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.

项目摘要基于机器学习的神经影像学数据（以下简称ML-MRI）预测临床在过去的十年中，诊断大幅增加。尽管有ML临床的承诺分类和预测，尚未做任何事情来预测道德义务和当ML算法预测症状前临床诊断时，会出现的挑战个人。婴儿脑成像研究（IBI）的两份最新报道已经举例说明了通过预测自闭症谱系障碍的24个月临床诊断，ML分类方法（ASD）来自ASD的高和低家庭风险的婴儿的6个月MRI数据。这些初步结果首次证明了在ASD发作之前识别婴儿的可行性症状，并提出了有关是否以及如何披露预测性ML-的关键道德问题 ASD对症状前婴儿父母的MRI分类。拟议的研究旨在调查父母的吸引力，以实现IBI的预测性ML-MRI测试，并解决了核心大脑倡议原则：考虑神经科学研究的道德意义。目标1 将回顾有关遗传研究结果披露的生物伦理学文献，并确定使用ML-MRI与基因检测的重叠和独特的道德考虑因素疾病预测。 AIM 2将使用基于理论的方法来评估宜必思父母的参与者通过定性访谈和定量调查进行预测性ML-MRI测试。这通过这项研究获得的知识将帮助调查人员做出有关道德决定未来对高风险婴儿的神经影像学研究中的披露。我们的长期目标是发展在神经影像中引发的新领域带来的独特道德挑战指南研究：通过越来越强大且基于ML的分析的临床预测大型神经影像学数据集。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的小儿生物伦理学中心在西雅图儿童医院和多部位的纵向婴儿脑成像研究。这申请人将接受神经伦理学，小儿生物伦理学和定性研究的正式培训方法，这将补充她先前的技能，并为她准备独立研究职业研究小儿神经科学中的道德问题。

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）

The Ethics of Predicting Autism Spectrum Disorder in Infancy.

DOI：
10.1016/j.jaac.2021.01.006
发表时间：
2021-08
期刊：
Journal of the American Academy of Child and Adolescent Psychiatry
影响因子：
13.3
作者：
MacDuffie KE;Estes AM;Peay HL;Pruett JR Jr;Wilfond BS
通讯作者：
Wilfond BS

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.