different units of analysis (i.e., biological, report, observed) and across two developmental stages (early childhood and middle childhood). This proposal is innovative because it will leverage computational linguistic methods and a new observational coding paradigm to assess affiliation and social communication, which have vital transdiagnostic implications for understanding risk for mental illness. The current proposal will also open new horizons by identifying age-specific and domain-specific risk factors, fundamentally advancing knowledge of how CU traits develop. The proposed R21 research is significant because it improves our ability to assess individual differences within the RDoC Social Processes domain, and will identify domain-specific and age-specific risk factors for CU traits, fundamentally advancing our understanding of the development of CU traits and our future ability to develop personalized and age-specific treatments for CU traits.

项目总结/摘要 无情的无情感（CU）特征，定义为低同情心，内疚和亲社会性，预测非常高的风险， 儿童期破坏性行为障碍（DBD）和不良的成人后果，包括暴力，精神病， 和犯罪DBD的标准治疗对具有CU特征的儿童不那么有效。通知个性化 治疗DBD，需要更好地了解CU特征的特定风险因素， 童年早期以往的研究局限于仅关注单一风险领域内的风险因素或单一风险因素。 年龄点。因此，根据现有的文献，我们不知道DBD和CU特征的哪些风险因素对 大多数人也不知道他们在什么年龄最重要，包括最有影响力的机制可能是 以跨领域和年龄的交互作用和非线性关联为特征。此外，虽然先前的研究 已经开始在认知和负价系统中识别CU特征的风险因素， 研究领域标准（RDoC），存在重大的知识差距，关注以下方面的可用措施较少 社会过程域和CU特质之间的联系。为了弥补这些知识差距， 这个R21建议是：（1）实施一个新开发的行为编码范式，评估归属 (e.g.,感情的口头和身体表现）和社会交流（例如，眼神交流，互动 （2）采用自动化方法来识别客观语言， 这些领域的标记;以及（3）使用机器学习（ML）方法来识别特定领域， 年龄特异性精确风险因素，最好地预测儿童早期和儿童中期的CU特征。我们 利用来自达勒姆儿童健康与发展研究（DCHDS）的现有数据实现这些目标 （n=206），其中包括广泛的观察，生物学和问卷报告数据的不同样本 儿童及其家庭在幼儿期（18、24、30和36个月）和中期评估了7次 儿童期（5岁、6岁和7岁），7-8岁时父母报告测量CU性状。我们会测试孩子，父母- ，以及跨不同分析单元的CU特征的背景水平风险因素（即，生物的、报告的、观察的） 并跨越两个发展阶段（幼儿期和幼儿期）。这一建议具有创新性 因为它将利用计算语言学方法和新的观察编码范式来评估 联系和社会沟通，这对理解风险有重要的transdiagnosis影响， 精神疾病目前的建议还将通过确定特定年龄和特定领域的 风险因素，从根本上推进CU性状如何发展的知识。R21研究的目的是 重要的是，它提高了我们评估RDoC社会过程中个体差异的能力 领域，并将确定特定领域和特定年龄的风险因素CU性状，从根本上推进我们的研究。 了解CU特征的发展以及我们未来发展个性化和特定年龄的能力 治疗CU性状。