Are Neural Data Protected by Bodily Integrity? A Discussion of the ‘Organic’ View on Neural Data Rights

By Abel Wajnerman Paz

Image courtesy of VPNs ‘R Us
It has been pointed out repeatedly that third-party access to personal neural data (ND, data about a person’s neural activity) implies serious risks for patients/research subjects, such as health insurance or bank-issued credit denial (Glannon 2017). Also, there are increasing controversial applications of ND to contexts not related to health-care, such as mining data from employees or students for labor/learning enhancement, which may involve problematic violations of mental privacy. 

These facts support the idea that ND control and access should be legally regulated. Sara Goering and Rafael Yuste claim that treating legally personal ND as a body organ could help to avoid main sharing risks (Yuste, Goering et al. 2017, Goering & Yuste 2016). This is would imply, for instance, that ND can be donated exclusively by individual patients and cannot be commercialized. Yuste has been recently proposing in South America that each country should implement this idea as part of a set of constitutional neurorights. Being the first country that shaped this proposal into a bill that will be plausibly considered by its Senate during 2020, Chile has become a pilot case.  

However, to the best of my knowledge, the view has not yet been subjected to philosophical discussion. I think that this is necessary because it is based on conceptual assumptions that will constraint ND management in relevant ways but are nonetheless far from obvious. Treating legally something as something else requires some kind of analogical reasoning. In legal reasoning, analogy involves an earlier decision being followed in a later case because the later case is sufficiently similar to the earlier one (Lamond 2006). Thus, we can assume that the ‘organic’ approach is grounded on an analogy between ND and body organs. The analogy seems to be based on the fact that ND is built from (or constituted by) biological signals produced by our own brains. Nevertheless, I will propose three main reasons to show that the basis of this analogy falls short.

Three disanalogies 

Externally produced data 
Unlike organs and organic tissue, ND are in part produced by others outside of our bodies. As Montgomery (2017) has pointed out, there are two ‘external’ sources of health-care related data: (1) analyses performed by clinicians/researchers and (2) information about other patients’ bodies.   

In neuroscience, graph-theoretic analysis is a nice example of the difficult theoretical decisions that have to be made before we obtain clinically useful ND (Telesford et al. 2011). Defining a ‘parcellation scheme’ (i.e., what neural structures we are going to consider as network nodes) may be theoretically challenging at the neural macro-scale, where it is difficult to draw anatomical and functional boundaries (see Fornito et al. 2016, ch. 2). There are also challenges related to the matrix representing connections between these nodes, such as identifying causal (or efficient) connections (Friston 2011).

From Tymofiyeva et al. (2012). An example of two alternative parcellation schemes of the six-month old baby brain, a) equipartition and b) gridded parcellation and their corresponding adjacency matrices, c) and d), where each point represents the relation between two nodes (black spaces indicate the presence of a connection whereas white spaces indicate the lack thereof).

Finally, graph-theoretic analysis is applied to the matrix by using complex graph metrics. The most important are measures of segregation between network nodes (such as clustering coefficient, i.e., how much neighbors of a given node are interconnected, forming isolated clusters) and measures of integration (such as characteristic path length, i.e., the average shortest path length across all pairs of nodes, indicating how ‘accessible’ any given node is to any other) (Karwowski et al. 2019). The combination of these measures defines different kinds of networks, such as regular (high clustering coefficient and long average path length), random (low clustering coefficient and a short average path length) and small-world networks (high clustering coefficient and a short path length). In one well-known study, clustering coefficient and average path length have been used to identify the small-world structure of neural networks (Sporns et al. 2004).

From Karwowski et al. (2019). Main global measures applied to brain networks: (A) clustering coefficient is an example of a segregation measure whereas (B) characteristic path length exemplifies an integration measure. These measures characterize (C) regular, a random and small-world networks.

It is often only at this final stage of analysis that ND can be clinically used. For instance, the degree of “small-worldness” in a brain network (i.e., the extent to which it has small-world properties) has been proposed as a possible biomarker for psychiatric diseases. Studies suggest that in contrast to healthy small-world networks, a shift towards a randomized structure with reduced segregation or clustering can be found in schizophrenic patients whereas a shift towards regular configuration with a decreased integration can be found in patients with Alzheimer’s disease (Liao et al. 2017). Thus, ND is often not a mere ‘copy’ of neural signals (i.e., raw data) but rather a highly elaborated construction by clinicians and researchers. As Montgomery (2017) suggested, denying some degree of data ownership to these professionals would require at least to discuss the idea that we have rights over the product of our physical and/or intellectual labor (e.g., Locke 1689, Nozick 1974).  

In the second place, clinically valuable data always includes information about other brains. As Montgomery (2017) observed regarding genomic information, the medical advice to a given patient is inferred by comparing the specific information about her brain with ‘normal’ expectations, which are constituted by a reference base of information from other brains. The formation of big neural data repositories containing information from many brains is a crucial step for the identification of validated biomarkers for neurodegenerative diseases, which in turn is necessary for the development of efficient treatment strategies (Fraiser 2016). This point is also crucial because it implies that ND sharing is required by reciprocity. 

Data secretion
Image courtesy of Pixabay
Of course, there is a part of ND that is produced exclusively by individuals. Our own neural activity constitutes the set of source signals from which ND is built. As I mentioned, this seems to be the basis of the ‘organic’ analogy. However, even in this case we have reason to doubt patient ownership. It can be argued that neural signals are not constitutive parts of the body and therefore, unlike the case of body organs, ownership cannot be grounded on bodily integrity. A main reason for this is that in non-invasive techniques (which are the most widely employed in humans), ND are obtained from organic material that is naturally expelled outside of the body, in the same way as secretions and excretions. For instance, waves of ions produced by many neurons are expelled outside of our head, forming an external signal that gets picked up by EEG electrodes. When ND signals are naturally secreted, taking or manipulating them does not require the kind of consent involved in organ extraction because there is no harvesting procedure. 

Furthermore, there is a debate regarding whether there is any other ground for patient ownership of biological materials that are separated from her body. The question about whether these materials belong to its source or to a medical research institute have led to significant litigation (See Harris 1996, Brownsword 2016, Maddox 2016, Nwabueze 2016, 2019).  

Neural data copyright
Finally, even if we concede that any organic material that has the patient’s body as its source has to belong to her, we must notice that ND is not constituted by any organic material at all. This can be explained by using Borgatti’s distinction between information replication and transfer (Borgatti 2005). Communication via transfer is simply moving an information carrying object from a source to a receiver (e.g., mailing a letter to another person). In turn, communication via replication is reproducing a new (materially different) copy of a message at another point of a network (e.g., the spread of a viral infection through direct contact, in which information at one host is replicated at another, i.e., the original material message does not leave the source). 

Unlike a biopsy, in which the tissue containing medical data is extracted and preserved for analysis, ‘harvesting’ ND is often a form of information replication. We can stick to the previous EEG example for illustrating this point. The basic components of an EEG system include electrodes or voltage sensors, amplifiers, and output devices. First, the data contained in neural waves of ions is replicated by a materially different signal constituted by electrons in EEG electrodes, which are affected by (or correlated with) those waves. After this second signal is amplified, data is replicated again in a new material format by the output devices. In classic analog EEG this involves a galvanometer-driven pen-writing system which offloads ND on paper. In current digital EEG the amplified signal is sent to an ADC (analog-to-digital converter) circuit that produces a digital signal (a high-resolution sampling of the original analog signal) constituted by strings of digits that can be stored in a variety of physical media (Yeh 2012). This means that the physical ND register that is kept by clinicians or researchers for analysis is often not constituted by any organic material at all.

From Yeh (2012). Comparison between analog and digital EEG systems.

It is interesting that this feature of ND not only constitutes a disanalogy between ND and body organs (or organic tissue in general) but also may support an alternative analogy that is inconsistent with the organic view. The rights over a physical copy or replica of data produced by my mind/brain seem closer to intellectual rights than to the right to physical integrity. This is problematic because copyright protects economic rights, which allow right owners to derive financial reward from the use of the information they produced. Unlike the organic approach, this analogy would allow the possibility of ND commercialization. 

A distributed cognition approach to ND

We saw that neural data sharing implies real risks.  However, as I mentioned, these risks are part of a more complex trade-off. Sharing is required for the formation of neural big data repositories, which is a crucial step for determining validated biomarkers for Parkinson’s and Alzheimer’s diseases. The relevance of this goal can be emphasized by mentioning that the creation of large data platforms was an stated aim of most of the national-level brain initiatives, which in turn led to the creation of a virtual International Brain Station as part of the recently formed International Brain Initiative (IBI) (Rommelfanger et al. 2018, Adams et al. 2020).  

This suggests that ND management may require to focus not only on the ND source (i.e., individual patients) but also on the larger (local, regional, national or global) ND communities (NDC) dedicated to data diffusion, processing and application. In this vein, we may study and analyze an NDC as a Distributed Cognitive System (Furniss et al. 2019) in which the mentioned trade-off must be optimized. 

Image courtesy of Pixabay
Interestingly, some strategies for facilitating the construction of health data repositories actually involve granting individual data rights to patients (and therefore minimizing sharing risks). Some authors are proposing patients/subjects’ data access, use and governance as a tool to optimize data liquidity, that is, data flow between repositories (e.g., Terry & Terry 2011, Vayena & Blasimme 2017). Patient data access and control would make them informational hubs that may be able to optimize ND transmission in fragmented health-care networks.

Grounding personal ND rights on informational efficiency not only takes into consideration the needs of the whole health-care community (i.e., building big ND repositories) but also, by making the organic analogy unnecessary, it also helps to get rid of sharing constraints (e.g., not using economic incentives, denying some degree of researcher/clinician ownership, etc.) which may affect negatively ND sharing optimization.   

Most importantly, this approach lines up with the idea, advanced by the Global Neuroethics Summit (one of IBI’s working groups), that culturally informed and aware neuroethics is required to “ensure that practice and products of neuroscience have the most fruitful and beneficial impacts for a global society” (Rommelfanger 2019). As Rommelfanger et al. (2018) pointed out, although the neuroethics questions proposed to the different brain initiatives should be universally useful, they must also be shaped by local cultural values and frameworks. 

Regarding ND, there is a variety of cultural and historical contexts in which data privacy may be characterized differently, depending on how public and individual interests are valued. For instance, in Japan and China privacy was historically viewed negatively and community values where prioritized. Both countries have witnessed a relatively recent shift towards individual freedoms (Miyashita 2011, 2016; Farrall 2008). In contrast, Chile is currently undergoing a major change in the opposite direction. Since October 2019, the country has suffered a legitimacy crisis that triggered regular and ongoing massive protests. This process resulted in a national plebiscite (scheduled to be held in on April 2020) for creating a completely new constitution, which plausibly will determine a shift from the individualism of an extreme laissez-faire underwritten by the 1980 constitution towards a more communitarian view focused on decreasing inequality. It seems that the outlined distributed cognition view on ND (which emphasizes the interests of the whole health-community) would be a better match for this cultural and political climate than the individual-centered organic view. If the brain initiatives will contribute to the elaboration of the Chilean bill for ND, they should not ignore such emerging values. 

  1. Adams, A., Albin, S., Amunts, K., Asakawa, T., Bernard, A., Bjaalie, J. G., Chakli, K., Deshler, J. O., De Koninck, Y., Ebell, C. J., Egan, G., Hale, M. E., Häusser, M., Jeong, S.-J., Illes, J., Lanyon, L., Li, Y., Magistretti, P., McMahon, A., Montojo, C., Ohtsuka, T., Okabe, S., Okano, H., Pei, G., Pouget, A., Reindorp, J., Richards, L. J., Rommelfanger, K. S., Sajda, P., Scobie, K. N., Suh, P.-G., Tanaka, K., Thiels, E., Valdes-Sosa, P. A., Welchman, A. E., White, S., Wilson, G., Yuste, R., Zhang, X. & Zheng, J. (2020). International Brain Initiative: An Innovative Framework for Coordinated Global Brain Research Efforts. Neuron, 105(2), 212-216.
  2. Borgatti, S. P. (2005). Centrality and network flow. Social networks, 27(1), 55-71. 
  3. Brownsword, R. (2016). Property in human tissue: triangulating the issue, in Sýkora, P., & Wiesing, U. (eds.) Altruism reconsidered: exploring new approaches to property in human tissue. Routledge, 93-104. 
  4. Farrall, K.N. (2008). Global privacy in flux: illuminating privacy across cultures in China and the U.S. Int. J. Commun. 2, 993–1030. 
  5. Fornito, A., Zalesky, A., & Bullmore, E. (2016). Fundamentals of brain network analysis. Academic Press. 
  6. Frasier, M. (2016). Perspective: data sharing for discovery. Nature, 538(7626), S4. 
  7. Friston, K. J. (2011). Functional and effective connectivity: a review. Brain connectivity, 1(1), 13-36. 
  8. Glannon, W. (2017). The evolution of neuroethics. In Racine, E., & Aspler, J. (eds.) Debates About Neuroethics, 19-44. Springer, Cham. 
  9. Goering, S., & Yuste, R. (2016). On the necessity of ethical guidelines for novel neurotechnologies. Cell, 167(4), 882-885. 
  10. Harris, J. W. (1996). Who owns my body. Oxford Journal of Legal Studies, 16(1), 55-84. 
  11. Karwowski, W., Vasheghani Farahani, F., & Lighthall, N. (2019). Application of graph theory for identifying connectivity patterns in human brain networks: a systematic review. frontiers in Neuroscience, 13, 585. 
  12. Lamond, G. /2006). Precedent and Analogy in Legal Reasoning, in Edward N. Zalta (ed.) The Stanford Encyclopedia of Philosophy, , URL = <>. 
  13. Liao, X., Vasilakos, A. V., & He, Y. (2017). Small-world human brain networks: perspectives and challenges. Neuroscience & Biobehavioral Reviews, 77, 286-300.   
  14. Locke, J. (1689). Two Treatises of Government, in P. Laslett (ed.), Locke’s Two Treatises of Government, Cambridge: Cambridge University Press, 1960. 
  15. Maddox, N. (2016). Abandonment’and the acquisition of property rights in separated human biomaterials. Medical Law International, 16(3-4), 229-251. 
  16. Miyashita, H. (2011). The evolving concept of data privacy in Japanese law. International Data Privacy Law 1, 229–238. 
  17. Miyashita, H. (2016). A tale of two privacies: enforcing privacy with hard power and soft power in Japan. In Enforcing Privacy Law, Governance and Technology Series, D. Wright and P. De Hert, eds. (Springer), pp. 105–122. 
  18. Montgomery, J. (2017). Data sharing and the idea of ownership. The New Bioethics, 23(1), 81-86. 
  19. Nozick, R. (1974). Anarchy, state, and utopia. New York: Basic Books. 
  20. Nwabueze, R. N. (2016). Biotechnology and the challenge of property: property rights in dead bodies, body parts, and genetic information. Routledge. 
  21. Nwabueze, R. N. (2019). Regulation of bodily parts: understanding bodily parts as a duplex. International Journal of Law in Context, 1-21. 
  22. Rommelfanger, K. S., Jeong, S. J., Ema, A., Fukushi, T., Kasai, K., Ramos, K. M., Salles, A. & Singh, I. (2018). Neuroethics Questions to Guide Ethical Research in the International Brain Initiatives. Neuron, 100(1), 19. 
  23. Rommelfanger, K. S., Jeong, S. J., Montojo, C., & Zirlinger, M. (2019). Neuroethics: Think Global. Neuron, 101(3), 363-364. 
  24. Sporns, O., Chialvo, D.R., Kaiser, M., Hilgetag, C.C., (2004). Organization, development and function of complex brain networks. Trends Cogn. Sci. 8 (9), 418–425. 
  25. Telesford, Q. K., Simpson, S. L., Burdette, J. H., Hayasaka, S., & Laurienti, P. J. (2011). The brain as a complex system: using network science as a tool for understanding the brain. Brain connectivity, 1(4), 295-308. 
  26. Terry, S. F., & Terry, P. F. (2011). Power to the people: participant ownership of clinical trial data. Science Translational Medicine, 3(69).  
  27. Tymofiyeva, O., Hess, C. P., Ziv, E., Tian, N., Bonifacio, S. L., McQuillen, P. S., ... & Xu, D. (2012). Towards the “baby connectome”: mapping the structural connectivity of the newborn brain. PloS one, 7(2). 
  28. Vayena, E., & Blasimme, A. (2017). Biomedical big data: new models of control over access, use and governance. Journal of bioethical inquiry, 14(4), 501-513. 
  29. Yeh, M. (2012) Digital EEG, in Yamada, T., & Meng, E. (eds.), Practical guide for clinical neurophysiologic testing: EEG. Lippincott Williams & Wilkins, pp. 54-62.  
  30. Yuste, R., Goering, S., Bi, G., Carmena, J. M., Carter, A., Fins, J. J., ... & Kellmeyer, P. (2017). Four ethical priorities for neurotechnologies and AI. Nature News, 551(7679), 159.

Abel Wajnerman Paz is an Assistant Professor in the Department of Philosophy at the Alberto Hurtado University, Santiago de Chile. He obtained his PhD in Philosophy at the University of Buenos Aires (2015), he was a CONICET Postdoctoral Fellow (2015-2017) and a FONDECYT Postdoctoral Fellow (2018-2021). His main area of interest is the Philosophy of Cognitive Neuroscience. He focusses on epistemic and conceptual issues regarding neurocognitive processes (neural coding, computation and information processing) and their relation to mental capacities (specifically, perception, thought and consciousness).

Want to cite this post?

Paz, A. W. (2020). Are Neural Data Protected by Bodily Integrity? A Discussion of the ‘Organic’ View on Neural Data Rights. The Neuroethics Blog. Retrieved on , from


Follow Us

Follow Us
Emory Neuroethics on Facebook

Emory Neuroethics on Twitter

AJOB Neuroscience on Facebook