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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要冷酷无情（CU）特征，由低同理心、内疚感和亲社会性定义，预示着非常高的风险儿童期破坏性行为障碍 (DBD) 和成人不良后果，包括暴力、精神病、和犯罪。 DBD 的标准治疗对于具有 CU 特征的儿童并不那么有效。告知个性化对于 DBD 的治疗，需要更好地了解 CU 特征的具体风险因素。幼儿期。先前的研究仅限于关注单个风险领域或单个风险因素。年龄点。因此，根据现有文献，我们不知道 DBD 和 CU 特征的哪些风险因素对大多数也不在什么年龄最重要，包括最有影响力的机制可能是其特点是跨领域和跨年龄的相互作用和非线性关联。此外，虽然之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），存在重大知识差距，并且专注于以下方面的可用措施较少社会过程域和 CU 特征之间的链接。为了解决这些知识差距，目标该 R21 提案旨在： (1) 实施新开发的行为编码范式，用于评估从属关系（例如，言语和身体上表达感情）和社交沟通（例如，眼神、参与、同步）在亲子互动过程中； (2) 采用自动化方法识别客观语言这些域的标记； (3) 使用机器学习 (ML) 方法来识别特定领域和特定年龄的精确风险因素，最能预测幼儿期和幼儿期的 CU 特征。我们利用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)，其中包括不同样本的广泛观察、生物学和问卷调查报告数据的儿童及其家庭在幼儿期（18、24、30 和 36 个月）和中期接受了 7 次评估童年（5、6 和 7 岁），家长报告测量 7-8 岁时的 CU 特征。我们将测试孩子、父母，以及跨不同分析单元（即生物学、报告、观察）的 CU 特征的背景风险因素并跨越两个发展阶段（幼儿期和幼儿期）。这个提议很有创新性因为它将利用计算语言方法和新的观察编码范式来评估归属感和社会沟通，这对于理解风险具有重要的跨诊断意义精神疾病。当前的提案还将通过确定特定年龄和特定领域来开辟新视野风险因素，从根本上增进对 CU 特征如何发展的了解。拟议的 R21 研究是意义重大，因为它提高了我们评估 RDoC 社会流程中个体差异的能力领域，并将识别 CU 特征的特定领域和特定年龄的风险因素，从根本上推进我们的了解 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 CU 特征的治疗。