Technology for deep brain stimulation at a gallop

Toward personalized medicine in connectomic deep brain stimulation

At the group-level, deep brain stimulation leads to significant therapeutic benefit in a multitude of neurological and neuropsychiatric disorders. At the single-patient level, however, symptoms may sometimes persist despite "optimal" electrode placement at established treatment coordinates. This may be partly explained by limitations of disease-centric strategies that are unable to account for heterogeneous phenotypes and comorbidities observed in clinical practice. Instead, tailoring electrode placement and programming to individual patients' symptom profiles may increase the fraction of top-responding patients. Here, we propose a three-step, circuit-based framework with the aim of developing patient-specific treatment targets that address the unique symptom constellation prevalent in each patient. First, we describe how a symptom network target library could be established by mapping beneficial or undesirable DBS effects to distinct circuits based on (retrospective) group-level data. Second, we suggest ways of matching the resulting symptom networks to circuits defined in the individual patient (template matching). Third, we introduce network blending as a strategy to calculate optimal stimulation targets and parameters by selecting and weighting a set of symptom-specific networks based on the symptom profile and subjective priorities of the individual patient. We integrate the approach with published literature and conclude by discussing limitations and future challenges.

Plasticity and Adaptation in Neuromorphic Biohybrid Systems

Neuromorphic systems take inspiration from the principles of biological information processing to form hardware platforms that enable the large-scale implementation of neural networks. The recent years have seen both advances in the theoretical aspects of spiking neural networks for their use in classification and control tasks and a progress in electrophysiological methods that is pushing the frontiers of intelligent neural interfacing and signal processing technologies. At the forefront of these new technologies, artificial and biological neural networks are tightly coupled, offering a novel "biohybrid" experimental framework for engineers and neurophysiologists. Indeed, biohybrid systems can constitute a new class of neuroprostheses opening important perspectives in the treatment of neurological disorders. Moreover, the use of biologically plausible learning rules allows forming an overall fault-tolerant system of co-developing subsystems. To identify opportunities and challenges in neuromorphic biohybrid systems, we discuss the field from the perspectives of neurobiology, computational neuroscience, and neuromorphic engineering.

On the need for adaptive learning in on-demand Deep Brain Stimulation for Movement Disorders

The results presented in this paper indicate that future on-demand Deep Brain Stimulation (DBS) systems for chronic use in patients with movement disorders should continuously and adaptively "learn" in order to maintain high symptom control efficacy. In this work, two machine learning algorithms-Decision Tree and LArge Memory STorage And Retrieval (LAMSTAR) neural network, both with surface Electromyography and accelerometry as control signals-are used to predict onset of tremor after DBS has been switched off in two patients, one suffering from Parkinson's disease and the other from essential tremor. The novelty of this work is that training and testing are done by using different data recorded during sessions at least one week apart. The question is whether the applied algorithms are robust to long-term operation (as patient's control signal may change over time due to disease progression, displacement of the wearable sensor, etc.). Various metrics are used to compare the performance of the proposed approach to those available in the literature, where training and testing are done on data from the same recording session. It is shown that a 100% sensitivity is achieved for training and testing over the same session; however, the sensitivity reduces when tested over a different session. The ratio of predicted stimulation-off time to observed stimulation-off time value is also found to be lower when training and testing on data from separate sessions. These results point to the need of adaptive learning in on-demand DBS systems.

Interleaving Stimulation in Parkinson's Disease, Tremor, and Dystonia

BACKGROUND/AIMS

Interleaving stimulation (ILS) in deep brain stimulation (DBS) provides individualized stimulation of 2 contacts delivered in alternating order. Currently, limited information on the utility of ILS exists. The aims of this study were to determine the practical applications and outcomes of ILS DBS in Parkinson's disease (PD), tremor, and dystonia.

METHODS

We performed a single-center, unblinded, retrospective chart review of all patients with DBS attempted on ILS at our referral center assessing for rationale and outcomes.

RESULTS

Fifty patients (PD, n = 27; tremor, n = 7; dystonia, n = 16 patients) tried ILS for 2 rationales: management of adverse effects (n = 29) and to improve clinical efficacy (n = 21). A total of 19 patients demonstrated improvement with ILS for adverse effect management predominately for the treatment of dyskinesias (n = 12). In the vast majority of dyskinetic patients, a contact added into the rostral zona incerta with ILS was performed. Nine out of 21 patients demonstrated improved clinical efficacy with ILS with all 6 PD patients who tried ILS for this rationale demonstrating benefit.

CONCLUSIONS

In PD, ILS provided benefits for dyskinesias and parkinsonism, with minimal improvement of other adverse effects. In tremor and dystonia, marginal effects in terms of mitigation of adverse effects and improvement of clinical outcomes were evident.

Parkinson's disease: Diagnosis and appreciation of comorbidities

Parkinson's disease (PD) is a complex neuropsychiatric disorder that manifests with a variety of motor and nonmotor symptoms. Its incidence increases with age. It is important for clinicians to be able to distinguish symptoms of aging and other comorbidities from those of PD. The diagnosis of PD has traditionally been rendered using strict criteria that mainly rely on the cardinal motor symptoms of rest tremor, rigidity, and bradykinesia. However, newer diagnostic criteria proposed by the Movement Disorders Society for diagnosis of PD collectively reflect a greater appreciation for the nonmotor symptoms. The treatment of PD remains symptomatic and the most noticeable improvements have been documented in the motor symptoms. Levodopa remains the gold standard for therapy, however there are now many other potential medical and surgical treatment strategies. Nonmotor symptoms have been shown to affect quality of life more than the motor symptoms. There is ongoing research into symptomatic and disease modifying treatments. Given the multisystem involvement in PD, an interdisciplinary patient-centered approach is recommended by most experts. This chapter addresses first the diagnostic approach and the many geriatric considerations. This is followed by a review of the nonmotor symptoms. Finally, a summary of current treatment strategies in PD is presented along with potential treatment complications.

Toward adaptive deep brain stimulation in Parkinson's disease: a review

Clinical deep brain stimulation (DBS) is now regarded as the therapeutic intervention of choice at the advanced stages of Parkinson's disease. However, some major challenges of DBS are stimulation induced side effects and limited pacemaker battery life. Side effects and shortening of pacemaker battery life are mainly as a result of continuous stimulation and poor stimulation focus. These drawbacks can be mitigated using adaptive DBS (aDBS) schemes. Side effects resulting from continuous stimulation can be reduced through adaptive control using closed-loop feedback, while those due to poor stimulation focus can be mitigated through spatial adaptation. Other advantages of aDBS include automatic, rather than manual, initial adjustment and programming, and long-term adjustments to maintain stimulation parameters with changes in patient's condition. Both result in improved efficacy. This review focuses on the major areas that are essential in driving technological advances for the various aDBS schemes. Their challenges, prospects and progress so far are analyzed. In addition, important advances and milestones in state-of-the-art aDBS schemes are highlighted – both for closed-loop adaption and spatial adaption. With perspectives and future potentials of DBS provided at the end.

Advances in closed-loop deep brain stimulation devices

BACKGROUND

Millions of patients around the world are affected by neurological and psychiatric disorders. Deep brain stimulation (DBS) is a device-based therapy that could have fewer side-effects and higher efficiencies in drug-resistant patients compared to other therapeutic options such as pharmacological approaches. Thus far, several efforts have been made to incorporate a feedback loop into DBS devices to make them operate in a closed-loop manner.

METHODS

This paper presents a comprehensive investigation into the existing research-based and commercial closed-loop DBS devices. It describes a brief history of closed-loop DBS techniques, biomarkers and algorithms used for closing the feedback loop, components of the current research-based and commercial closed-loop DBS devices, and advancements and challenges in this field of research. This review also includes a comparison of the closed-loop DBS devices and provides the future directions of this area of research.

RESULTS

Although we are in the early stages of the closed-loop DBS approach, there have been fruitful efforts in design and development of closed-loop DBS devices. To date, only one commercial closed-loop DBS device has been manufactured. However, this system does not have an intelligent and patient dependent control algorithm. A closed-loop DBS device requires a control algorithm to learn and optimize the stimulation parameters according to the brain clinical state.

CONCLUSIONS

The promising clinical effects of open-loop DBS have been demonstrated, indicating DBS as a pioneer technology and treatment option to serve neurological patients. However, like other commercial devices, DBS needs to be automated and modernized.

Neurosurgery of the future: Deep brain stimulations and manipulations

Important advances are afoot in the field of neurosurgery-particularly in the realms of deep brain stimulation (DBS), deep brain manipulation (DBM), and the newly introduced refinement "closed-loop" deep brain stimulation (CLDBS). Use of closed-loop technology will make both DBS and DBM more precise as procedures and will broaden their indications. CLDBS utilizes as feedback a variety of sources of electrophysiological and neurochemical afferent information about the function of the brain structures to be treated or studied. The efferent actions will be either electric, i.e. the classic excitatory or inhibitory ones, or micro-injection of such things as neural proteins and transmitters, neural grafts, implants of pluripotent stem cells or mesenchymal stem cells, and some variants of gene therapy. The pathologies to be treated, beside Parkinson's disease and movement disorders, include repair of neural tissues, neurodegenerative pathologies, psychiatric and behavioral dysfunctions, i.e. schizophrenia in its various guises, bipolar disorders, obesity, anorexia, drug addiction, and alcoholism. The possibility of using these new modalities to treat a number of cognitive dysfunctions is also under consideration. Because the DBS-CLDBS technology brings about a cross-fertilization between scientific investigation and surgical practice, it will also contribute to an enhanced understanding of brain function.

Deep brain stimulation for Parkinson's disease: current status and future outlook

Parkinson's disease is a neurodegenerative condition secondary to loss of dopaminergic neurons in the substantia nigra pars compacta. Surgical therapy serves as an adjunct when unwanted medication side effects become apparent or additional therapy is needed. Deep brain stimulation emerged into the forefront in the 1990s. Studies have demonstrated improvement in all of the cardinal parkinsonian signs with stimulation. Frameless and ‘mini-frame’ stereotactic systems, improved MRI for anatomic visualization, and intraoperative MRI-guided placement are a few of the surgical advances in deep brain stimulation. Other advances include rechargeable pulse generators, voltage- or current-based stimulation, and enhanced abilities to ‘steer’ stimulation. Work is ongoing investigating closed-loop ‘smart’ stimulation in which stimulation is predicated on neuronal feedback.

The adaptive deep brain stimulation challenge

Sub-optimal clinical outcomes of conventional deep brain stimulation (cDBS) in treating Parkinson's Disease (PD) have boosted the development of new solutions to improve DBS therapy. Adaptive DBS (aDBS), consisting of closed-loop, real-time changing of stimulation parameters according to the patient's clinical state, promises to achieve this goal and is attracting increasing interest in overcoming all of the challenges posed by its development and adoption. In the design, implementation, and application of aDBS, the choice of the control variable and of the control algorithm represents the core challenge. The proposed approaches, in fact, differ in the choice of the control variable and control policy, in the system design and its technological limits, in the patient's target symptom, and in the surgical procedure needed. Here, we review the current proposals for aDBS systems, focusing on the choice of the control variable and its advantages and drawbacks, thus providing a general overview of the possible pathways for the clinical translation of aDBS with its benefits, limitations and unsolved issues.

Beta oscillations in freely moving Parkinson's subjects are attenuated during deep brain stimulation

BACKGROUND

Investigations into the effect of deep brain stimulation (DBS) on subthalamic (STN) beta (13-30 Hz) oscillations have been performed in the perioperative period with the subject tethered to equipment. Using an embedded sensing neurostimulator, this study investigated whether beta power was similar in different resting postures and during forward walking in freely moving subjects with Parkinson's disease (PD) and whether STN DBS attenuated beta power in a voltage-dependent manner.

METHODS

Subthalamic local field potentials were recorded from the DBS lead, using a sensing neurostimulator (Activa(®) PC+S, Medtronic, Inc., Food and Drug Administration- Investigational Device Exemption (IDE)-, institutional review board-approved) from 15 PD subjects (30 STNs) off medication during lying, sitting, and standing, during forward walking, and during randomized periods of 140 Hz DBS at 0 V, 1 V, and 2.5/3 V. Continuous video, limb angular velocity, and forearm electromyography recordings were synchronized with neural recordings. Data were parsed to avoid any movement or electrical artifact during resting states.

RESULTS

Beta power was similar during lying, sitting, and standing (P = 0.077, n = 28) and during forward walking compared with the averaged resting state (P = 0.466, n = 24), although akinetic rigid PD subjects tended to exhibit decreased beta power when walking. Deep brain stimulation at 3 V and at 1 V attenuated beta power compared with 0 V (P < 0.003, n = 14), and this was voltage dependent (P < 0.001).

CONCLUSIONS

Beta power was conserved during resting and forward walking states and was attenuated in a voltage-dependent manner during 140-Hz DBS. Phenotype may be an important consideration if this is used for closed-loop DBS.