Neuroethics of Predictive MRI Testing: Parental Attitudes Towards Pre-Symptomatic Identification of Autism Spectrum Disorder

项目总结 基于机器学习的神经影像数据分类(以下简称ML-MRI)用于临床预测 在过去的十年里，诊断病例大幅增加。尽管ML在临床上有希望 分类和预测，还没有做任何工作来预测道德义务和 ML算法在无症状的情况下预测临床诊断时出现的挑战 个人。婴儿脑成像研究(IBIS)最近的两份报告举例说明了 预测自闭症谱系障碍24个月临床诊断的ML分类法 (ASD)来自ASD家族高风险和低风险婴儿6个月的MRI数据。这些初步的 结果首次证明了在自闭症发作前识别婴儿的可行性。 症状，并提出了关于是否以及如何披露预测性ML- 先证儿父母ASD的MRI分型。这项拟议的研究旨在 调查父母对IBIS中预测性ML-MRI测试的态度，并解决一个核心问题 大脑主动性原则：考虑神经科学研究的伦理含义。目标1 将回顾关于基因研究结果披露的生命伦理学文献，并确定 使用ML-MRI披露与使用基因测试披露的重叠和不同的伦理考虑 疾病预测。目标2将使用基于理论的方法来评估IBIS父母的态度 通过定性访谈和定量调查进行预测性ML-MRI测试。这个 通过这项研究获得的知识将帮助调查人员做出关于 在未来对高危婴儿的神经成像研究中的披露。我们的长远目标是发展 应对神经成像新前沿带来的独特伦理挑战的指导方针 研究：通过日益强大和复杂的基于ML的分析进行临床预测 大型神经成像数据集。拟议的研究和培训计划将利用资源 华盛顿大学自闭症中心、特鲁曼·卡茨儿科生命伦理学中心 在西雅图儿童医院，和多点，纵向婴儿脑成像研究。这个 申请者将接受神经伦理学、儿科生物伦理学和定性研究方面的正式培训。 方法，这将补充她之前的技能，并为她的独立研究做好准备 研究儿科神经科学中的伦理问题。

PROJECT SUMMARY Machine-learning-based classification of neuroimaging data (hereafter ML-MRI) to predict clinical diagnoses has increased substantially in the last decade. Despite the promise of ML for clinical classification and prediction, no work has been done to anticipate the ethical obligations and challenges that emerge when ML algorithms predict clinical diagnoses in pre-symptomatic individuals. Two recent reports from the Infant Brain Imaging Study (IBIS) have exemplified the ML classification approach by predicting 24-month clinical diagnosis of autism spectrum disorder (ASD) from 6-month MRI data in infants at high and low familial risk for ASD. These preliminary results demonstrated—for the first time—the feasibility of identifying infants prior to onset of ASD symptoms, and raised critical ethical questions about whether and how to disclose predictive ML- MRI classification of ASD to parents of pre-symptomatic infants. The proposed research aims to investigate parental attitudes towards predictive ML-MRI testing in IBIS, and addresses a core principle of BRAIN Initiative: considering the ethical implications of neuroscience research. Aim 1 will review the bioethics literature on disclosure of results in genetic research, and identify overlapping and distinct ethical considerations for disclosure using ML-MRI vs. genetic testing for disease prediction. Aim 2 will use a theory-based approach to assess the attitudes of IBIS parents towards predictive ML-MRI testing through qualitative interviews and a quantitative survey. The knowledge gained through this research will help investigators make ethical decisions about disclosure in future neuroimaging studies of high-risk infants. Our long-term goal is to develop guidelines for navigating the unique ethical challenges posed by a new frontier in neuroimaging research: clinical prediction via increasingly powerful and sophisticated ML-based analyses of large neuroimaging datasets. The proposed research and training plan will leverage the resources of the University of Washington Autism Center, the Treuman Katz Center for Pediatric Bioethics at Seattle Children’s Hospital, and the multi-site, longitudinal Infant Brain Imaging Study. The applicant will receive formal training in neuroethics, pediatric bioethics, and qualitative research methods, which will complement her prior skillset and prepare her for an independent research career investigating ethical issues in pediatric neuroscience.