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. Author manuscript; available in PMC: 2026 Sep 4.
Published in final edited form as: Am J Psychiatry. 2026 Jul 8;183(10):743–753. doi: 10.1176/appi.ajp.20251187

Implantable Neuromodulation Devices in Psychiatry: Human Fidelity and Implementation Challenges

Gabriel Lázaro-Muñoz 1,2,3,*, Amanda R Merner 1,3
PMCID: PMC13541265  NIHMSID: NIHMS2196750  PMID: 42415253

Introduction

Major investments in neurotechnology, materials science, computing capacity, and our increasing understanding of the brain are leading to rapid advances in the development of brain-based implantable neuromodulation devices (INDs). By 2045, the total addressable market for deep brain stimulation (DBS),1 brain-computer interfaces (BCI), and other INDs is expected to be over $400 billion in the U.S. alone.2 Brain-based INDs are intended to help patients with “treatment-resistant” (“drug-resistant”, “refractory”) presentations of movement disorders, seizure disorders, other neurological conditions, psychiatric disorders, and patients with loss of function involving paralysis, blindness, or loss of limbs.3–12 In this article, we discuss some of the most pressing challenges in the responsible development and translation of brain-based INDs for psychiatric conditions.

In the next section, we describe the concept of human fidelity and how we believe it can help better align devices (e.g., brain-based INDs), device access workflows (device- and patient-related processes necessary to get access to an IND in research and clinical contexts), and device-related messaging (the content and communication approach used to reach stakeholders) to be more responsive to the needs (e.g., medical, informational) and expectations of patients, families, providers, and other relevant stakeholders to promote responsible adoption of devices into practice. We define stakeholder as, “an individual or group who is responsible for or affected by health- and healthcare-related decisions that can be informed by research evidence.”13 After we define human fidelity, we discuss challenges that represent distinct domains in which misalignment between the device, its workflow or its messaging, and the needs and expectations of key stakeholders, challenges responsible and effective translation of INDs: Access and Uptake; Invasiveness; Implications of the Brain as Control Center for Cognition and Behavior; Patient Centered Outcomes; and Post-Trial IND Maintenance for Patients with Treatment-Resistant Conditions who Benefit.

For the purposes of this article, we define brain-based INDs, as neuromodulation devices implanted in or near the brain with the goal of recording activity from and/or stimulating brain targets. The focus on brain-based INDs aims to narrow the scope of the article to address, in more detail, pressing issues in the development and translation of brain-based INDs. If approved by the U.S. Food and Drug Administration (FDA), brain-based INDs (e.g., DBS for depression) would be a novel kind of intervention in psychiatric practice. INDs that stimulate the peripheral nervous system such as the vagus nerve stimulator (VNS) Therapy System have had FDA premarket approval for the management of treatment-resistant depression since 2005.14 Because brain-based INDs involve a neurosurgical intervention and the device is generally expected to remain implanted for the duration of the patient’s life, these devices will likely have a more significant risk profile compared to devices that target the peripheral nervous system or non-invasive neuromodulation devices already approved by the FDA for the management of some psychiatric conditions (e.g., transcranial magnetic stimulation; TMS, transcranial direct current stimulation; tDCS).15–17 There are ongoing pivotal trials for INDs such as DBS for depression and multiple earlier phase studies examining INDs for conditions like obsessive compulsive disorder (OCD), Tourette’s syndrome (TS), among other conditions.18–26 While the focus of this article is on brain-based INDs, many of the challenges addressed will apply to other neuromodulation devices in psychiatry.

Human Fidelity and Brain-based Implantable Neuromodulation Devices

The concept of human fidelity draws from a convergent tradition across several design and engineering disciplines that have grappled with a shared problem: the persistent gap between what a system is designed to do and what the humans interacting with it actually need, expect, and experience. Human-centered design established the foundational principle that solutions to complex human challenges must be developed iteratively, with ongoing and structured input from the people who will use them—not as a courtesy, but as a requirement of good design and producing systems that work.27,28 Human-computer interaction extended human-centered design into the context of interactive and increasingly autonomous systems, showing that misalignment between system design and human cognition, behavior, and context produces dissatisfaction and consequential errors.28,29 Human factors engineering, codified in medical development through FDA guidance and international standards such as IEC 62366–1, operationalized these insights into regulatory requirements: manufacturers of medical devices must systematically analyze, specify, develop, and evaluate how real users interact with their devices across the full range of intended use environments—treating human-system alignment not as a design aspiration but as a safety-critical performance dimension.30,31 Patient-centered outcomes research, institutionalized through PCORI (Patient-Centered Outcomes Research Institute), applied a parallel logic to clinical research: that the relevance, uptake, and impact of research is fundamentally constrained when the perspectives of patients, caregivers, and other key stakeholders are not embedded in the design of the research itself.13,32

Human Fidelity extends and synthesizes these traditions to the context of implantable neuromodulation devices in medicine. Human Fidelity in device development and translation considers the full context of influential forces and stakeholders in which the devices are being developed (see Figure 1). For example, it considers the legal and regulatory parameters in which the device is being developed and is intended to be used, relevant research and medical ethics considerations, current practices and policies related to how similar devices or conditions are managed, the device’s capacities, limitations, and intended uses, and stakeholder perspectives regarding the device, medical and informational needs, and expectations. Human Fidelity centers the role and perspectives of stakeholders throughout the design, development, and clinical translation process.

Figure 1.

Figure 1.

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The Development and Translation Ecosystem. Human Fidelity requires that we account for stakeholder needs and expectations with the goal of promoting responsible adoption and optimized uptake for eligible patients who can benefit from a technology.

Relevant stakeholders for human fidelity in this context include the patients and care partners, but also the perspectives and practices of the clinicians involved in making these devices available or identifying eligible patients who could benefit from the IND. Relevant stakeholders or parties also include the healthcare systems in which these INDs are offered and their policies and practices relevant to device access, along with the broader ecosystem of stakeholders including professional organizations, patient advocacy groups, the public and society, regulators, industry, and researchers (Figure 1). By considering the surrounding context, and empirically examining and integrating the perspectives of stakeholders on the device, device- and patient-related workflow to get access to the device, and device-related messaging at different time points during the development and translation of the device, developers and others can modify the device, it’s workflows, and messaging to better align these with the medical and informational needs and expectations of stakeholders to promote responsible adoption and uptake.

We define human fidelity as the measurable degree to which a device, its access workflows, and associated messaging are aligned with the empirically determined needs and expectations of key stakeholders across the full development and clinical translation pipeline. Treated as a performance variable analogous to reliability or scalability in systems engineering, Human Fidelity is not a static property of a device but a dynamic outcome of an iterative, evidence-driven process: one that continuously examines stakeholder perspectives at different stages of development, identifies misalignments between the system and its human context, and modifies the device, workflow, or messaging accordingly. Figure 1 summarizes the key stakeholders involved in the research and clinical translation of brain-based INDs in psychiatry and surrounding considerations.

There are four workflows that we consider particularly important to examine in the design, development, and clinical translation of brain-based INDs: 1) device research workflow or the process and stakeholders that the device needs to go through to become available in a clinical trial, 2) Patient-participant research/clinical trial workflow or the process and stakeholders, patients must navigate and interact with to get access to a device in a clinical trial, 3) Device clinical workflow or the process and stakeholders (e.g., physicians, healtcare systems, payers, regulators) that the device needs to go through to be available to a patient in a clinical setting, and 4) Patient clinical workflow or the process that a patient needs to go through to get access to an FDA-approved DBS or IND in clinical care. The focus of our examination is on challenges related to the clinical workflows necessary to make brain-based INDs clinically available, but we also address the importance of patient-centered outcomes research in brain-based IND research, which we see as a patient research workflow issue, and post-trial issues in brain-based IND research which we see as a device research workflow issue.

Implantable Neuromodulation Devices in Psychiatry

INDs in psychiatry bring hope for the estimated 20–60% of patients with depression, OCD, schizophrenia, TS, and other psychiatric disorders that do not respond to FDA-approved pharmacological or electroceutical treatments, or psychotherapy; this represents over 10 million patients with treatment-resistant psychiatric conditions in the US.18,33 Although brain-based INDs like DBS are not currently FDA-approved for psychiatric indications, there is a growing body of knowledge that supports the safety and effectiveness of INDs, like DBS, for different psychiatric disorders (e.g., depression, OCD, schizophrenia, TS).18–26 Importantly, because of the need for neurosurgery and the risks associated with keeping the device implanted long term, research on DBS for psychiatric conditions, at this stage, is only available for individuals with treatment-resistant presentations of these disorders.

DBS is the most widely used brain-based IND, with more than 250,000 patients having undergone DBS surgery, most of them to manage symptoms of treatment-resistant movement disorders. DBS is an FDA-approved IND that can effectively manage medication-resistant symptoms of Parkinson’s disease (PD), essential tremor (ET), and dystonia, some of the most common movement disorders.34–46 DBS and the Responsive Neurostimulation System (RNS) have also received pre-market approval for the treatment of refractory epilepsy.8,47,48 The FDA has not granted pre-market approval for the use of DBS for any psychiatric condition. However, the FDA has approved a humanitarian device exemption (HDE) for the use of DBS for refractory OCD. Importantly, HDEs do not require the same level of evidence of safety and effectiveness as required for pre-market approval. HDEs are intended for rare conditions or patient subpopulations (e.g., treatment-resistant OCD) and are exempt from the effectiveness requirements of the pre-market approval process, although it requires a demonstration of “probable benefit.”49 Beyond the FDA-approved uses of DBS for PD, ET, dystonia, and epilepsy, and the HDE for OCD, there are ongoing pivotal trials of DBS for depression and Tourette’s syndrome, and multiple smaller studies of DBS for depression, OCD, post-traumatic stress disorder (PTSD), substance use disorders, schizophrenia, and other disorders.50–54

Access and Uptake

A medical system’s capacity to allow patients to navigate their way to being offered an IND, when clinically indicated, is an understudied and underappreciated element of access to INDs. Barriers in access relate to the processes, groups, and factors involved in whether an eligible patient is offered DBS, and barriers to uptake relate to the processes, groups, and factors involved in whether a person accepts or declines DBS. Access and uptake are key components of the process necessary to make FDA-approved INDs available in clinical care.

DBS Underutilization

While DBS has been FDA-approved for PD and ET since 2003, and over 250,000 devices have been implanted, the vast majority of PD and ET patients eligible for DBS are not getting this IND, even if there is nothing else that can manage their severe neurological condition. For example, about 10%−15% of patients with PD and up to 50% with ET have medication-resistant presentations, but the use of DBS for movement disorders has remained low for the past 20 years, at around 1% for PD patients and 2.2% for ET patients per year.3–6,11,55–57 A similar situation is observed with brain-based INDs for epilepsy, approximately 30% of patients with seizure disorders have drug-resistant epilepsy (~1 million patients in the US), but only 2% of patients with seizure disorders received DBS or responsive neurostimulation (RNS) in 2019.8,58 Over this time there has been an increase in the prevalence of PD and ET due to an aging population, and there are increasing numbers of trained functional neurosurgeons; however, the number of DBS surgeries for movement disorders and epilepsy has remained consistently low and DBS underutilization has been a recurring concern among researchers and clinicians.5,6,35,37,38,55,59,60 Each of these underutilization patterns reflects, in part, a failure of human fidelity: a misalignment between the device and its surrounding workflow and the informational needs, referral practices, and attitudes of the stakeholders responsible for identifying and offering eligible patients access to DBS.

The underutilization of FDA-approved DBS for movement disorders and epilepsy suggests that INDs such as DBS for psychiatric conditions could face similar challenges. There are multiple factors that can contribute to this underutilization, among them, challenges related to the clinical workflows (e.g., patient journey from symptom onset to access to IND, to issues related to device workflows such as a healthcare system having the infrastructure and personnel necessary to offer and implant INDs) necessary to match a medically eligible patient to an FDA-approved IND. 5,6,35,37,38,55,59,60

Evidence suggests that DBS underutilization among patients with approved indications is in many ways related to the stakeholders involved in the patient’s care journey (“patient clinical workflow”; e.g., patients, care partners, family members, general practitioners, other clinicians, device programmers, general neurologists, neurologists specialized in movement disorders, functional neurosurgeons who implant the devices).5,6,35,37,38,55,59,60 For example, patient concern about the perceived invasiveness and risks of the device is a factor that can limit a patient’s willingness to pursue DBS and consent to it.61–63 Other evidence suggests provider-level challenges can act as barriers to DBS access that help explain underutilization, including: neurologists’ training (e.g., general vs movement disorders specialist) impacting the likelihood of considering referring for DBS evaluation, burdensome referral practices for DBS, and perceptions of patient social support as candidacy considerations for DBS.55,61,62,64–67 Furthermore, there is evidence that DBS utilization is disproportionately lower in patients with certain demographic characteristics such as being female, black, Hispanic, having public insurance, or living in a rural environment.58–60,68 A development and translational approach that places a premium on human fidelity, would empirically examine the attitudes, views, and perspectives on potential solutions of the stakeholders involved in a patient navigating the healthcare system from symptom onset to a brain-based IND (see Figure 2), and would seek to minimize the barriers identified by integrating stakeholder feedback.

Figure 2.

Figure 2.

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Likely Patient Clinical Workflow from Symptom Onset to IND or DBS (Assuming FDA Approval)

People’s attitudes and concerns about these devices can influence DBS uptake. A recent study examined the perceptions of a U.S. nationally representative sample of individuals (N=1052) based on sex, race, and age, on the use of DBS for mood, memory, and motor symptoms.63 Individuals felt DBS was riskier, more invasive, and less likely to be used than other technologies (i.e., MRgFUS, TMS, pills), even if they expected DBS to be more beneficial than MRgFUS or TMS. Further, their views on acceptability of use for themselves and for others was inversely related to their perceptions of risk, invasiveness, and the potential for the technology to “change who they are as a person.” Results suggest that the public is largely unfamiliar with DBS and views DBS as risky, invasive, and potentially able to alter who they are, and these views are associated with a decreased willingness to use DBS. One way to potentially decrease perceptions of risk and invasiveness and increase willingness to use DBS, may be to increase familiarity and knowledge surrounding the benefits and safety profile of DBS, and this is something that can be empirically examined. This is the kind of evidence that a Human Fidelity approach is designed to generate and act on: empirical data about misalignment between stakeholder perceptions and the safety evidence about the device, translated to modifications to messaging and access workflow to better align with potential endusers informational needs.

What Constitutes a Treatment-Resistant Psychiatric Condition

The way “treatment-resistance” is defined in trials and FDA applications for premarket approval, will be important in determining which patients get access and insurance coverage for INDs in clinical care.33 Currently, IND studies often use different definitions of treatment-resistance even for the same disorders. As the field of INDs in psychiatry progresses, it will be important to continue examining which patients can safely benefit from INDs and whether it would be helpful to have a uniform set of criteria regarding elements like severity, duration of disorder, number and type of failed treatments to make determinations about treatment-resistance and eligibility for INDs.

Medical systems should, ideally, not rely on patients with treatment-resistant conditions or their family members to 1) identify when a patient’s condition could be considered treatment-resistant, and 2) pursue clinical trials or novel therapeutics. Rather, clinicians are in a better position to identify patients with a treatment-resistant condition. Professional guidelines defining treatment-resistance for each condition and strategies to help these patients consider clinical trials or novel therapeutics could be helpful. However, clinicians are less likely to follow guidelines integrating technology if they do not perceive the enough clinical utility in the technology.69 Thus, if brain-based INDs are FDA-approved, working with professional mental health clinician organizations and patient advocacy groups to increase awareness of the safety and effectiveness (utility) of these tools will be key to addressing patient and clinician informational needs.

Finally, defining the “treatment-resistance” necessary to be eligible for brain-based INDs raises important questions: Are patients able to provide adequate informed consent to participate in a trial or, if eventually FDA approved for psychiatric disorders, consent to DBS in the clinical setting, when they have tried so many treatments (average of 20 treatments attempted before a trial of DBS for depression)23 and are likely “desperate” (as some describe these patients)70 to find a treatment that works? We have argued before that “treatment-search fatigue”71 may influence informed consent, but that the capacity to provide consent needs to be determined on an individual basis. It is important to consider that while patients with treatment-resistant psychiatric conditions have likely experienced significant difficulty in their search for a treatment, this experience and the experience with the condition, also allows them to inform the risk-benefit calculus when considering INDs.

Invasiveness

When people hear about anything that impacts brain tissue, it is usually in the context of stroke, traumatic brain injury (TBI), or neurodegenerative diseases such as dementias, or Alzheimer’s and Parkinson’s disease. In addition, there is a widely held belief that the brain is extremely limited in its ability to recover from injury.72 While research on neurorecovery73,74 and the use of technologies such as DBS for stroke75 and TBI76 may slowly change these notions, implantable devices that pierce through brain tissue are still seen as invasive and risky.63 In the recent survey on public perceptions of neurotechnologies described earlier, DBS was viewed as the most invasive, risky, and least likely to be used despite high ratings of perceived benefit.63 While these findings highlight negative public perceptions on DBS, they also offer a path forward for addressing these concerns. These findings underscore the importance of helping clinicians and patients understand the benefits of DBS and that, based on current knowledge, the insertion of DBS electrodes for recording and stimulation can generally be done safely and without undue harm to the brain that could negatively impact cognitive functions or the quality of life of the patient.1,25

A key issue that will need further examination is how the invasiveness of different neurotechnologies is presented or marketed to the public, patients, and clinicians. For example, experimental devices like Motif’s DOT being studied for the management of treatment-resistant depression, is intended to be placed in the skull above the dura, without piercing or damaging brain tissue to provide transmagnetic cortical stimulation and is described as “minimally invasive”.77 On the other hand, MRgFUS, specifically high-intensity FUS (HIFU), which involves an “incisionless” approach that employs focused ultrasound to ablate (lesion) a specific brain target is also described as “minimally invasive”.78 HIFU is FDA-approved for treatment-resistant symptoms of movement disorders,79 and there is ongoing research on low intensity focused ultrasound as a neuromodulation tool for depression.80 Finally, Synchron’s Stentrode BCI which is implanted via the jugular vein and positioned through blood vessels in the brain, the system can record and stimulate brain targets from these blood vessels, is also referred to as “minimally invasive.”81 At a minimum, referring to these three markedly different technologies as “minimally invasive” could be confusing for patients and providers. It will be important to develop clear, uniform, and conceptually-sound terminology to describe the placement, benefits, and risks of different devices, to help patients, families, and clinicians compare interventions. A Human Fidelity approach that systematically examines how patients, physicians, and researchers conceptualize and compare the invasiveness of different devices, would provide the empirical basis for developing terminology that is not only scientifically accurate but genuinely responsive to how these distinctions are understood and weighed by the people making treatment decisions.

Implications of Brain as Control Center for Cognition and Behavior

The brain’s role in controlling or regulating emotions, behaviors, memory, motor outputs, and other functions associated with an individual’s identity gives neuromodulation the opportunity to regulate these functions to help manage a patient’s difficult-to-treat psychiatric condition. However, it also creates apprehensions surrounding potential side effects that could lead to behavioral changes such as increased impulsivity, hypomania, or other behaviors that can raise concerns about whether the agency of patients who receive neuromodulation could be unduly compromised.82–87 The gap between what the evidence shows about behavioral effects and what patients, families, and other stakeholders believe about brain-based INDs, is itself a Human Fidelity failure, one that can be addressed through targeted empirical research and more transparent, stakeholder informed communication of findings.

Concerns about the possibility that neuromodulation could significantly alter a patient’s personality, identity, agency, autonomy, or feelings of authenticity are both overstated by some and dismissed without sufficient consideration by others. Most importantly, concerns about the behavioral impacts of neuromodulation remain understudied, with only a handful of studies to-date that have prospectively explored changes in behavior and personal traits following DBS. 82,84,87–89 Although the existing literature largely signals changes that are perceived positively by patients, more research is still needed.84,87,90 To move the field of neuromodulation forward and help promote alignment between device messaging and public concerns, it will be helpful to examine stakeholder (e.g., DBS eligible patients who decline the device) concerns about these issues in more detail and to publish more data on the behavioral effects of these technologies and not just the impact of the device on decreasing symptoms of a treatment-resistant condition.

Patient-Centered Outcomes Research

Consistent with a device development and translation approach that prioritizes human fidelity, research on patient perspectives—including clinical trial participants—is critical at every point in the pipeline of development. Patient-centered outcomes research is particularly important in brain-based IND research and trials in which the samples for pivotal trials are relatively small (100–200 patients) compared to drug trials (1,000–2,000 patients or more). Given the expected relatively low number of opportunities to learn from patients in brain-based IND research, device developers prioritizing human fidelity, should maximize the opportunity to learn from participants’ experiences before, during, and after the trials to help address misalignments with patients’ and other stakeholders’ needs and expectations.

During the initial stages of device development, research with patients can help identify device features that are responsive to patients’ needs and what they perceive as beneficial for their condition and quality of life. Identifying concerns that patients or other stakeholders (i.e., caregivers, physicians) have about the device is also critical because it can allow device developers to modify devices in early design stages to better meet the needs and expectations of the patient population that will ultimately use the device. Even exploring the perceptions of the general population, regarding neural device design and intended uses can help test and understand potential concerns and address device features that will promote uptake of these technologies.63 The general population are also potential end users of neural devices for psychiatric disorders, so understanding their perspectives is another way of anticipating and addressing challenges before they become a barrier to access or uptake.

As a device moves to testing phases for FDA approval, gathering good data on clinical trial participants' experiences and evaluations of the risks and benefits of the device is key to ensuring the most comprehensive risk/benefit analysis of the device is conducted and submitted to the FDA. Importantly, patient experiences, patient centered outcomes data, and perspectives on the risk-benefit analysis of using the device can be considered by the FDA when evaluating the safety and effectiveness of the device.91 Patient perspectives are critical, as the patients who have experienced treatment-resistant conditions are generally in the best position to evaluate whether the risks of neuromodulation outweigh the potential benefits and the burden of the condition.71 Further, although standard outcome measures used in clinical trials and long-term studies to assess treatment efficacy and impacts on the patient are thorough, they are typically focused only on a narrow set of symptoms or outcomes, with research suggesting they are often incomplete and leave many impacts unassessed.87 Recent work has shed light on this, and systematic investigations into outcomes that are identified by both patients and care partners as meaningful to them in the context of treatment are growing in number.

Post-Trial IND Maintenance for Patients with Treatment-Resistant Conditions who Benefit

As stated earlier, there are no FDA-approved INDs for psychiatric conditions, but there is a significant amount of ongoing clinical research examining the safety and effectiveness of INDs for psychiatric conditions, thus, post-trial device maintenance is a key challenge in the development of psychiatric brain-based INDs.18–26 One topic has gathered increasing attention in research and media is how to maintain investigational brain-based INDs for patients with treatment-resistant health conditions or loss of function who benefit during a clinical trial.92–96 Importantly, while post-trial access and maintenance of drugs and devices is not a novel issue, we believe that there is a heightened responsibility to help ensure post-trial access when the devices help manage a severe and treatment-resistant condition.97,98

Researchers, participants, and other stakeholders believe that current post-trial management of devices is a concerning situation and one of the most significant challenges for brain-based IND development.97–108 For example, in a recent study, a brain-based IND researcher said: “[I]t seems to me not ethical to have a therapy that, say, a subset of patients really benefits from [and] then say, ‘Well, sorry. Now you canť have this. It was just for the study.”98 While a patient-participant with an experimental DBS expressed: “If you can afford it, then you should be able to pay maintenance, but if you can’t…It would be kind of cruel to give a guy this much life back and snatch it away just because of a few pennies, although it’s more than a few pennies.”98 Post-trial device maintenance is also a Human Fidelity issue: the failure to align the post-trial experience of patients with treatment-resistant conditions who have benefited from an experimental IND with any durable system of device maintenance support represents a misalignment with what patients/endusers and researchers medically need, their expectations, and the lack of device maintenanc offered at the end of the trials.98

Without access to the maintenance necessary to keep these devices functioning, the patient will re-experience the severe symptoms of the treatment-resistant condition and in some cases with greater severity and more debilitating than before the device was implanted.109,110 This makes brain-based INDs different from trials for most drugs and devices. Brain-based INDs are generally only used when the patient has not responded to available treatments. Thus, if the experimental brain-based IND works, that is the only known beneficial intervention for that patient and without maintenance, they have nowhere to turn for symptom relief from treatment-resistant depression, OCD, or any of the treatment-resistant psychiatric conditions under study.

In the US, and other countries, if the patient has health insurance, their insurance provider will often deny coverage for the maintenance of the device by considering it not “medically necessary” because the device is still experimental and has not been approved by the FDA.97,98 Even in countries with universal health care coverage, patients may also lose access to a functioning device because device manufacturers decide to change the design, making it no longer compatible with the iteration that is implanted in the individual. Further, device manufacturers may go out of business and abandon the development of the device or sell the intellectual property to a buyer that has no legal responsibility or interest in providing maintenance or care for these devices.95 Finally, in some cases, patients may simply not have access to the individuals (e.g., trained clinicians, researchers, device companies) that have the expertise to operate and maintain these devices.

The current practice in the field is to not offer a systematic plan for maintenance of these devices at the end of trials. Although researchers often try to help their patients maintain the devices, there is only so much they can do without a comprehensive and systematic approach to ensuring post-trial access for patients with treatment-resistant conditions that benefit from brain-based INDs.98,108 The lack of legal, regulatory, or practice guard rails to ensure continued access and maintenance means that many of these research participants will experience what we refer to as “deprivation harms.”111 That is, as a result of being deprived access to a functioning device, participants will re-experience a return to the severe treatment-resistant symptoms or loss of function that they experienced for years. As the field of neural device continues to develop, these deprivation harms could be the subject of tort or other lawsuits.

Alternatives to help improve access to the maintenance of these devices range from options such as self-regulation (e.g., device developer and other stakeholders developing ways to minimize this risk), insurance pools, co-payment sliding scales, recommendations for institutional review boards, and formal legislation.112,97,99,113,108 Insurance pools would require device developers to pay into the pool for every participant that joins a neural device trial for treatment-resistant indications. If the patient benefits from the device, the patient would then be eligible to petition this fund for assistance in covering the cost of maintenance. Legislation could, for example, help ensure that insurance providers would be responsible for covering the maintenance of the investigational device if the research team and patient can show evidence that the patient with an otherwise treatment-resistant, has benefited from the investigational device or a device for an investigational indication. Thus, although not approved by the FDA, the investigational device may be medically necessary for that participant.

These policies would also have to integrate some assurance from device manufacturers that they will produce the hardware and software or compatible parts to allow patients to keep the neural device functional. We recognize these are not easy problems to fix, and will likely involve significant investment from different stakeholders, but we believe that responsible development and translation of brain-based INDs will require some assurances to help patient-participants who benefit from these experimental devices maintain the treatment-resistant condition controlled. Identifying effective and feasible post-trial management strategies will require input from device manufacturers and other key stakeholders. Post-trial neural device maintenance programs would also signal to future patients that the developers consider the safety of their device users as paramount.

Conclusion

Brain-based implantable neuromodulation devices bring hope for patients in need of novel alternatives to manage severe treatment-resistant psychiatric conditions. As investment in neural device development expands, so will trials, and eventually FDA approvals, and translation of these devices into clinical care. Human fidelity should ideally be woven into the fabric of the design and clinical translation to help promote alignment with stakeholder needs and expectations, this, in turn, can help optimize access to INDs and promote the willingness of patients and providers to uptake and offer the device. Each of these stages requires careful consideration and empirical examination of stakeholder perspectives, as well as analysis of the ethical, legal, social, policy, and clinical implications of these technologies and their intended uses. The earlier device developers can identify and address stakeholder concerns or other considerations the more likely they are to develop devices that are responsive to patients’ needs and what patients perceive as beneficial for their condition and quality of life. The goal should be to develop devices that patients will want to use with relatively little hesitation as opposed to devices they would only use as a last resort, if at all, and to protect the patients that are making these developments possible for future generations.

To help maximize the responsible development of neural devices for psychiatry, we suggest that device developers in this space: 1) design the device, workflow, and messaging with human fidelity as a measurable performance variable considered throughout the development and translation process, 2) increase training on biomedical research ethics and clinical ethics to have a better understanding of ethical and regulatory considerations when integrating technology in medicine, 3) gain more exposure to patients in psychiatry and their experiences, 4) increase communication between device developers, neuromodulation researchers, and clinicians who will be the main gatekeepers of these technologies (e.g., psychiatrists). Finally, 5) more integration of experts in ethical, legal, and social implications of developments in biotechnology will help device developers identify, empirically examine, and address issues that can negatively impact the well-being of patients, their access to neuromodulation, and their willingness to uptake these technologies.

References

  • 1.Krauss JK et al. Technology of deep brain stimulation: current status and future directions. Nat Rev Neurol 17, 75–87 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Rao N 400 Billion Reasons To Believe In Brain-Computer Interfaces. Forbes (2024). [Google Scholar]
  • 3.Beckers M, Bloem BR & Verbeek MM Mechanisms of peripheral levodopa resistance in Parkinson’s disease. NPJ Parkinsons Dis 8, 56 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Morgante L et al. How many parkinsonian patients are suitable candidates for deep brain stimulation of subthalamic nucleus? Results of a questionnaire. Parkinsonism Relat Disord 13, 528–531 (2007). [DOI] [PubMed] [Google Scholar]
  • 5.Lange M et al. Underutilization of deep brain stimulation for Parkinson’s disease? A survey on possible clinical reasons. Acta Neurochir (Wien) 159, 771–778 (2017). [DOI] [PubMed] [Google Scholar]
  • 6.Wagle Shukla A, Deeb W, Patel B & Ramirez-Zamora A Is deep brain stimulation therapy underutilized for movement disorders? Expert Rev Neurother 18, 899–901 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Zhdanava M et al. The Prevalence and National Burden of Treatment-Resistant Depression and Major Depressive Disorder in the United States. J Clin Psychiatry 82, 20m13699 (2021). [DOI] [PubMed] [Google Scholar]
  • 8.Sultana B et al. Incidence and Prevalence of Drug-Resistant Epilepsy: A Systematic Review and Meta-analysis. Neurology 96, 805–817 (2021). [DOI] [PubMed] [Google Scholar]
  • 9.Neuralink Corp. Precise Robotically IMplanted Brain-Computer InterfacE (PRIME). https://clinicaltrials.gov/study/NCT06429735.
  • 10.Synchron. COMMAND Early Feasibility Study: Implantable BCI to Control a Digital Device for People With Paralysis. COMMAND Early Feasibility Study: Implantable BCI to Control a Digital Device for People With Paralysis https://clinicaltrials.gov/study/NCT05035823. [Google Scholar]
  • 11.Louis ED, Rios E & Henchcliffe C How are we doing with the treatment of essential tremor (ET)?: Persistence of patients with ET on medication: data from 528 patients in three settings. Eur J Neurol 17, 882–884 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Health Sciences Policy; Forum on Neuroscience and Nervous System Disorders; Norris SMP, Childers E, Pool R, editors. Exploring the Adoption of Implantable Brain Stimulation into Standard of Care for Central Nervous System Disorders: Proceedings of a Workshop. (2024) %20 10.17226/27657. [DOI] [PubMed] [Google Scholar]
  • 13.Concannon TW et al. A New Taxonomy for Stakeholder Engagement in Patient-Centered Outcomes Research. J GEN INTERN MED 27, 985–991 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Food & Drug Administration. Pre-market Approval: VNS THERAPY SYSTEM. (2005). [Google Scholar]
  • 15.Carmi L et al. Efficacy and Safety of Deep Transcranial Magnetic Stimulation for Obsessive-Compulsive Disorder: A Prospective Multicenter Randomized Double-Blind Placebo-Controlled Trial. Am J Psychiatry 176, 931–938 (2019). [DOI] [PubMed] [Google Scholar]
  • 16.FDA News Release. FDA permits marketing of transcranial magnetic stimulation for treatment of obsessive compulsive disorder. (2018). [Google Scholar]
  • 17.FDA. Premarket Approval (PMA) for Flow FL-100: Cranial Electrotherapy Stimulator to Treat Depression. (2025). [Google Scholar]
  • 18.Hitti FL et al. Future directions in psychiatric neurosurgery: Proceedings of the 2022 American Society for Stereotactic and Functional Neurosurgery meeting on surgical neuromodulation for psychiatric disorders. Brain Stimul 16, 867–878 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Johnson KA, Gunduz A & Okun MS Deep brain stimulation for Gilles de la Tourette syndrome. Handb Clin Neurol 215, 283–297 (2026). [DOI] [PubMed] [Google Scholar]
  • 20.Nadeem SF et al. Efficacy of deep brain stimulation in treating the negative symptoms of treatment-resistant schizophrenia: Systematic review of human studies. Surg Neurol Int 16, 534 (2025). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Cohen SE et al. Deep brain stimulation for obsessive-compulsive-disorder: a systematic review and meta-analysis of individual participant outcome data from sham-controlled trials. Mol Psychiatry 30, 4937–4947 (2025). [DOI] [PubMed] [Google Scholar]
  • 22.Mol GJJ et al. Pre-operative predictors of response to deep brain stimulation in depression: A systematic review and meta-analysis. Journal of Affective Disorders 388, 119387 (2025). [DOI] [PubMed] [Google Scholar]
  • 23.Holtzheimer PE et al. Subcallosal cingulate deep brain stimulation for treatment-resistant depression: a multisite, randomised, sham-controlled trial. The Lancet Psychiatry 4, 839–849 (2017). [DOI] [PubMed] [Google Scholar]
  • 24.Hitti FL, Yang AI, Cristancho MA & Baltuch GH Deep Brain Stimulation Is Effective for Treatment-Resistant Depression: A Meta-Analysis and Meta-Regression. JCM 9, 2796 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Xue T et al. Comparative efficacy and safety of different surgical strategies for refractory obsessive-compulsive disorder: evidence from network meta-analysis. Mol Psychiatry https://doi.org/10.1038/s41380-025-03438-7 (2026) doi: 10.1038/s41380-025-03438-7. [DOI] [PubMed] [Google Scholar]
  • 26.Abdulbaki A et al. Efficacy of deep brain stimulation for obsessive-compulsive disorder: umbrella review and updated meta-analysis. Psychiatry Research 351, 116651 (2025). [DOI] [PubMed] [Google Scholar]
  • 27.Norman DA & Draper SW User Centered System Design: New Perspectives on Human-Computer Interaction. (L. Erlbaum, Hillsdale (N. J.) London, 1986). [Google Scholar]
  • 28.Norman DA The Design of Everyday Things. (MIT press, Cambridge (Mass.), 2013). [Google Scholar]
  • 29.Moran TP, Newell A & Card SK The Psychology of Human-Computer Interaction. (Chapman and Hall/CRC, Boca Raton, 2017). [Google Scholar]
  • 30.Food & Drug Administration. Applying Human Factors and Usability Engineering to Medical Devices: Guidance for Industry and Food and Drug Administration Staff. (2016). [Google Scholar]
  • 31.International Electrotechnical Commission. IEC 62366–1:2015/AMD1:2020 — Medical Devices: Application of Usability Engineering to Medical Devices. Geneva: IEC; 2015 (amended 2020). [Google Scholar]
  • 32.Forsythe LP et al. Patient Engagement In Research: Early Findings From The Patient-Centered Outcomes Research Institute. Health Aff (Millwood) 38, 359–367 (2019). [DOI] [PubMed] [Google Scholar]
  • 33.Howes OD, Thase ME & Pillinger T Treatment resistance in psychiatry: state of the art and new directions. Mol Psychiatry 27, 58–72 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Thanvi B, Lo N & Robinson T Essential tremor-the most common movement disorder in older people. Age Ageing 35, 344–349 (2006). [DOI] [PubMed] [Google Scholar]
  • 35.Wong JK et al. Deep brain stimulation in essential tremor: targets, technology, and a comprehensive review of clinical outcomes. Expert Rev Neurother 20, 319–331 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Louis ED & Ferreira JJ How common is the most common adult movement disorder? Update on the worldwide prevalence of essential tremor. Mov Disord 25, 534–541 (2010). [DOI] [PubMed] [Google Scholar]
  • 37.Willis AW et al. Incidence of Parkinson disease in North America. NPJ Parkinsons Dis 8, 170 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Starr PA et al. Five-Year Outcomes from Deep Brain Stimulation of the Subthalamic Nucleus for Parkinson Disease. JAMA Neurol 82, 1181 (2025). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Weaver FM Bilateral Deep Brain Stimulation vs Best Medical Therapy for Patients With Advanced Parkinson DiseaseA Randomized Controlled Trial. JAMA 301, 63 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Deuschl G et al. A Randomized Trial of Deep-Brain Stimulation for Parkinson’s Disease. N Engl J Med 355, 896–908 (2006). [DOI] [PubMed] [Google Scholar]
  • 41.Okun MS et al. Subthalamic deep brain stimulation with a constant-current device in Parkinson’s disease: an open-label randomised controlled trial. The Lancet Neurology 11, 140–149 (2012). [DOI] [PubMed] [Google Scholar]
  • 42.Follett KA et al. Pallidal versus Subthalamic Deep-Brain Stimulation for Parkinson’s Disease. N Engl J Med 362, 2077–2091 (2010). [DOI] [PubMed] [Google Scholar]
  • 43.Schuepbach WMM et al. Neurostimulation for Parkinson’s Disease with Early Motor Complications. N Engl J Med 368, 610–622 (2013). [DOI] [PubMed] [Google Scholar]
  • 44.Odekerken VJ et al. Subthalamic nucleus versus globus pallidus bilateral deep brain stimulation for advanced Parkinson’s disease (NSTAPS study): a randomised controlled trial. The Lancet Neurology 12, 37–44 (2013). [DOI] [PubMed] [Google Scholar]
  • 45.Williams A et al. Deep brain stimulation plus best medical therapy versus best medical therapy alone for advanced Parkinson’s disease (PD SURG trial): a randomised, open-label trial. The Lancet Neurology 9, 581–591 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Medtronic. Medtronic earns FDA approval for expanded deep brain stimulation labeling for Dystonia. (2025). [Google Scholar]
  • 47.Medtronic. Medtronic Receives FDA Approval for Deep Brain Stimulation Therapy for Medically Refractory Epilepsy. (2018). [Google Scholar]
  • 48.Food & Drug Administration. FDA Premarket Approval for the RNS System. (2013). [Google Scholar]
  • 49.Food & Drug Administration. Getting a Humanitarian Use Device to Market. (2022). [Google Scholar]
  • 50.ClinicalTrials.gov. Treatment ResistAnt Depression Subcallosal CingulatE Network DBS (TRANSCEND). (2026). [Google Scholar]
  • 51.ClinicalTrials.gov. Responsive Neurostimulation for Post-Traumatic Stress Disorder. (2024). [Google Scholar]
  • 52.ClinicalTrials.gov. Deep Brain Stimulation in Treatment Resistant Schizophrenia. (2025). [Google Scholar]
  • 53.ClinicalTrials.gov. Individualized Adaptive Deep Brain Stimulation in Opioid Use Disorder (aDBS OUD). (2025). [Google Scholar]
  • 54.ClinicalTrials.gov. Exploring DBS Efficacy in Treatment-refractory Tourette’s Syndrome. (2024). [Google Scholar]
  • 55.Kestenbaum M, Ford B & Louis ED Estimating the Proportion of Essential Tremor and Parkinson’s Disease Patients Undergoing Deep Brain Stimulation Surgery: Five-Year Data From Columbia University Medical Center (2009–2014). Mov Disord Clin Pract 2, 384–387 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Zaman MS, Ghahari S & McColl MA Barriers to Accessing Healthcare Services for People with Parkinson’s Disease: A Scoping Review. J Parkinsons Dis 11, 1537–1553 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Agency for Healthcare Research and Quality. HCUP Databases. Healthcare Cost and Utilization Project (HCUP). [PubMed] [Google Scholar]
  • 58.Venkatraman V et al. Disparities in Access to Deep Brain Stimulation and Responsive Neurostimulation Approaches to Drug-Resistant Epilepsy. Neuromodulation 27, 792–799 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Deshpande N, Hadi M & Ali R Healthcare disparities in deep brain stimulation access and utilization: a systematic review. J Neurosurg 140, 1137–1147 (2024). [DOI] [PubMed] [Google Scholar]
  • 60.Cavaleri J, Stefanescu K, Lee D & Mason X Deep brain stimulation access in 2025: geographic, gender, racial, and socioeconomic disparities re-examined. Curr Opin Neurol 38, 330–336 (2025). [DOI] [PubMed] [Google Scholar]
  • 61.Kim M-R et al. Patients’ reluctance to undergo deep brain stimulation for Parkinson’s disease. Parkinsonism Relat Disord 23, 91–94 (2016). [DOI] [PubMed] [Google Scholar]
  • 62.Das S et al. Capturing Initial Understanding and Impressions of Surgical Therapy for Parkinson’s Disease. Front Neurol 12, 605959 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Furrer RA et al. Public Perceptions of Neurotechnologies Used to Target Mood, Memory, and Motor Symptoms. Device 100804 (2025) doi: 10.1016/j.device.2025.100804. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Lannon M et al. Mixed Methods Survey to Identify Barriers to Accessing Deep Brain Stimulation for Movement Disorders in Canada. Can J Neurol Sci 1–19 (2025) doi: 10.1017/cjn.2025.71. [DOI] [PubMed] [Google Scholar]
  • 65.Cabrera LY, Young Han C, Ostendorf T, Jimenez-Shahed J & Sarva H Neurologists’ Attitudes Toward Use and Timing of Deep Brain Stimulation. Neurol Clin Pract 11, 506–516 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Shih LC & Tarsy D Survey of U.S. neurologists’ attitudes towards deep brain stimulation for Parkinson’s disease. Neuromodulation 14, 208–213; discussion 213 (2011). [DOI] [PubMed] [Google Scholar]
  • 67.Torgerson LN et al. Clinical and Psychosocial Factors Considered When Deciding Whether to Offer Deep Brain Stimulation for Childhood Dystonia. Neuromodulation 26, 1646–1652 (2023). [DOI] [PubMed] [Google Scholar]
  • 68.Venkatraman V et al. Disparities in the treatment of movement disorders using deep brain stimulation. J Neurosurg 141, 241–251 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Soda T et al. Child and Adolescent Psychiatrists’ Perceptions of Utility and Self-rated Knowledge of Genetic Testing Predict Usage for Autism Spectrum Disorder. J Am Acad Child Adolesc Psychiatry 60, 657–660 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Eban K Brain biopsies on ‘vulnerable’ patients at Mount Sinai set off alarm bells at FDA, documents show. STAT (2024). [Google Scholar]
  • 71.Zuk P & Lázaro-Muñoz G Treatment Search Fatigue and Informed Consent. AJOB Neurosci 12, 77–79 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Fins JJ Rights Come to Mind: Brain Injury, Ethics, and the Struggle for Consciousness. (Cambridge University Press, New York, NY, 2015). [Google Scholar]
  • 73.Giacino JT et al. Practice guideline update recommendations summary: Disorders of consciousness: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology; the American Congress of Rehabilitation Medicine; and the National Institute on Disability, Independent Living, and Rehabilitation Research. Neurology 91, 450–460 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Bodien YG et al. Cognitive Motor Dissociation in Disorders of Consciousness. N Engl J Med 391, 598–608 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Baker KB et al. Cerebellar deep brain stimulation for chronic post-stroke motor rehabilitation: a phase I trial. Nat Med 29, 2366–2374 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Schiff ND et al. Thalamic deep brain stimulation in traumatic brain injury: a phase 1, randomized feasibility study. Nat Med 29, 3162–3174 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Woods JE et al. Miniature battery-free epidural cortical stimulators. Sci Adv 10, eadn0858 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78.Cleveland Clinic. High-Intensity Focused Ultrasound (HIFU). (2022). [Google Scholar]
  • 79.Food & Drug Administration. Premarket Approval: Exablate Model 4000 Type-1 System. (2025). [Google Scholar]
  • 80.ClinicalTrials.gov. Low Intensity Focused Ultrasound: a New Paradigm for Depression and Anxiety (LIFU). NCT05147142. (2025). [Google Scholar]
  • 81.Synchron. Synchron.
  • 82.Gilbert F, Viaña JNM, Ineichen C. Deflating the ‘DBS causes personality changes’ blubble. Neuroethics 19, 1–17 (2018). [Google Scholar]
  • 83.Zuk P et al. Researcher Views on Changes in Personality, Mood, and Behavior in Next-Generation Deep Brain Stimulation. AJOB Neurosci 14, 287–299 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 84.Merner AR et al. Participant perceptions of changes in psychosocial domains following participation in an adaptive deep brain stimulation trial. Brain Stimul 16, 990–998 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 85.Goering S, Klein E, Dougherty D & Widge A Staying in the loop: Relational agency and identity in next-generation DBS for psychiatry. AJOB neuroscience 3, 59–70 (2017). [Google Scholar]
  • 86.Lázaro-Muñoz G, McGuire AL & Goodman WK Should we be concerned about preserving agency and personal identity in patients with Adaptive Deep Brain Stimulation systems? AJOB Neurosci 8, 73–75 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 87.Merner AR et al. A Patient-Centered Perspective on Changes in Personal Characteristics After Deep Brain Stimulation. JAMA Netw Open 7, e2434255 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 88.Kubu CS et al. Pragmatism and the Importance of Interdisciplinary Teams in Investigating Personality Changes following DBS. Neuroethics 2019, 95–105 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 89.Merner AR et al. Changes in Patients’ Desired Control of Their Deep Brain Stimulation and Subjective Global Control Over the Course of Deep Brain Stimulation. Front Hum Neurosci 15, 642195 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 90.Wilt JA, Merner AR, Zeigler J, Montpetite M & Kubu CS Does Personality Change Follow Deep Brain Stimulation in Parkinson’s Disease Patients? Front Psychol 12, 643277 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 91.Food & Drug Administration. Factors to consider regarding benefit-risk in medical device product availability, compliance, and enforcement decisions: guidance for industry and Food and Drug Administration staff. (2016). [Google Scholar]
  • 92.Underwood E Researchers grapple with the ethics of testing brain implants. Science (2017). [Google Scholar]
  • 93.Drew L ‘Like taking away a part of myself’ - life after a neural implant trial. Nat Med 26, 1154–1156 (2020). [DOI] [PubMed] [Google Scholar]
  • 94.Drew L Abandoned: the human cost of neurotechnology failure. Nature https://doi.org/10.1038/d41586-022-03810-5 (2022) doi: 10.1038/d41586-022-03810-5. [DOI] [PubMed] [Google Scholar]
  • 95.Strickland E & Harris M What Happens When a Bionic Body Part Becomes Obsolete?: Blind People with Second Sight’s Retinal Implants Found Out. IEEE Spectr. 59, 24–31 (2022). [Google Scholar]
  • 96.Ungar L Grant cuts reveal a crisis for patients with experimental brain implants. Associated Press (2025). [Google Scholar]
  • 97.Lázaro-Muñoz G, Yoshor D, Beauchamp MS, Goodman WK & McGuire AL Continued access to investigational brain implants. Nat Rev Neurosci 19, 317–318 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 98.Lázaro-Muñoz G et al. Post-trial access in implanted neural device research: Device maintenance, abandonment, and cost. Brain Stimul 15, 1029–1036 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 99.Hendriks S et al. Ethical Challenges of Risk, Informed Consent, and Posttrial Responsibilities in Human Research With Neural Devices: A Review. JAMA Neurol 76, 1506–1514 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 100.Muñoz KA et al. Researcher Perspectives on Ethical Considerations in Adaptive Deep Brain Stimulation Trials. Front Hum Neurosci 14, 578695 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 101.Merner AR, Fins JJ & Lázaro-Muñoz G Brain Device Research and the Underappreciated Role of Care Partners before, during, and Post-Trial. AJOB Neurosci 13, 236–239 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 102.Sankary LR et al. Exit from Brain Device Research: A Modified Grounded Theory Study of Researcher Obligations and Participant Experiences. AJOB Neurosci 13, 215–226 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 103.Fins JJ, Merner AR, Wright MS & Lázaro-Muñoz G Identity Theft, Deep Brain Stimulation, and the Primacy of Post-trial Obligations. Hastings Cent Rep 54, 34–41 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 104.Sierra-Mercado D et al. Device Removal Following Brain Implant Research. Neuron 103, 759–761 (2019). [DOI] [PubMed] [Google Scholar]
  • 105.Dasgupta I et al. What Happens After a Neural Implant Study? Neuroethics Expert Workshop on Post-Trial Obligations. Neuroethics 17, 22 (2024). [Google Scholar]
  • 106.Higgins N et al. Post-trial access to implantable neural devices: an exploratory international survey. BMJ Surg Interv Health Technol 6, e000262 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 107.Okun MS et al. Definition of Implanted Neurological Device Abandonment: A Systematic Review and Consensus Statement. JAMA Netw Open 7, e248654 (2024). [DOI] [PubMed] [Google Scholar]
  • 108.Van Stuijvenberg OC et al. Ethical Guidance for Investigational Implantable Neural Device Studies: Recommendations for Researchers and Research Ethics Committees. Neuromodulation: Technology at the Neural Interface S1094715926000061 (2026) doi: 10.1016/j.neurom.2025.12.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 109.Vora AK, Ward H, Foote KD, Goodman WK & Okun MS Rebound symptoms following battery depletion in the NIH OCD DBS cohort: Clinical and reimbursement issues. Brain Stimulation 5, 599–604 (2012). [DOI] [PubMed] [Google Scholar]
  • 110.Ooms P et al. Rebound of Affective Symptoms Following Acute Cessation of Deep Brain Stimulation in Obsessive-compulsive Disorder. Brain Stimulation 7, 727–731 (2014). [DOI] [PubMed] [Google Scholar]
  • 111.Johnson KA et al. Proceedings of the 11th Annual Deep Brain Stimulation Think Tank: pushing the forefront of neuromodulation with functional network mapping, biomarkers for adaptive DBS, bioethical dilemmas, AI-guided neuromodulation, and translational advancements. Front Hum Neurosci 18, 1320806 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 112.Grady C The challenge of assuring continued post-trial access to beneficial treatment. Yale J Health Policy Law Ethics 5, 425–435 (2005). [PubMed] [Google Scholar]
  • 113.Rotenberg A, Gunning M, Nadler RM, Kiss ZHT & Illes J A liability framework for high-risk neural devices. Science 388, 1136–1138 (2025). [DOI] [PubMed] [Google Scholar]

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