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.

项目概要基于机器学习的神经影像数据分类（以下简称 ML-MRI）来预测临床尽管机器学习在临床上前景广阔，但诊断在过去十年中大幅增加。分类和预测，尚未开展任何工作来预测道德义务和当机器学习算法预测症状前的临床诊断时出现的挑战婴儿脑成像研究（IBIS）最近的两份报告证明了这一点。通过预测 24 个月自闭症谱系障碍临床诊断的 ML 分类方法 (ASD) 来自 ASD 高风险和低家族风险婴儿的 6 个月 MRI 数据。结果首次证明了在自闭症谱系障碍（ASD）发病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机器学习的关键道德问题对有症状婴儿的父母进行 MRI 分类本研究旨在调查家长对 IBIS 中预测性 ML-MRI 测试的态度，并解决核心问题 BRAIN Initiative 的原则：考虑神经科学研究的伦理影响。目标 1。将审查有关基因研究结果披露的生物伦理学文献，并确定使用 ML-MRI 与基因检测进行披露的重叠和不同的道德考虑目标 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。通过这项研究获得的知识将帮助研究人员做出道德决定我们的长期目标是开发未来高危婴儿的神经影像学研究。应对神经影像学新领域带来的独特伦理挑战的指南研究：通过日益强大和复杂的基于机器学习的分析进行临床预测拟议的研究和培训计划将利用这些资源。华盛顿大学自闭症中心、特罗伊曼卡茨儿科生物伦理学中心西雅图儿童医院的多中心纵向婴儿脑成像研究。申请人将接受神经伦理学、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，并为她进行独立研究做好准备儿科神经科学中的职业伦理调查问题。

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.