Deep Brain Stimulation, Brain Maps and Personalized Medicine: Lessons from the Human Genome Project

Identity Theft, Deep Brain Stimulation, and the Primacy of Post-trial Obligations

Patient narratives from two investigational deep brain stimulation trials for traumatic brain injury and obsessive-compulsive disorder reveal that injury and illness rob individuals of personal identity and that neuromodulation can restore it. The early success of these interventions makes a compelling case for continued post-trial access to these technologies. Given the centrality of personal identity to respect for persons, a failure to provide continued access can be understood to represent a metaphorical identity theft. Such a loss recapitulates the pain of an individual's initial injury or illness and becomes especially tragic because it could be prevented by robust policy. A failure to fulfill this normative obligation constitutes a breach of disability law, which would view post-trial access as a means to achieve social reintegration through this neurotechnological accommodation.

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis

The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.

Machine learning-based personalized subthalamic biomarkers predict ON-OFF levodopa states in Parkinson patients

Objective.Adaptive deep brain stimulation (aDBS) based on subthalamic nucleus (STN) electrophysiology has recently been proposed to improve clinical outcomes of DBS for Parkinson's disease (PD) patients. Many current models for aDBS are based on one or two electrophysiological features of STN activity, such as beta or gamma activity. Although these models have shown interesting results, we hypothesized that an aDBS model that includes many STN activity parameters will yield better clinical results. The objective of this study was to investigate the most appropriate STN neurophysiological biomarkers, detectable over long periods of time, that can predict OFF and ON levodopa states in PD patients.Approach.Long-term local field potentials (LFPs) were recorded from eight STNs (four PD patients) during 92 recording sessions (44 OFF and 48 ON levodopa states), over a period of 3-12 months. Electrophysiological analysis included the power of frequency bands, band power ratio and burst features. A total of 140 engineered features was extracted for 20 040 epochs (each epoch lasting 5 s). Based on these engineered features, machine learning (ML) models classified LFPs as OFF vs ON levodopa states.Main results.Beta and gamma band activity alone poorly predicts OFF vs ON levodopa states, with an accuracy of 0.66 and 0.64, respectively. Group ML analysis slightly improved prediction rates, but personalized ML analysis, based on individualized engineered electrophysiological features, were markedly better, predicting OFF vs ON levodopa states with an accuracy of 0.8 for support vector machine learning models.Significance.We showed that individual patients have unique sets of STN neurophysiological biomarkers that can be detected over long periods of time. ML models revealed that personally classified engineered features most accurately predict OFF vs ON levodopa states. Future development of aDBS for PD patients might include personalized ML algorithms.

DBS: a compelling example for ethical and legal reflection-a French perspective on ethical and legal concerns about DBS

Deep brain stimulation (DBS) is an approved treatment for neurological diseases and a promising one for psychiatric conditions, which may produce spectacular results very quickly. It is also a powerful tool for brain research and exploration. Beyond an overview of the ethical and legal literature on this topic, this paper aims at showing that DBS is a compelling example for ethical-legal reflection, as it combines a highly technical surgical procedure, a complex active medical device and neuromodulation of the human brain to restore lost abilities caused by a chronic and evolving disease. Some of the ethical and legal issues raised by DBS are not specific, but shed new light on medical ethics and law. Others are more DBS-specific, as they are linked to the intricacies of research and treatment, to the need to tune the device, to the patients' control over the device and its effects and to the involvement of family caregivers.

Patient's lived experience with DBS between medical research and care: some legal implications

In the past 50 years, an ethical-legal boundary has been drawn between treatment and research. It is based on the reasoning that the two activities pursue different purposes. Treatment is aimed at achieving optimal therapeutic benefits for the individual patient, whereas the goal of scientific research is to increase knowledge, in the public interest. From this viewpoint, the patient's experience should be clearly distinguished from that of a participant in a clinical trial. On this premise, two parallel and mutually exclusive regimes have been established. Yet in the case of deep brain stimulation (DBS), this presentation is a poor fit, for both the patient's lived experience and medical practice and research. The frictions may be explained by the specificities of the treatment (including surgery and medical devices) and of the pathologies concerned (chronic and evolutive), and by the characteristics of the medical team implementing the treatment. These particularities challenge the dominant frame of reference in medical bioethics and cause difficulties for the current legal framework in fulfilling its dual role: to protect patients while supporting the development of innovative treatments. The dominant model is still the clinical trial for medication safety and legal requirements of drug market regulation. However, DBS forces us to reflect on a medical device that is permanently implanted in the brain by highly specialized multi-disciplinary neurosurgical teams, for the treatment of chronic evolutive diseases. These devices demand fine-tuning on a case-by-case basis and there is still a lot to discover about why DBS is effective (or not). As a result, the wall between treatment and research is osmotic: many discoveries are made incidentally, in the course of treatment. The following study begins with these observations, and suggests that we review legal provisions (especially in French and United States law) so that they are better adapted to the first-person needs and experience of the patient undergoing brain stimulation.

Whither the "Improvement Standard"? Coverage for Severe Brain Injury after Jimmo v. Sebelius

As improvements in neuroscience have enabled a better understanding of disorders of consciousness as well as methods to treat them, a hurdle that has become all too prevalent is the denial of coverage for treatment and rehabilitation services. In 2011, a settlement emerged from a Vermont District Court case, Jimmo v. Sebelius, which was brought to stop the use of an "improvement standard" that required tangible progress over an identifiable period of time for Medicare coverage of services. While the use of this standard can have deleterious effects on those with many chronic conditions, it is especially burdensome for those in the minimally conscious state (MCS), where improvements are unpredictable and often not manifested through repeatable overt behaviors. Though the focus of this paper is on the challenges of brain injury and the minimally conscious state, which an estimated 100,000 to 200,000 individuals suffer from in the United States, the post-Jimmo arguments presented can and should have a broad impact as envisioned by the plaintiffs who brought the case on behalf of multiple advocacy groups representing patients with a range of chronic care conditions.

Personalized medicine in neurodegenerative diseases: how far away?

Neurodegenerative diseases are characterized by progressive dysfunction of the nervous system as a result of neuronal loss in the brain and spinal cord. Despite extensive research efforts aimed at development of new disease-modifying therapeutics, there is still no effective treatment to halt neurodegenerative processes. Thus, modification of current therapeutic and diagnostic research strategies is a goal of increasing urgency. The biggest limitation in neurodegenerative disease research is the lack of appropriate biomarkers. Discovery of universal biomarkers capable of diagnosing patients with neurodegenerative diseases, monitoring their response to therapy, and predicting disease progression seems to be a tall order. Instead, a combination of different methodologies in the discovery of biomarkers specific for each described aspect of the disease seems to be a more viable approach. Although application of personalized medicine in diagnosis and treatment of neurodegenerative diseases may seem far off, some recent developments, such as utilizing specific biological therapies in multiple sclerosis, microRNA profiling as a source of novel biomarkers in Parkinson’s disease, or combination of neuroimaging and proteomic analyses in diagnosis of Alzheimer’s disease patients, already point to the way clinical neurology may integrate new achievements in everyday practice. Combination of genomic, proteomic, glycomic, and metabolomic approaches may yield novel insights into molecular mechanisms of disease pathophysiology, which could then be integrated and translated into clinical neurology. Based on the developments during the past decade, it is feasible to predict that a personalized approach to treating neurological disorders will become more widely applicable in the coming years.