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

项目摘要/摘要 冷酷无情(CU)特征，由低同理心、负罪感和亲社交定义，预示着极高的风险 儿童破坏性行为障碍(DBD)和不良成人后果，包括暴力、精神病、 和犯罪。对于患有慢性阻塞性肺疾病的儿童，标准的DBD治疗方法并不有效。通知个性化信息 对于DBD的治疗，需要更好地了解CU特征的特定风险因素 童年早期。以前的研究局限于只关注单一风险领域或单一风险领域内的风险因素 年龄点。因此，根据现有的文献，我们不知道DBD和CU性状的哪些风险因素对 最重要的也不是在什么年龄，包括最有影响力的机制可能是 以跨域和跨年龄的相互作用和非线性关联为特征。此外，虽然之前的研究 已经开始在认知和负值系统中识别CU特征的风险因素 研究领域标准(RDoC)，存在重大的知识差距和关注以下方面的可用措施较少 社会过程领域和CU特征之间的联系。为了解决这些知识差距， 该R21建议是：(1)实现新开发的评估从属关系的行为编码范例 (例如，言语和身体上的感情展示)和社会交流(例如，眼睛凝视，参与， 同步性)在亲子互动期间；(2)使用自动化方法来识别目标语言 这些领域的标记；以及(3)使用机器学习(ML)方法来识别特定于领域的 年龄特定的精确风险因素，最好地预测儿童早期和儿童中期的CU特征。我们 利用达勒姆儿童健康和发展研究(DCHDS)的现有数据实现这些目标 (n=206)，其中包括关于不同样本的广泛的观察、生物学和问卷报告数据 在儿童早期(18、24、30和36个月)和中期对儿童及其家庭进行7次评估 儿童时期(5、6和7岁)，父母报告测量7-8岁的CU特征。我们将测试孩子，家长- ，以及跨不同分析单元(即，生物学、报告、观察)的CU特征的上下文级别风险因素 并跨越两个发展阶段(幼儿期和儿童期中期)。这个建议很有新意 因为它将利用计算语言方法和新的观察性编码范例来评估 隶属关系和社会沟通，这对了解癌症的风险具有重要的跨诊断意义 精神疾病。目前的提案还将通过确定特定年龄和特定领域来开辟新的视野 风险因素，从根本上促进了对CU特征如何发展的了解。拟议的R21研究是 意义重大，因为它提高了我们在RDoC社交流程中评估个人差异的能力 领域，并将确定特定领域和特定年龄的CU特征的风险因素，从根本上促进我们的 了解CU特征的发展和我们未来发展个性化和特定年龄的能力 CU性状的治疗。

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.