Suppr超能文献

神经影像学数据存储库中数据共享政策的范围。

The spectrum of data sharing policies in neuroimaging data repositories.

机构信息

Department of Psychology, Stanford University, Stanford, California, USA.

出版信息

Hum Brain Mapp. 2022 Jun 1;43(8):2707-2721. doi: 10.1002/hbm.25803. Epub 2022 Feb 10.

Abstract

Sharing data is a scientific imperative that accelerates scientific discoveries, reinforces open science inquiry, and allows for efficient use of public investment and research resources. Considering these benefits, data sharing has been widely promoted in diverse fields and neuroscience has been no exception to this movement. For all its promise, however, the sharing of human neuroimaging data raises critical ethical and legal issues, such as data privacy. Recently, the heightened risks to data privacy posed by the rapid advances in artificial intelligence and machine learning techniques have made data sharing more challenging; the regulatory landscape around data sharing has also been evolving rapidly. Here we present an in-depth ethical and regulatory analysis that examines how neuroimaging data are currently shared against the backdrop of the relevant regulations and policies in the United States and how advanced software tools and algorithms might undermine subjects' privacy in neuroimaging data sharing. The implications of these novel technological threats to privacy in neuroimaging data sharing practices and policies will also be discussed. We then conclude with a proposal for a legal prohibition against malicious use of neuroscience data as a regulatory mechanism to address privacy risks associated with the data while maximizing the benefits of data sharing and open science practice in the field of neuroscience.

摘要

数据共享是加速科学发现、强化开放科学探究以及实现公共投资和研究资源高效利用的科学要务。鉴于这些益处,数据共享已在不同领域得到广泛推广,神经科学也不例外。然而,尽管数据共享前景广阔,但它也引发了一些关键的伦理和法律问题,例如数据隐私问题。最近,人工智能和机器学习技术的快速发展给数据隐私带来了更高的风险,使得数据共享更加具有挑战性;数据共享的监管环境也在迅速演变。在这里,我们进行了深入的伦理和法规分析,研究了在美国相关法规和政策背景下,神经影像学数据目前是如何被共享的,以及先进的软件工具和算法可能如何破坏神经影像学数据共享中受试者的隐私。我们还讨论了这些新型技术对神经影像学数据共享实践和政策中隐私的威胁所产生的影响。最后,我们提出了一项禁止恶意使用神经科学数据的法律建议,将其作为一种监管机制,以解决与数据相关的隐私风险,同时在神经科学领域最大限度地提高数据共享和开放科学实践的益处。

相似文献

1
The spectrum of data sharing policies in neuroimaging data repositories.
Hum Brain Mapp. 2022 Jun 1;43(8):2707-2721. doi: 10.1002/hbm.25803. Epub 2022 Feb 10.
2
A marathon, not a sprint - neuroimaging, Open Science and ethics.
Neuroimage. 2021 Aug 1;236:118041. doi: 10.1016/j.neuroimage.2021.118041. Epub 2021 Apr 20.
3
What Do We Mean by Sharing of Patient Data? DaSH: A Data Sharing Hierarchy of Privacy and Ethical Challenges.
Appl Clin Inform. 2024 Oct;15(5):833-841. doi: 10.1055/a-2373-3291. Epub 2024 Jul 25.
4
National Institutes of Mental Health Data Archive: Privacy, Consent, and Diversity Considerations and Options for Improvement.
AJOB Neurosci. 2022 Jan-Mar;13(1):3-9. doi: 10.1080/21507740.2021.1904025. Epub 2021 Apr 9.
5
Ethical Issues Posed by Field Research Using Highly Portable and Cloud-Enabled Neuroimaging.
Neuron. 2020 Mar 4;105(5):771-775. doi: 10.1016/j.neuron.2020.01.041.
6
Ethical, Legal, and Social Issues (ELSI) of Responsible Data Sharing Involving Children in Genomics: A Systematic Literature Review of Reasons.
AJOB Empir Bioeth. 2020 Oct-Dec;11(4):233-245. doi: 10.1080/23294515.2020.1818875. Epub 2020 Sep 25.
7
The growth and gaps of genetic data sharing policies in the United States.
J Law Biosci. 2014 Dec 20;2(1):56-68. doi: 10.1093/jlb/lsu032. eCollection 2015 Feb.
10
Addressing privacy risk in neuroscience data: from data protection to harm prevention.
J Law Biosci. 2022 Sep 4;9(2):lsac025. doi: 10.1093/jlb/lsac025. eCollection 2022 Jul-Dec.

引用本文的文献

2
Can I have your data? Recommendations and practical tips for sharing neuroimaging data upon a direct personal request.
Imaging Neurosci (Camb). 2025 Mar 19;3. doi: 10.1162/imag_a_00508. eCollection 2025.
3
Demystifying the likelihood of reidentification in neuroimaging data: A technical and regulatory analysis.
Imaging Neurosci (Camb). 2024 Mar 22;2. doi: 10.1162/imag_a_00111. eCollection 2024.
4
The future of data analysis is now: Integrating generative AI in neuroimaging methods development.
Imaging Neurosci (Camb). 2024 Jul 24;2. doi: 10.1162/imag_a_00241. eCollection 2024.
6
Pseudonymisation of neuroimages and data protection: .
Neuroimage Rep. 2021 Sep 15;1(4):100053. doi: 10.1016/j.ynirp.2021.100053. eCollection 2021 Dec.
7
The current status and future directions of artificial intelligence in the prediction, diagnosis, and treatment of liver diseases.
Digit Health. 2025 Apr 13;11:20552076251325418. doi: 10.1177/20552076251325418. eCollection 2025 Jan-Dec.
9
Spectral normative modeling of brain structure.
medRxiv. 2025 Jan 21:2025.01.16.25320639. doi: 10.1101/2025.01.16.25320639.

本文引用的文献

1
Constructing Compact Signatures for Individual Fingerprinting of Brain Connectomes.
Front Neurosci. 2021 Apr 6;15:549322. doi: 10.3389/fnins.2021.549322. eCollection 2021.
2
Changing the face of neuroimaging research: Comparing a new MRI de-facing technique with popular alternatives.
Neuroimage. 2021 May 1;231:117845. doi: 10.1016/j.neuroimage.2021.117845. Epub 2021 Feb 11.
3
The Open Brain Consent: Informing research participants and obtaining consent to share brain imaging data.
Hum Brain Mapp. 2021 May;42(7):1945-1951. doi: 10.1002/hbm.25351. Epub 2021 Feb 1.
6
Disruptive and avoidable: GDPR challenges to secondary research uses of data.
Eur J Hum Genet. 2020 Jun;28(6):697-705. doi: 10.1038/s41431-020-0596-x. Epub 2020 Mar 2.
7
Establishment of Best Practices for Evidence for Prediction: A Review.
JAMA Psychiatry. 2020 May 1;77(5):534-540. doi: 10.1001/jamapsychiatry.2019.3671.
8
Identification of Anonymous MRI Research Participants with Face-Recognition Software.
N Engl J Med. 2019 Oct 24;381(17):1684-1686. doi: 10.1056/NEJMc1908881.
9
Estimating the success of re-identifications in incomplete datasets using generative models.
Nat Commun. 2019 Jul 23;10(1):3069. doi: 10.1038/s41467-019-10933-3.
10
BrainMap VBM: An environment for structural meta-analysis.
Hum Brain Mapp. 2018 Aug;39(8):3308-3325. doi: 10.1002/hbm.24078. Epub 2018 May 2.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验