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Neuroimaging in Predicting and Detecting Neurodegenerative Diseases and Mental Disorders

By Anayelly Medina

This post was written as part of a class assignment from students who took a neuroethics course with Dr. Rommelfanger in Paris of Summer 2016.

Anayelly is a Senior at Emory University majoring in Neuroscience and Behavioral Biology. 

If your doctor told you they could determine whether or not you would develop a neurodegenerative disease or mental disorder in the future through a brain scan, would you undergo the process? Detecting the predisposition to or possible development of disorders or diseases not only in adults but also in fetuses through genetic testing (i.e. preimplantation genetics) has been a topic of continued discussion and debate [2]. Furthermore, questions regarding the ethical implications of predictive genetic testing have been addressed by many over the past years [4,8]. However, more recently, neuroimaging and its possible use in detecting predispositions to neurodegenerative diseases as well as mental disorders has come to light. The ethical questions raised by the use of predictive neuroimaging technologies are similar to those posed by predictive genetic testing; nevertheless, given that the brain is the main structure analyzed and affected by these neurodegenerative and mental disorders, different questions (from those posed by predictive genetic testing) have also surfaced.

Computerized Axial Tomography (CAT), Positron Emission Tomography (PET) and radioactive tracers, Magnetic Resonance Imaging (MRI), and Functional Magnetic Resonance Imaging (fMRI) are all current neuroimaging technologies used in the field of neuroscience. While each of these technologies function differently, they ultimately all provide information on brain functioning or structure. Furthermore, these neuroscientific instruments have, in recent years, been used to explore the brain in order to determine predictive markers for neurodegenerative diseases and mental disorders, such as Parkinson’s disease, Schizophrenia, Huntington’s disease, and Alzheimer’s disease [1,9,11,12]. For example, Stoessl [11] explains how PET scans and radiotracers have shown evidence of abnormal dopamine dysfunction (a pathway known to be compromised in PD) in asymptomatic individuals from families with known inherited PD (although whether this dysfunction is an early measure of those who will develop PD or if it is associated with the inherited PD gene is unclear). In addition, Callicott et al. [1] provided evidence, through fMRI scans, that showed greater response in the right dorsolateral prefrontal cortex in cognitively intact siblings of patients with schizophrenia (this abnormal response was similar to that in patients with schizophrenia). Furthermore, Paulsen et al [9] used fMRI scans to show that striatal and white matter volumes in the brain could be used to predict diagnosis proximity (estimated years to diagnosis) of Huntington’s disease. Finally, the use of neuroimaging techniques to establish predictive markers of disease and mental disorders has been clearly seen in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) that was started in 2007 and is currently active [12]. The potential ability to predict whether or not an individual will develop a neurodegenerative disease or mental disorders seems like an initiative without faults. However, there are questions surrounding the ethics of such an ability that must be addressed.

Image courtesy of Flikr

The use of neuroimaging data to predict neurodegenerative diseases and mental disorders is an initiative that should continue to be pursued as it could help in the prevention or delay the disease or disorder by early intervention, but that research should also take into account the ethical implications of conducting and providing such information for and to the public. Some of the main ethical issues that have developed with the increased use of predictive neuroimaging include concerns surrounding intervention, privacy, and access. In terms of intervention, the main concern involves determining when to notify the patient—this would require having an established degree of probability, as well as prevalence of false positives, that would count as being sufficient to warrant patient knowledge of the neurodegenerative disease or mental disorder [3]. This would be further complicated when assessing brains of younger individuals given that their brains are still undergoing developmental changes, and the reliability of prognoses at such an early age has yet to be assessed. In addition to timing and accuracy, other issues involve the use of neuroimaging to predict diseases or mental disorders that have no cures or treatment as well as taking into account the impact that providing said information could have on the patient (such as the burden of knowledge [3] or the impact of stigma). Furthermore, the diseases and mental disorders that are being predicted using neuroimaging all affect the brain and its function, and thus possibly “also affect mental competence, mood, personality, and sense of self” [10]. In addition to intervention, privacy and discrimination are other issues at play as employers or insurers could determine whether or not a person is hired or what type of healthcare policy an individual receives based on predisposition/predictive neuroimaging tests [7]. Finally, the ethical concerns surrounding the access to neuroimaging technology must also be addressed. Neuroimaging scans are typically expensive, and its use in predicting the development of diseases and disorders may lead to more healthcare disparities; this could become a greater problem if the technology were to become commercialized and only accessible to those who are privileged [6;7].

In order to address these ethical issues, changes must be implemented at various stages of this predictive neuroimaging technology’s implementation. In addressing the issue of timing and accuracy in intervention, more studies looking at the correlation between various brain structures or functions and predisposition/prediction of neurodegenerative diseases and mental disorders must be conducted; specifically, studies in which the complementary approach of testing for genetic predisposition is controlled for would provide more conclusive and valid data supporting the associations made between brain scan findings and prediction of disease or mental disorder. Furthermore, more initiatives, like that of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), should be created for other neurodegenerative diseases and mental disorders (with possible funding from the NIH, through the Functional Magnetic Resonance Imaging Core Facility (FMRIF)). Organizations like the ADNI hope to create a network of shared data, concerning biomarkers in the brain, in order to facilitate early detection of disease [12]. Moreover, initiatives like this could further facilitate the development and establishment of methods and protocols for predicting the onset of disease. In addition, regulation of the neuroimaging devices in predictive neuroimaging testing must also be implemented; as explained by Greely [5] the Food and Drug Administration (FDA) typically has jurisdiction over the use of drugs, biologicals, and medical devices, but if a test involves using a device (in this case a neuroimaging device) that has already been approved in the past, the FDA would not need to approve its use in a new test. Thus, an appeal to the FDA in order to reevaluate this decision should be pursued in order to establish safe and effective use of the technology. Furthermore, in order to protect the privacy of the patient, as well as protect against discrimination or unfair actions against the patient through the use of predictive neuroimaging data by insurers or employers (in terms of predictive data of neurocognitive disease or mental disorder development) protocols and regulations should be established by the U.S. Department of Human Health Services (HHS) and the U.S. Equal Employment Opportunity Commission. Finally, involvement of the U.S. DHHS in making this predictive neuroimaging accessible to those who cannot afford these services should also be established.


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&Weinberger, D. R. 2003. Abnormal fMRI Response of the Dorsolateral Prefrontal Cortex in Cognitively Intact Siblings of Patients With Schizophrenia. American Journal of Psychiatry AJP 160(4): 709-719. doi:10.1176/appi.ajp.160.4.709.

 2. Farrimond, H. R., & Kelly, S. E. 2011. Public Viewpoints on New Non-invasive Prenatal Genetic Tests. Public Understanding of Science, 22(6): 730-744. doi:10.1177/0963662511424359

 3. Fuchs, T. 2006. Ethical Issues in Neuroscience. Current Opinion in Psychiatry 19(6): 600-607. doi:10.1097/01.yco.0000245752.75879.26.

 4. Fulda, K. G. 2006. Ethical Issues in Predictive Genetic Testing: A Public Health Perspective. Journal of Medical Ethics 32(3): 143-147. doi:10.1136/jme.2004.010272.

 5. Greely, H. 2004. Markula Center for Applied Ethics. The Neuroscience Revolution, Ethics, and the Law. Available here. (accessed June 19, 2016).

 6. Illes, J., & Racine, E. 2005. Imaging or Imagining? A Neuroethics Challenge Informed by Genetics. The American Journal of Bioethics 5(2): 5-18. doi:10.1080/15265160590923358 .

 7. Illes, J., Rosen, A., Greicius, M., & Racine, E., 2012. Ethics Analysis of Neuroimaging in Alzheimer’s Disease. Annals of the New York Academy of Sciences 1097: 278-295. doi:10.1196/annals.1379.030.

 8. Leah, DH., Williams J., Donahue MP., 2005. Ethical Issues in Genetic Testing. Journal of Midwifery & Women’s Health 50(3): 234-240. doi:10.1016/j.jmwh.2004.12.016.

 9. Paulsen, J. S., Nopoulos, P. C., Aylward, E., Ross, C. A., Johnson, H., Magnotta, V. A., . . . Nance, M. 2010. Striatal and White Matter Predictors of Estimated Diagnosis for Huntington Disease. Brain Research Bulletin 82(3-4): 201-207. doi:10.1016/j.brainresbull.2010.04.003.

 10. Roskies, A. 2016. Neuroethics. The Stanford Encyclopedia of Philosophy. Available here. (accessed June 19, 2016).

 11. Stoessl, A. J. 2012. Neuroimaging in the Early Diagnosis of Neurodegenerative Disease. Translational Neurodegeneration 1(1), 5. doi:10.1186/2047-9158-1-5.

 12. Weiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J., Cedarbaum, J., . . . Trojanowski, J. Q. 2015. 2014 Update of the Alzheimer’s Disease Neuroimaging Initiative: A Review of Papers Published Since its Inception. Alzheimer’s & Dementia 11(6). doi:10.1016/j.jalz.2014.11.001.

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Medina, A. (2016). Neuroimaging in Predicting and Detecting Neurodegenerative Diseases and Mental Disorders. The Neuroethics Blog. Retrieved on , from


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