Adaptive Estimation of the Neural Activation Extent in Computational Volume Conductor Models of Deep Brain Stimulation

Deep Brain Stimulation: Emerging Tools for Simulation, Data Analysis, and Visualization

Deep brain stimulation (DBS) is a well-established neurosurgical procedure for movement disorders that is also being explored for treatment-resistant psychiatric conditions. This review highlights important consideration for DBS simulation and data analysis. The literature on DBS has expanded considerably in recent years, and this article aims to identify important trends in the field. During DBS planning, surgery, and follow up sessions, several large data sets are created for each patient, and it becomes clear that any group analysis of such data is a big data analysis problem and has to be handled with care. The aim of this review is to provide an update and overview from a neuroengineering perspective of the current DBS techniques, technical aids, and emerging tools with the focus on patient-specific electric field (EF) simulations, group analysis, and visualization in the DBS domain. Examples are given from the state-of-the-art literature including our own research. This work reviews different analysis methods for EF simulations, tractography, deep brain anatomical templates, and group analysis. Our analysis highlights that group analysis in DBS is a complex multi-level problem and selected parameters will highly influence the result. DBS analysis can only provide clinically relevant information if the EF simulations, tractography results, and derived brain atlases are based on as much patient-specific data as possible. A trend in DBS research is creation of more advanced and intuitive visualization of the complex analysis results suitable for the clinical environment.

Experimental deep brain stimulation in rodent models of movement disorders

Deep brain stimulation (DBS) is the preferred treatment for therapy-resistant movement disorders such as dystonia and Parkinson's disease (PD), mostly in advanced disease stages. Although DBS is already in clinical use for ~30 years and has improved patients' quality of life dramatically, there is still limited understanding of the underlying mechanisms of action. Rodent models of PD and dystonia are essential tools to elucidate the mode of action of DBS on behavioral and multiscale neurobiological levels. Advances have been made in identifying DBS effects on the central motor network, neuroprotection and neuroinflammation in DBS studies of PD rodent models. The phenotypic dt^sz mutant hamster and the transgenic DYT-TOR1A (ΔETorA) rat proved as valuable models of dystonia for preclinical DBS research. In addition, continuous refinements of rodent DBS technologies are ongoing and have contributed to improvement of experimental quality. We here review the currently existing literature on experimental DBS in PD and dystonia models regarding the choice of models, experimental design, neurobiological readouts, as well as methodological implications. Moreover, we provide an overview of the technical stage of existing DBS devices for use in rodent studies.

Endovascular deep brain stimulation: Investigating the relationship between vascular structures and deep brain stimulation targets

BACKGROUND

Endovascular delivery of current using 'stentrodes' - electrode bearing stents - constitutes a potential alternative to conventional deep brain stimulation (DBS). The precise neuroanatomical relationships between DBS targets and the vascular system, however, are poorly characterized to date.

OBJECTIVE

To establish the relationships between cerebrovascular system and DBS targets and investigate the feasibility of endovascular stimulation as an alternative to DBS.

METHODS

Neuroanatomical targets as employed during deep brain stimulation (anterior limb of the internal capsule, dentatorubrothalamic tract, fornix, globus pallidus pars interna, medial forebrain bundle, nucleus accumbens, pedunculopontine nucleus, subcallosal cingulate cortex, subthalamic nucleus, and ventral intermediate nucleus) were superimposed onto probabilistic vascular atlases obtained from 42 healthy individuals. Euclidian distances between targets and associated vessels were measured. To determine the electrical currents necessary to encapsulate the predefined neurosurgical targets and identify potentially side-effect inducing substrates, a preliminary volume of tissue activated (VTA) analysis was performed.

RESULTS

Six out of ten DBS targets were deemed suitable for endovascular stimulation: medial forebrain bundle (vascular site: P1 segment of posterior cerebral artery), nucleus accumbens (vascular site: A1 segment of anterior cerebral artery), dentatorubrothalamic tract (vascular site: s2 segment of superior cerebellar artery), fornix (vascular site: internal cerebral vein), pedunculopontine nucleus (vascular site: lateral mesencephalic vein), and subcallosal cingulate cortex (vascular site: A2 segment of anterior cerebral artery). While VTAs effectively encapsulated mfb and NA at current thresholds of 3.5 V and 4.5 V respectively, incremental amplitude increases were required to effectively cover fornix, PPN and SCC target (mean voltage: 8.2 ± 4.8 V, range: 3.0-17.0 V). The side-effect profile associated with endovascular stimulation seems to be comparable to conventional lead implantation. Tailoring of targets towards vascular sites, however, may allow to reduce adverse effects, while maintaining the efficacy of neural entrainment within the target tissue.

CONCLUSIONS

While several challenges remain at present, endovascular stimulation of select DBS targets seems feasible offering novel and exciting opportunities in the neuromodulation armamentarium.

OSS-DBS: Open-source simulation platform for deep brain stimulation with a comprehensive automated modeling

In this study, we propose a new open-source simulation platform that comprises computer-aided design and computer-aided engineering tools for highly automated evaluation of electric field distribution and neural activation during Deep Brain Stimulation (DBS). It will be shown how a Volume Conductor Model (VCM) is constructed and examined using Python-controlled algorithms for generation, discretization and adaptive mesh refinement of the computational domain, as well as for incorporation of heterogeneous and anisotropic properties of the tissue and allocation of neuron models. The utilization of the platform is facilitated by a collection of predefined input setups and quick visualization routines. The accuracy of a VCM, created and optimized by the platform, was estimated by comparison with a commercial software. The results demonstrate no significant deviation between the models in the electric potential distribution. A qualitative estimation of different physics for the VCM shows an agreement with previous computational studies. The proposed computational platform is suitable for an accurate estimation of electric fields during DBS in scientific modeling studies. In future, we intend to acquire SDA and EMA approval. Successful incorporation of open-source software, controlled by in-house developed algorithms, provides a highly automated solution. The platform allows for optimization and uncertainty quantification (UQ) studies, while employment of the open-source software facilitates accessibility and reproducibility of simulations.