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  • Perspective
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Advocating for neurodata privacy and neurotechnology regulation

Nature Protocols volume 18, pages 2869–2875 (2023)Cite this article

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Abstract

The ability to record and alter brain activity by using implantable and nonimplantable neural devices, while poised to have significant scientific and clinical benefits, also raises complex ethical concerns. In this Perspective, we raise awareness of the ability of artificial intelligence algorithms and data-aggregation tools to decode and analyze data containing highly sensitive information, jeopardizing personal neuroprivacy. Voids in existing regulatory frameworks, in fact, allow unrestricted decoding and commerce of neurodata. We advocate for the implementation of proposed ethical and human rights guidelines, alongside technical options such as data encryption, differential privacy and federated learning to ensure the protection of neurodata privacy. We further encourage regulatory bodies to consider taking a position of responsibility by categorizing all brain-derived data as sensitive health data and apply existing medical regulations to all data gathered via pre-registered neural devices. Lastly, we propose that a technocratic oath may instill a deontology for neurotechnology practitioners akin to what the Hippocratic oath represents in medicine. A conscientious societal position that thoroughly rejects the misuse of neurodata would provide the moral compass for the future development of the neurotechnology field.

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Fig. 1: Potential approaches to protect brain data privacy.
Fig. 2: Developments in brain data decoding and neurotechnology regulation.

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References

  1. Coughlin, B. et al. Modified Neuropixels probes for recording human neurophysiology in the operating room. Nat. Protoc. https://doi.org/10.1038/s41596-023-00871-2 (2023).

  2. Steinmetz, N. A. et al. Neuropixels 2.0: a miniaturized high-density probe for stable, long-term brain recordings. Science 372, eabf4588 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Paulk, A. C. et al. Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex. Nat. Neurosci. 25, 252–263 (2022).

    CAS  PubMed  Google Scholar 

  4. Tang, J., LeBel, A., Jain, S. J. & Huth, A. G. Semantic reconstruction of continuous language from non-invasive brain recordings. Nat. Neurosci. 26, 858–866 (2023).

    CAS  PubMed  Google Scholar 

  5. Takagi, Y. & Nishimoto, S. High-resolution image reconstruction with latent diffusion models from human brain activity. Preprint at https://www.biorxiv.org/content/10.1101/2022.11.18.517004v2 (2022).

  6. Défossez, A., Caucheteux, C., Rapin, J. & Kabeli, O. Decoding speech from non-invasive brain recordings. Preprint at https://arxiv.org/abs/2208.12266 (2022).

  7. Chen, Z., Qing, J., Xiang, T., Yue, W. & Zhou, J. L. Seeing beyond the brain: conditional diffusion model with sparse masked modeling for vision decoding. Preprint at https://arxiv.org/abs/2211.06956 (2022).

  8. Grover, S., Wen, W., Viswanathan, V., Gill, C. & Reinhart, R. Long-lasting, dissociable improvements in working memory and long-term memory in older adults with repetitive neuromodulation. Nat. Neurosci. 25, 1237–1246 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Alivisatos, A. P. et al. Nanotools for neuroscience and brain activity mapping. ACS Nano 7, 1850–1866 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Nicolelis, M. A. & Lebedev, M. A. Principles of neural ensemble physiology underlying the operation of brain-machine interfaces. Nat. Rev. Neurosci. 10, 530–540 (2009).

    CAS  PubMed  Google Scholar 

  11. Hochberg, L. R. et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–171 (2006).

    CAS  PubMed  Google Scholar 

  12. Alivisatos, A. P. et al. Neuroscience. The brain activity map. Science 339, 1284–1285 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Insel, T. R., Landis, S. C. & Collins, F. S. The NIH BRAIN Initiative. Science 340, 687–688 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Jorgenson, L. A. et al. The BRAIN Initiative: developing technology to catalyse neuroscience discovery. Philos. Trans. R. Soc. Lond. B Biol. Sci. 370, 20140164 (2015).

    PubMed  PubMed Central  Google Scholar 

  15. Yuste, R. & Bargmann, C. Toward a global BRAIN initiative. Cell 168, 956–959 (2017).

    CAS  PubMed  Google Scholar 

  16. Adams, A. et al. International brain initiative: an innovative framework for coordinated global brain research efforts. Neuron 105, 212–216 (2020).

    Google Scholar 

  17. The Neurorights Foundation: Market Analysis: Neurotechnology. Available at https://neurorightsfoundation.org/publications (2023).

  18. Yuste, R. From the neuron doctrine to neural networks. Nat. Rev. Neurosci. 16, 487–497 (2015).

    CAS  PubMed  Google Scholar 

  19. Miller, J. E., Ayzenshtat, I., Carrillo-Reid, L. & Yuste, R. Visual stimuli recruit intrinsically generated cortical ensembles. Proc. Natl. Acad. Sci. USA. 111, E4053–E4061 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Carrillo-Reid, L., Han, S., Yang, W., Akrough, A. & Yuste, R. Controlling visually guided behavior by holographic recalling of cortical ensembles. Cell 178, 447–457.e5 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Marshel, J. H. et al. Cortical layer-specific critical dynamics triggering perception. Science 365, eaaw5202 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Ramirez, S. et al. Creating a false memory in the hippocampus. Science 341, 387–391 (2013).

    CAS  PubMed  Google Scholar 

  23. Hamm, J. P., Peterka, D. S., Gogos, J. A. & Yuste, R. Altered cortical ensembles in mouse models of schizophrenia. Neuron 94, 153–167.e8 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Wenzel, M., Hamm, J. P., Peterka, D. S. & Yuste, R. Acute focal seizures start as local synchronizations of neuronal ensembles. J. Neurosci. 39, 8562–8575 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Benabid, A. L. Deep brain stimulation for Parkinson’s disease. Curr. Opin. Neurobiol. 13, 696–706 (2003).

    CAS  PubMed  Google Scholar 

  26. Burke, M. J., Fried, P. J. & Pascual-Leone, A. Transcranial magnetic stimulation: neurophysiological and clinical applications. Handb. Clin. Neurol. 163, 73–92 (2019).

    PubMed  Google Scholar 

  27. Tripp, S. & Grueber, M. Economic Impact of the Human Genome Project. Battelle Laboratory. Available at https://www.battelle.org/docs/default-source/misc/battelle-2011-misc-economic-impact-human-genome-project.pdf (Battelle Memorial Institute, 2011).

  28. Wexler, A. & Reiner, P. B. Oversight of direct-to-consumer neurotechnologies. Science 363, 234–235 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Anumanchipalli, G. K., Chartier, J. & Chang, E. F. Speech synthesis from neural decoding of spoken sentences. Nature 568, 493–498 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Willett, F. R., Avansino, D. T., Hochberg, L. R., Henderson, J. M. & Shenoy, K. V. High-performance brain-to-text communication via handwriting. Nature 593, 249–254 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Kay, K. N., Naselaris, T., Prenger, R. J. & Gallant, J. L. Identifying natural images from human brain activity. Nature 452, 352–355 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Ghashghaei, H. T., Hilgetag, C. C. & Barbas, H. Sequence of information processing for emotions based on the anatomic dialogue between prefrontal cortex and amygdala. Neuroimage 34, 905–923 (2007).

    CAS  PubMed  Google Scholar 

  33. Kragel, P., Knodt, A., Hariri, A. & LaBar, K. Decoding spontaneous emotional states in the human brain. PLoS Biol. 14, e2000106 (2016).

    PubMed  PubMed Central  Google Scholar 

  34. Chang, C., Nastase, S. & Hasson, U. Information flow across the cortical timescale hierarchy during narrative construction. Proc. Natl. Acad. Sci. USA. 119, e2209307119 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Momi, D. et al. Cognitive enhancement via network-targeted cortico-cortical associative brain stimulation. Cereb. Cortex 30, 1516–1527 (2020).

    CAS  PubMed  Google Scholar 

  36. Goering, S. & Yuste, R. On the necessity of ethical guidelines for novel neurotechnologies. Cell 167, 882–885 (2016).

    CAS  PubMed  Google Scholar 

  37. Klein, E., Brown, T., Sample, M., Truitt, A. R. & Goering, S. Engineering the brain: ethical issues and the introduction of neural devices. Hastings Cent. Rep. 45, 26–35 (2015).

    PubMed  Google Scholar 

  38. Yuste, R. et al. Four ethical priorities for neurotechnologies and AI. Nature 551, 159–163 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Lewis, C. J. et al. Subjectively perceived personality and mood changes associated with subthalamic stimulation in patients with Parkinson’s disease. Psychol. Med. 45, 73–85 (2015).

    CAS  PubMed  Google Scholar 

  40. Pham, U. et al. Personality changes after deep brain stimulation in Parkinson’s disease. Parkinsons Dis. 2015, 49057 (2015).

    Google Scholar 

  41. Goering, S., Klein, E., Dougherty, D. D. & Widge, A. S. Staying in the loop: relational agency and identity in next-generation DBS for psychiatry. AJOB Neurosci. 8, 59–70 (2017).

    Google Scholar 

  42. Farah, M. J. & Heberlein, A. S. Personhood and neuroscience: naturalizing or nihilating? Am. J. Bioeth. 7, 37–48 (2007).

    PubMed  Google Scholar 

  43. Goering, S. et al. Recommendations for responsible development and application of neurotechnologies. Neuroethics 14, 365–386 (2021).

    PubMed  PubMed Central  Google Scholar 

  44. Information Commissioner’s Office. ICO Tech Futures: Neurotechnology. Available at https://ico.org.uk/about-the-ico/research-and-reports/ico-tech-futures-neurotechnology/ (2023).

  45. Eaton, M. L. & Illes, J. Commercializing cognitive neurotechnology—the ethical terrain. Nat. Biotechnol. 25, 393–397 (2007).

    CAS  PubMed  Google Scholar 

  46. Kellmeyer, P. Ethical issues in the application of machine learning to brain disorders. In Machine Learning (eds. Mechelli, A. & Vieira, S.) 329–342 (Academic Press, 2020).

  47. Kreitmair, K. V. Dimensions of ethical direct-to-consumer neurotechnologies. AJOB Neurosci. 10, 152–166 (2019).

    PubMed  Google Scholar 

  48. Steinert, S. & Friedrich, O. Wired emotions: ethical issues of affective brain–computer interfaces. Sci. Eng. Ethics 26, 351–367 (2020).

    PubMed  Google Scholar 

  49. Antal, A. et al. Low intensity transcranial electric stimulation: safety, ethical, legal regulatory and application guidelines. Clin. Neurophysiol. 128, 1774–1809 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Wexler, A. The practices of do-it-yourself brain stimulation: implications for ethical considerations and regulatory proposals. J. Med. Ethics 42, 211–215 (2016).

    PubMed  Google Scholar 

  51. Klein, E. et al. Brain-computer interface-based control of closed-loop brain stimulation: attitudes and ethical considerations. Brain Comput. Interfaces (Abingdon) 3, 140–148 (2016).

    Google Scholar 

  52. Riggall, K. et al. Researchers’ perspectives on scientific and ethical issues with transcranial direct current stimulation: an international survey. Sci. Rep. 5, 10618 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Hildt, E. What will this do to me and my brain? Ethical issues in brain-to-brain interfacing. Front. Syst. Neurosci. 9, 17 (2015).

    PubMed  PubMed Central  Google Scholar 

  54. Parens, E. Enhancing Human Traits: Ethical and Social Implications (Georgetown University Press, 2000).

  55. Juengst, E. What does “enhancement” mean? In (ed., Parens, E.) Enhancing Human Traits: Ethical and Social Implications (Georgetown University Press, 1998).

  56. Wexler, A. Who uses direct-to-consumer brain stimulation products, and why? A study of home users of tDCS devices. J. Cogn. Enhanc. 2, 114–134 (2018).

    Google Scholar 

  57. Wexler, A. A pragmatic analysis of the regulation of consumer transcranial direct current stimulation (TDCS) devices in the United States. J. Law Biosci. 2, 669–696 (2016).

    Google Scholar 

  58. OECD-Council. OECD Recommendation on Responsible Innovation in Neurotechnology. (Organisation for Economic Co-operation and Development, 2019).

  59. Ienca, M., Haselager, P. & Emanuel, E. J. Brain leaks and consumer neurotechnology. Nat. Biotechnol. 36, 805–810 (2018).

    CAS  PubMed  Google Scholar 

  60. Ienca, M., Jotterand, F. & Elger, B. S. From healthcare to warfare and reverse: how should we regulate dual-use neurotechnology? Neuron 97, 269–274 (2018).

    CAS  PubMed  Google Scholar 

  61. Ienca, M. & Andorno, R. Towards new human rights in the age of neuroscience and neurotechnology. Life Sci. Soc. Policy 13, 5 (2017).

    PubMed  PubMed Central  Google Scholar 

  62. Ienca, M. et al. Towards a governance framework for brain data. Neuroethics 15, 20 (2022).

    Google Scholar 

  63. Wexler, A. Separating neuroethics from neurohype. Nat. Biotechnol. 37, 988–990 (2019).

    CAS  PubMed  Google Scholar 

  64. Greely, H. T. et al. Neuroethics guiding principles for the NIH BRAIN initiative. J. Neurosci. 38, 10586–10588 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. IEEE. IEEE Neuroethics Framework. Available at https://brain.ieee.org/publications/ieee-neuroethics-framework/ (2021).

  66. International Bioethics Committee of UNESCO. Ethical Issues of Neurotechnology. Available at https://unesdoc.unesco.org/ark:/48223/pf0000383559 (2022).

  67. Farahany, N. A. The Battle for Your Brain: Defending the Right to Think Freely in the Age of Neurotechnology (St. Martin’s Press, 2023).

  68. Yuste, R., Genser, J. & Herrmann, S. It’s time for Neuro-Rights. Horizons 18, 154–164 (2021).

    Google Scholar 

  69. Borbón, D. & Borbón, L. A critical perspective on NeuroRights: comments regarding ethics and law. Front. Hum. Neurosci. 15, 703121 (2021).

    PubMed  PubMed Central  Google Scholar 

  70. Bublitz, C. Novel neurorights: from nonsense to substance. Neuroethics 15, 7 (2022).

    PubMed  PubMed Central  Google Scholar 

  71. Susser, D. & Cabrera, L. Brain data in context: are new rights the way to mental and brain privacy? AJOB Neurosci. 5, 1–12 (2023).

    Google Scholar 

  72. Fins, J. J. The unintended consequences of Chile’s neurorights constitutional reform: moving beyond negative rights to capabilities. Neuroethics 15, 26 (2022).

    Google Scholar 

  73. Rainey, S. Neurorights as Hohfeldian privileges. Neuroethics 16, 9 (2023).

    Google Scholar 

  74. Herrmann, S., Yuste, R. & Genser, J. Neurorights Foundation: Gap Analysis. Available at https://static1.squarespace.com/static/60e5c0c4c4f37276f4d458cf/t/6275130256dd5e2e11d4bd1b/1651839747023/Neurorights+Foundation+PUBLIC+Analysis+5.6.22.pdf (2022).

  75. Library of the National Congress of Chile. Law 21383: Amends the Fundamental Charter, to Establish Scientific and Technological Development at the Service of People. Available at https://www.bcn.cl/leychile/navegar?idNorma=1166983&tipoVersion=0 (2021).

  76. Republica de Chile Senado. C.N. BIll. Available at https://www.senado.cl/appsenado/templates/tramitacion/index.php?boletin_ini=13828-19 (2020).

  77. Government of Spain. Carta Derechos Digitales. Available at https://www.lamoncloa.gob.es/presidente/actividades/Documents/2021/140721-Carta_Derechos_Digitales_RedEs.pdf (2021).

  78. Yuste, R., Quadra-Salcedo, T. & Fernandez, M. G. Neurorights and new charts of digital rights: a dialogue. Indiana J. Global Leg. Studies 30, 1 (2023).

    Google Scholar 

  79. Organization of American States. Declaration of the Interamerican Juridical Committee on Neuroscience, Neurotechnologies and Human Rights: New Legal Challenges for the Americas. CJI/DEC. 01 (XCIX-O/21) (2021).

  80. Committee of the Council of Europe. Committee on Bioethics of the Council of Europe, Strategic Action Plan on Human Rights and Technologies in Biomedicine (2020–2025). Adopted by the Committee on Bioethics (DH-BIO) at its 16th meeting (19–21 November 2019) (2019).

  81. UNESCO. Report of the International Bioethics Committee of UNESCO, Ethical Issues of Neurotechnology, SHS/BIO/IBC28/2021/3Rev (2021).

  82. OECD. Recommendation on Responsible Innovation in Neurotechnology, Adopted by the OECD Council on 11 December 2019 (2019).

  83. United Nations. United Nations, Our Common Agenda—Report of the Secretary-General, New York 2021, par. 35 (2021).

  84. Human Rights Council. Assessing the Human Rights Impact of Neurotechnology: Towards the Recognition of ‘Neurorights’. Available at https://www.ohchr.org/sites/default/files/documents/hrbodies/hrcouncil/advisorycommittee/session28/2022-08-09/AC28-Human-rights-impact-of-neurotechnology.docx (2022).

  85. Lauter, K., Naehrig, M. & Vaikuntanathan, V. Can homomorphic encryption be practical? Available at https://eprint.iacr.org/2011/405.pdf (2011).

  86. Dwork, C. Differential Privacy and the US Census. Available at https://dl.acm.org/doi/pdf/10.1145/3294052.3322188?download=true (2019).

  87. Abadi, M. et al. Deep Learning with Differential Privacy. Available at https://dl.acm.org/doi/pdf/10.1145/2976749.2978318?download=true (2016).

  88. McMahan, B. & Ramage, D. Federated Learning: Collaborative Machine Learning without Centralized Training Data. Available at https://ai.googleblog.com/2017/04/federated-learning-collaborative.html (2017).

  89. Choudhury, O. et al. Differential privacy-enabled federated learning for sensitive health data. Preprint at https://arxiv.org/abs/1910.02578 (2020).

  90. US Congress. Health Insurance Portability and Accountability Act of 1996. Public Law 104, 191 (1996).

    Google Scholar 

  91. Voigt, P. & Von dem Bussche, A. The EU general data protection regulation (GDPR). In A Practical Guide (Springer, 2017).

  92. Rainey, S. et al. Is the European data protection regulation sufficient to deal with emerging data concerns relating to neurotechnology? J. Law Biosci. 7, lsaa051 (2020).

    PubMed  PubMed Central  Google Scholar 

  93. State of California. California Legislative Information. Title 1.81.5. California Consumer Privacy Act of 2018. Available at https://leginfo.legislature.ca.gov/faces/codes_displayText.xhtml?division=3.&part=4.&lawCode=CIV&title=1.81.5 (1988).

  94. Observational Health Data Sciences and Informatics (OHDSI). Available at https://www.ohdsi.org/ (2023).

  95. McMahan, H. B., Moore, E., Ramage, D. & Hampson, S. Communication-efficient learning of deep networks from decentralized data. Preprint at https://arxiv.org/abs/1602.05629 (2016).

  96. Rieke, A., Yu, H., Robinson, D. & van Hoboken, J. Data Brokers in an Open Society (Open Society Foundation, London, UK, 2016).

  97. Tanner, A. How data brokers make money off your medical records. Sci. Am. 314, 26–27 (2016).

    PubMed  Google Scholar 

  98. Rocher, L., Hendrickx, J. M. & de Montjoye, Y.-A. Estimating the success of re-identifications in incomplete datasets using generative models. Nat. Commun. 10, 3069 (2019).

    PubMed  PubMed Central  Google Scholar 

  99. Sweeney, L. Simple demographics often identify people uniquely. Health (San Francisco) 671, 1–34 (2000).

    Google Scholar 

  100. Azemi, E. et al. Biosignal sensing device using dynamic selection of electrodes. US Patent 20230225659 A1 Available at https://ppubs.uspto.gov/pubwebapp/ (2023).

  101. Government of the United Kingdom. Regulatory Horizons Council (RHC) Publishes Independent Recommendations on the Future Regulation of Neurotechnology and AI as a Medical Device. Available at https://www.gov.uk/government/news/regulatory-horizons-council-rhc-publishes-independent-recommendations-on-the-future-regulation-of-neurotechnology-and-ai-as-a-medical-device (2022).

  102. European Union. Commission Implementing Regulation (EU) 2022/2346 of 1 December 2022 Laying Down Common Specifications for the Groups of Products without an Intended Medical Purpose Listed in Annex XVI to Regulation (EU) 2017/745 of the European Parliament and of the Council on Medical Devices. Available at https://eur-lex.europa.eu/eli/reg_impl/2022/2346/oj (2023).

  103. US Department of Health and Human Services. The Belmont Report. Available at https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/read-the-belmont-report/index.html (1979).

  104. Alamos, M. F. et al. A technochratic oath. In Protecting the Mind: Challenges in Law, Neuroprotection, and Neurorights (eds. Varela, L. & Lopez, P.) (Springer, 2022).

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Acknowledgements

This work was supported by Columbia University’s Precision Medicine & Society Program and the Sloan Foundation. The author thanks G. Hripcsak, J. Genser, J. Davies and S. Neustadter for discussions and E. Einhorn for assistance.

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    Rafael Yuste

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The author discloses his role as chairman of the Neurorights Foundation, whose goal is to promote research, advocacy and outreach on neurorights.

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Yuste, R. Advocating for neurodata privacy and neurotechnology regulation. Nat Protoc 18, 2869–2875 (2023). https://doi.org/10.1038/s41596-023-00873-0

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