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: 10369459
• 负责人: Nicholas J Wagner
• 金额: $ 25.22万
• 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 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10369459
• 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; Disruptive Behavior Disorder; Emotions; Empathy; Etiology; Exploratory/Developmental Grant; Eye; Family; Future; Goals; Guilt; Individual Differences; Influentials; Intervention; Knowledge; Laboratories; Language; Lead; 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; Psyche structure; 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; Time; Violence; adverse outcome; affection; base; callous unemotional trait; career; early childhood; gaze; high risk; improved; individualized medicine; innovation; middle childhood; modifiable risk; personalized intervention; personalized medicine; preempt; prevent; psychopathic personality; social; social communication; standard care; systems research; treatment strategy; 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.

项目摘要/摘要 死亡的人（CU）特征是由低同理心，内gui和亲社会定义的，预测的风险很高。 儿童时期的破坏性行为障碍（DBD）和不利的成人结果，包括暴力，精神病， 和犯罪。 DBD的标准治疗方法对CU特征儿童不那么有效。告知个性化 DBD的治疗方法，需要更好地理解CU特征的特定风险因素 幼儿。先前的研究受到限制，仅在单个风险域内或单个风险域中仅关注风险因素 年龄点。因此，基于现存的文献，我们不知道DBD和CU特征的哪些风险因素很重要 他们最重要的年龄最大，包括最有影响力的机制是 以跨域和年龄之间的相互作用和非线性关联为特征。而且，先前的研究 已经开始确定在认知和负面价系统内的CU特征的危险因素 研究领域标准（RDOC），存在一个主要的知识差距，而较少的可用措施重点是 社会过程领域与CU特征之间的联系。为了解决这些知识差距， 该R21建议是：（1）实施新开发的行为编码范式，评估隶属关系 （例如，情感的口头和身体表现）和社会交流（例如，眼睛注视，参与， 同步）在亲子互动期间； （2）采用自动化方法来识别客观语言 这些领域的标记； （3）使用机器学习（ML）方法来识别特定领域的特定和 特定年龄的精度风险因素，可以最好地预测幼儿期和中期童年时期的CU特征。我们 使用达勒姆儿童健康与发展研究（DCHD）的现有数据实现这些目标 （n = 206），其中包括广泛的观察，生物学和问卷报告有关不同样本的数据 在童年时期（18、24、30和36个月）和中间的儿童及其家人的评估7次 童年（5、6和7年），父母报告的含量为7-8岁。我们将测试子，父母 - ，以及跨不同分析单位（即生物学，报告，观察到的）CU性状的上下文级危险因素 以及两个发展阶段（幼儿和中年童年）。该建议是创新的 因为它将利用计算语言方法和新的观测编码范式来评估 隶属关系和社会交流，对了解风险的风险具有至关重要的经诊断的影响 精神疾病。当前的提案还将通过识别特定年龄和域特异性来打开新的视野 风险因素，从根本上推进了CU特征如何发展的知识。拟议的R21研究是 意义重大，因为它提高了我们评估RDOC社会过程中个体差异的能力 域，并将确定cu特征的特定领域和特定年龄的风险因素，从根本上推进了我们 了解CU特征的发展以及我们发展个性化和特定年龄的未来能力 Cu特征的治疗方法。