Industrial perspective on deep brain stimulation: history, current state, and future developments

Advances in Deep Brain Stimulation: From Mechanisms to Applications

Deep brain stimulation (DBS) is an effective therapy for various neurologic and neuropsychiatric disorders, involving chronic implantation of electrodes into target brain regions for electrical stimulation delivery. Despite its safety and efficacy, DBS remains an underutilized therapy. Advances in the field of DBS, including in technology, mechanistic understanding, and applications have the potential to expand access and use of DBS, while also improving clinical outcomes. Developments in DBS technology, such as MRI compatibility and bidirectional DBS systems capable of sensing neural activity while providing therapeutic stimulation, have enabled advances in our understanding of DBS mechanisms and its application. In this review, we summarize recent work exploring DBS modulation of target networks. We also cover current work focusing on improved programming and the development of novel stimulation paradigms that go beyond current standards of DBS, many of which are enabled by sensing-enabled DBS systems and have the potential to expand access to DBS.

Past, Present, and Future of Deep Brain Stimulation: Hardware, Software, Imaging, Physiology and Novel Approaches

Deep brain stimulation (DBS) has advanced treatment options for a variety of neurologic and neuropsychiatric conditions. As the technology for DBS continues to progress, treatment efficacy will continue to improve and disease indications will expand. Hardware advances such as longer-lasting batteries will reduce the frequency of battery replacement and segmented leads will facilitate improvements in the effectiveness of stimulation and have the potential to minimize stimulation side effects. Targeting advances such as specialized imaging sequences and “connectomics” will facilitate improved accuracy for lead positioning and trajectory planning. Software advances such as closed-loop stimulation and remote programming will enable DBS to be a more personalized and accessible technology. The future of DBS continues to be promising and holds the potential to further improve quality of life. In this review we will address the past, present and future of DBS.

Parkinson's Disease and Deep Brain Stimulation Have an Impact on My Life: A Multimodal Study on the Experiences of Patients and Family Caregivers

Parkinson’s disease (PD) has a large impact on patients’ physical and mental health, which also greatly affects their family caregivers. Deep brain stimulation (DBS) has emerged as an effective treatment for PD, but different authors have expressed their concerns about the potential impact of DBS on personality and identity. Our study aims at better understanding how patients and family caregivers experience life with PD and DBS, the impact of both on their personal and social lives, and their perception of the changes that have occurred as a result of the disease and the treatment. Our study applies a multimodal approach by means of narrative semi-structured interviews and drawings. Seven principal themes have been identified: “everyone’s Parkinson’s is different”, “changing as a person during the disease”, “going through Parkinson’s together”, “DBS improved my life”, “I am treated with DBS but I have Parkinson’s still”, “DBS is not perfect”, and “being different after DBS”. PD is perceived as an unpredictable and heterogeneous disease that changes from person to person, as does the effect of DBS. While DBS side-effects may have an impact on patients’ personality, behavior, and self-perception, PD symptoms and drug side-effects also have a great impact on these aspects.

Neuroethics Inside and Out: A Comparative Survey of Neural Device Industry Representatives and the General Public on Ethical Issues and Principles in Neurotechnology

Neurotechnologies are rapidly being developed with the aim of alleviating suffering caused by disease and assisting individuals with various disabilities. As the capabilities and applications of neural devices advance, potential ethical challenges related to agency, identity, privacy, equality, normality and justice have been noted. We sought to explore attitudes toward these ethical challenges in two important, but understudied groups of stakeholders-members of the neural device industry and members of the general public. Survey responses from 66 industry professionals and 1088 members of the general public who do not work with neural devices were collected. After controlling for demographic differences between the groups (industry vs. general public; age, gender, racial/ethnic background), we found a large degree of consistency between the groups in their attitudes toward the ethical topic areas and the need for guiding ethical principles, but also some differences related to privacy, consent, and confidence in the neural device industry to incorporate ethical concerns into the design process. These data have implications for industry professionals tasked with designing and disseminating new neural devices, end-users of their products, and stakeholders at each step in between who must navigate the rapidly-growing landscape of advances in neurotechnology.

Neural engineering: the process, applications, and its role in the future of medicine

OBJECTIVE

Recent advances in neural engineering have restored mobility to people with paralysis, relieved symptoms of movement disorders, reduced chronic pain, restored the sense of hearing, and provided sensory perception to individuals with sensory deficits.

APPROACH

This progress was enabled by the team-based, interdisciplinary approaches used by neural engineers. Neural engineers have advanced clinical frontiers by leveraging tools and discoveries in quantitative and biological sciences and through collaborations between engineering, science, and medicine. The movement toward bioelectronic medicines, where neuromodulation aims to supplement or replace pharmaceuticals to treat chronic medical conditions such as high blood pressure, diabetes and psychiatric disorders is a prime example of a new frontier made possible by neural engineering. Although one of the major goals in neural engineering is to develop technology for clinical applications, this technology may also offer unique opportunities to gain insight into how biological systems operate.

MAIN RESULTS

Despite significant technological progress, a number of ethical and strategic questions remain unexplored. Addressing these questions will accelerate technology development to address unmet needs. The future of these devices extends far beyond treatment of neurological impairments, including potential human augmentation applications. Our task, as neural engineers, is to push technology forward at the intersection of disciplines, while responsibly considering the readiness to transition this technology outside of the laboratory to consumer products.

SIGNIFICANCE

This article aims to highlight the current state of the neural engineering field, its links with other engineering and science disciplines, and the challenges and opportunities ahead. The goal of this article is to foster new ideas for innovative applications in neurotechnology.

Currents of memory: recent progress, translational challenges, and ethical considerations in fornix deep brain stimulation trials for Alzheimer's disease

The serendipitous discovery of triggered autobiographical memories and eventual memory improvement in an obese patient who received fornix deep brain stimulation in 2008 paved the way for several phase I and phase II clinical trials focused on the safety and efficacy of this potential intervention for people with Alzheimer's disease. In this article, we summarize clinical trials and case reports on fornix deep brain stimulation for Alzheimer's disease and review experiments on animal models evaluating the physiological or behavioral effects of this intervention. Based on information from these reports and studies, we identify potential translational challenges of this approach and determine practical and ethical considerations for clinical trials, focusing on issues regarding selection criteria, trial design, and outcome evaluation. Based on initial results suggesting greater benefit for those with milder disease stage, we find it essential that participant expectations are carefully managed to avoid treatment disenchantment and/or frustration from participants and caregivers. Finally, we urge for collaboration between centers to establish proper clinical standards and to promote better trial results comparison.

Intraoperative acceleration measurements to quantify improvement in tremor during deep brain stimulation surgery

Deep brain stimulation (DBS) surgery is extensively used in the treatment of movement disorders. Nevertheless, methods to evaluate the clinical response during intraoperative stimulation tests to identify the optimal position for the implantation of the chronic DBS lead remain subjective. In this paper, we describe a new, versatile method for quantitative intraoperative evaluation of improvement in tremor with an acceleration sensor that is mounted on the patient's wrist during surgery. At each anatomical test position, the improvement in tremor compared to the initial tremor is estimated on the basis of extracted outcome measures. This method was tested on 15 tremor patients undergoing DBS surgery in two centers. Data from 359 stimulation tests were acquired. Our results suggest that accelerometric evaluation detects tremor changes more sensitively than subjective visual ratings. The effective stimulation current amplitudes identified from the quantitative data (1.1 ± 0.8 mA) are lower than those identified by visual evaluation (1.7 ± 0.8 mA) for similar improvement in tremor. Additionally, if these data had been used to choose the chronic implant position of the DBS lead, 15 of the 26 choices would have been different. These results show that our method of accelerometric evaluation can potentially improve DBS targeting.

Autonomous Optimization of Targeted Stimulation of Neuronal Networks

Driven by clinical needs and progress in neurotechnology, targeted interaction with neuronal networks is of increasing importance. Yet, the dynamics of interaction between intrinsic ongoing activity in neuronal networks and their response to stimulation is unknown. Nonetheless, electrical stimulation of the brain is increasingly explored as a therapeutic strategy and as a means to artificially inject information into neural circuits. Strategies using regular or event-triggered fixed stimuli discount the influence of ongoing neuronal activity on the stimulation outcome and are therefore not optimal to induce specific responses reliably. Yet, without suitable mechanistic models, it is hardly possible to optimize such interactions, in particular when desired response features are network-dependent and are initially unknown. In this proof-of-principle study, we present an experimental paradigm using reinforcement-learning (RL) to optimize stimulus settings autonomously and evaluate the learned control strategy using phenomenological models. We asked how to (1) capture the interaction of ongoing network activity, electrical stimulation and evoked responses in a quantifiable ‘state’ to formulate a well-posed control problem, (2) find the optimal state for stimulation, and (3) evaluate the quality of the solution found. Electrical stimulation of generic neuronal networks grown from rat cortical tissue in vitro evoked bursts of action potentials (responses). We show that the dynamic interplay of their magnitudes and the probability to be intercepted by spontaneous events defines a trade-off scenario with a network-specific unique optimal latency maximizing stimulus efficacy. An RL controller was set to find this optimum autonomously. Across networks, stimulation efficacy increased in 90% of the sessions after learning and learned latencies strongly agreed with those predicted from open-loop experiments. Our results show that autonomous techniques can exploit quantitative relationships underlying activity-response interaction in biological neuronal networks to choose optimal actions. Simple phenomenological models can be useful to validate the quality of the resulting controllers.

Electrical stimulation of the brain is increasingly used to alleviate the symptoms of a range of neurological disorders and as a means to artificially inject information into neural circuits in neuroprosthetic applications. Machine learning has been proposed to find optimal stimulation settings autonomously. However, this approach is impeded by the complexity of the interaction between the stimulus and the activity of the network, which makes it difficult to test how good the result actually is. We used phenomenological models of the interaction between stimulus and spontaneous activity in a neuronal network to design a testable machine learning challenge and evaluate the quality of the solution found by the algorithm. In this task, the learning algorithm had to find a solution that balances competing interdependencies of ongoing neuronal activity with opposing effects on the efficacy of stimulation. We show that machine learning can successfully solve this task and that the solutions found are close to the optimal settings to maximize the efficacy of stimulation. Since the paradigm involves several typical problems found in other settings, such concepts could help to formalize machine learning problems in more complex biological networks and to test the quality of their performance.

Non-human Primates in Neuroscience Research: The Case against its Scientific Necessity

Public opposition to non-human primate (NHP) experiments is significant, yet those who defend them cite minimal harm to NHPs and substantial human benefit. Here we review these claims of benefit, specifically in neuroscience, and show that: a) there is a default assumption of their human relevance and benefit, rather than robust evidence; b) their human relevance and essential contribution and necessity are wholly overstated; c) the contribution and capacity of non-animal investigative methods are greatly understated; and d) confounding issues, such as species differences and the effects of stress and anaesthesia, are usually overlooked. This is the case in NHP research generally, but here we specifically focus on the development and interpretation of functional magnetic resonance imaging (fMRI), deep brain stimulation (DBS), the understanding of neural oscillations and memory, and investigation of the neural control of movement and of vision/binocular rivalry. The increasing power of human-specific methods, including advances in fMRI and invasive techniques such as electrocorticography and single-unit recordings, is discussed. These methods serve to render NHP approaches redundant. We conclude that the defence of NHP use is groundless, and that neuroscience would be more relevant and successful for humans, if it were conducted with a direct human focus. We have confidence in opposing NHP neuroscience, both on scientific as well as on ethical grounds.

Use of quantitative tremor evaluation to enhance target selection during deep brain stimulation surgery for essential tremor

Deep brain stimulation (DBS), an effective surgical treatment for Essential Tremor (ET), requires test stimulations in the thalamus to find the optimum site for permanent electrode implantation. During these test stimulations, the changes in tremor are only visually evaluated. This, along with other parameters, increases the subjectivity when comparing the efficacy of different thalamic nuclei. We developed a method to quantitatively evaluate tremor during the test stimulations of DBS surgery and applied to 6 ET patients undergoing this treatment. From the quantitative data collected, we identified effective stimulation amplitudes for every test stimulation position and compared it with the ones identified visually during the surgery. We also classified the data based on the thalamic nuclei in which the center of the stimulating contact was present during test stimulations. Results indicate that, to achieve the same reduction in tremor, on average, the stimulation amplitude identified by our method was 0.6 mA lower than those identified by visual evaluation. The comparison of the different thalamic nuclei showed that stimulations in the Ventro-oral and the Intermediolateral nuclei of the thalamus result in higher reduction in tremor for similar stimulation amplitudes as the frequently targeted Ventrointermediate nucleus. We conclude that our quantitative tremor evaluation method is more sensitive than the widely used visual evaluation. Using such quantitative methods will aid in identifying the optimum target structure for patients undergoing DBS.

The temporal pattern of stimulation may be important to the mechanism of deep brain stimulation

Deep brain stimulation (DBS) has emerged as an important and potentially powerful treatment option for the management of carefully selected patients with advanced Parkinson's disease (PD) who are not adequately controlled by standard medication therapy. Though considerable advances have been made, the mechanisms underlying the therapeutic effects of DBS remain unclear despite its clinical efficacy. It is now widely held that both excitation and inhibition can occur secondary to stimulation, and it is suspected that abnormal synchronized oscillations may also be important in the mechanism of DBS. Other potentially important processes, including blood flow changes, local and upstream neurogenesis, and the modulation of neurotransmitters through stimulation of bordering astrocytes are also being investigated. Recent research has suggested that the temporal pattern of DBS stimulation is also an important variable in DBS neuromodulation, yet the extent of its influence on DBS efficacy has yet to be determined. As high stimulation frequency alone does not appear to be sufficient for optimal symptom suppression, attention to stimulation pattern might lead to more effective symptom control and reduced side effects, possibly at a lower frequency. Stimulation pattern may be potentially amenable to therapeutic modulation and its role in the clinical efficacy of DBS should be addressed through further focus and research.