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Deep brain stimulation (DBS) is a neuromodulatory therapy that has been FDA approved for the treatment of various disorders, including but not limited to, movement disorders (e.g., Parkinson's disease and essential tremor), epilepsy, and obsessive-compulsive disorder. Computational methods for estimating the volume of tissue activated (VTA), coupled with brain imaging techniques, form the basis of models that are being generated from retrospective clinical studies for predicting DBS patient outcomes. For instance, VTA models are used to generate target-and network-based probabilistic stimulation maps that play a crucial role in predicting DBS treatment outcomes. This review defines the methods for calculation of tissue activation (or modulation) including ones that use heuristic and clinically derived estimates and more computationally involved ones that rely on finite-element methods and biophysical axon models. We define model parameters and provide a comparison of commercial, open-source, and academic simulation platforms available for integrated neuroimaging and neural activation prediction. In addition, we review clinical studies that use these modeling methods as a function of disease. By describing the tissue-activation modeling methods and highlighting their application in clinical studies, we provide the neural engineering and clinical neuromodulation communities with perspectives that may influence the adoption of modeling methods for future DBS studies.
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The use of metallic nanostructures in the fabrication of bioelectrodes (e.g., neural implants) is gaining attention nowadays. Nanostructures provide increased surface area that might benefit the performance of bioelectrodes. However, there is a need for comprehensive studies that assess electrochemical performance of nanostructured surfaces in physiological and relevant working conditions. Here, we introduce a versatile scalable fabrication method based on magnetron sputtering to develop analogous metallic nanocolumnar structures (NCs) and thin films (TFs) from Ti, Au, and Pt. We show that NCs contribute significantly to reduce the impedance of metallic surfaces. Charge storage capacity of Pt NCs is remarkably higher than that of Pt TFs and that of the other metals in both morphologies. Circuit simulations of the electrode/electrolyte interface show that the signal delivered in voltage-controlled systems is less filtered when nanocolumns are used. In a current-controlled system, simulation shows that NCs provide safer stimulation conditions compared to TFs. We have assessed the durability of NCs and TFs for potential use in vivo by reactive accelerated aging test, mimicking one-year in vivo implantation. Although each metal/morphology reveals a unique response to aging, NCs show overall more stable electrochemical properties compared to TFs in spite of their porous structure.
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Introduction: Essential tremor (ET) is a common movement disorder with an estimated prevalence of 0.9% worldwide. Deep brain stimulation (DBS) is an established therapy for medication refractory and debilitating tremor. With the arrival of next generation technology, the implementation and delivery of DBS has been rapidly evolving. This review will highlight the current applications and constraints for DBS in ET.Areas covered: The mechanism of action, targets for neuromodulation, next generation guidance techniques, symptom-specific applications, and long-term efficacy will be reviewed.Expert opinion: The posterior subthalamic area and zona incerta are alternative targets to thalamic DBS in ET. However, they may be associated with additional stimulation-induced side effects. Novel stimulation paradigms and segmented electrodes provide innovative approaches to DBS programming and stimulation-induced side effects.
