Patient-Centered Neurointerventions

Deep Brain Stimulation (DBS) is being developed to treat Parkinson’s Disease (PD), as well as Treatment-Resistant Depression (TRD), and Obsessive-Compulsive Disorder (OCD). Many DBS patients experience significant relief from debilitating symptoms. However, unwanted side effects may compromise a patient’s quality of life in ways that undermine their autonomy, authenticity and agency.

1. Participatory machine learning & social justice in the development of neurological interventions

“Participatory” machine learning approaches have been proposed for engaging patients as active contributors to the design and development of algorithms used to deliver medical interventions, with the aim of addressing issues of bias in machine learning that have been shown to exacerbate and proliferate racial disparities. In my poster presentation for the 2021 Annual International Neuroethics Society Meeting I discuss how these issues pertain to the design of machine learning algorithms for the delivery of adaptive neurostimulation. I draw inspiration from Dorothy Robert’s critiques of “race-based” medicine in genetics that demonstrate how personalization in healthcare can obscure or pull focus away from structural inequalities as the cause of racial health disparities.

As the director of Patient Engagement in the MGH Brain Modulation Lab I am working with Dr. Mark Richardson to develop an approach to patient participation for the development of neurostimulation interventions that prioritizes:

The development of long-term partnerships that recognize participation as labor, and compensating individuals and communities for their contributions.
Ongoing efforts to develop communication tools for sharing “technical” knowledge in order to elicit meaningful input from patients.
The integration of neuroscience research with concerns of population health, where understanding the impact of structural inequalities is essential to and inseparable from the successful generation of scientific knowledge.

Selected Publication

Walton, A. (November, 2021). Participatory machine learning and social justice in the personalization of neurological interventions. Poster presentation at the annual International Neuroethics Society Meeting. [click here for PDF] *Awarded Best Contribution in Philosophical Neuroethics.

2. Agency Assessment Tool (AAT)

Principal Investigator Adina Roskies, funded by the National Institute of Mental Health, is creating an Agency Assessment Tool (AAT) for measuring changes in agency that result from neurological interventions in order to support patients in making treatment decisions in line with their individual needs and values.

Neuroethics research conceptualizes agency as a multi-dimensional space where the effects of different diseases can result in changes across the different dimensions of agency. Interventions to treat disease, like Deep Brain Stimulation (DBS), will also impact the dimensions of a patient’s agency. Optimal stimulation parameters will vary depending on what dimensions of agency are most valued by a particular patient.¹

For example, an individual with Parkinson’s Disease (PD) may have DBS implanted to restore motor control, but experience changes in impulsivity and emotional control. Depending on the nature of these changes and how they impact aspects of agency that are important to them, they may communicate needed adjustments to stimulation settings in order to achieve a personalized balance across the dimensions of their agency.

For updates on this project see our poster presentation from the NIH BRAIN Meeting.

Selected Publications

Walton, A. (2021). The development of self-trust in DBS patients. American Journal of
Bioethics Neuroscience: 12(2), 194-196. [pdf]

Roskies, A. & Walton, A. (2020). Neuroethics in the shadow of a pandemic. American Journal of Bioethics Neuroscience. Insight article in Special Issue: BRAIN 2.0. [pdf]

¹ Roskies, A. (2015). Agency and intervention.

3. Machine Learning in Psychiatry

I collaborated with members of the Nock Lab at Harvard to co-author a paper that discusses the importance of increasing the transparency of machine learning models to ensure they can be applied ethically and fairly in clinical decision-making.

Selected Publications

Jacobson, N. C., Bentley, K. H., Walton, A., Wang, S. B., Fortgang, R. G., Millner, A. J., Coombs III, G., Rodmand, A. and Coppersmith, D. (2020). Ethical dilemmas posed by mobile health and machine learning in psychiatry research. Bulletin of the World Health Organization, 98(4), 270. [pdf]

PDF files linked to this site are intended for personal use only.