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

预测性 MRI 测试的神经伦理学：父母对自闭症谱系障碍症状前识别的态度

基本信息

批准号：
10003829
负责人：
Kate E. MacDuffie
金额：
$ 6.66万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-16 至 2020-09-15
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10003829
关键词：
Address Attitude BRAIN initiative Behavioral Model Belief Bioethics Brain Brain Diseases Brain imaging Categories Child Childhood Classification Clinical Complement Data Data Collection Data Set Decision Making Development Diagnosis Diagnostic Disclosure Disease Early Intervention Environmental Risk Factor Ethical Issues Ethics Face Future Genetic Genetic Research Genetic screening method Genomics Goals Guidelines Human Individual Infant Intention Interview Judgment Knowledge Literature Machine Learning Magnetic Resonance Imaging Measures Modality Neurosciences Neurosciences Research Parents Participant Pattern Pediatric Hospitals Population Predictive Factor Qualitative Research Reporting Research Research Methodology Research Personnel Research Training Review Literature Risk Sampling Site Surveys Symptoms Techniques Test Result Testing Time Training University resources Validation Washington Work autism onset autism spectrum disorder autistic children base career clinical Diagnosis clinical predictors frontier high risk infant imaging study interest neuroethics neuroimaging parental influence prediction algorithm predictive test programs recruit systematic review theories

项目摘要

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.

项目摘要神经成像数据的基于机器学习的分类（以下简称ML-MRI），以预测临床在过去十年中，诊断大幅增加。尽管ML在临床上有希望分类和预测，没有做任何工作来预测道德义务，当ML算法预测症状前临床诊断时出现的挑战个体婴儿脑成像研究（IBIS）最近的两份报告举例说明了通过预测自闭症谱系障碍24个月临床诊断的ML分类方法 (ASD)来自ASD高和低家族风险婴儿的6个月MRI数据。这些初步结果首次证明了在ASD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的关键伦理问题- 有症状前婴儿父母的ASD MRI分类。拟议的研究旨在调查父母对IBIS中预测性ML-MRI测试的态度，并解决了一个核心问题， BRAIN Initiative的原则：考虑神经科学研究的伦理含义。要求1 将审查关于披露遗传研究结果的生物伦理学文献，使用ML-MRI与基因检测进行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家长的态度通过定性访谈和定量调查预测ML-MRI测试。的通过这项研究获得的知识将有助于研究人员做出道德决定，在未来的高风险婴儿的神经影像学研究中披露。我们的长期目标是发展神经影像学新前沿所带来的独特伦理挑战指南研究：通过日益强大和复杂的ML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、Treuman Katz儿科生物伦理学中心在西雅图儿童医院，和多地点，纵向婴儿脑成像研究。的申请人将接受神经伦理学，儿科生物伦理学和定性研究的正式培训方法，这将补充她以前的技能，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。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Presymptomatic Detection and Intervention for Autism Spectrum Disorder.

自闭症谱系障碍的症状前检测和干预。

DOI：
10.1542/peds.2020-032250
发表时间：
2021
期刊：
Pediatrics
影响因子：
8
作者：
MacDuffie,KatherineE;Estes,AnnetteM;Harrington,LucasT;Peay,HollyL;Piven,Joseph;PruettJr,JohnR;Wolff,JasonJ;Wilfond,BenjaminS
通讯作者：
Wilfond,BenjaminS

A "salad bowl" approach to neuroethics collaboration.

神经伦理学合作的“沙拉碗”方法。