A model for simulation and patient-specific visualization of the tissue volume of influence during brain microdialysis

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.

Numerical Modeling of Electroosmotic Push-Pull Perfusion and Assessment of Its Application to Quantitative Determination of Enzymatic Activity in the Extracellular Space of Mammalian Tissue

Many sampling methods have been developed to measure the extracellular concentrations of solutes in the extracellular space of mammalian tissue, e.g., brain. However, few have been used to quantitatively study the various processes, such as enzymatic degradation, that determines the fate of these solutes. For a method to be useful in this pursuit, it must be able to (1) perfuse tissue and collect the perfusate for quantitative analysis of the solutes introduced and reaction products produced, (2) control the average residence time of the active solutes, and (3) have the appropriate spatial resolution for the process of interest. Our lab previously developed a perfusion technique based on electroosmosis (EO), called EO push-pull perfusion (EOPPP), that is in principle suitable to meet these needs. However, much like the case for other sampling methods that came before, there are parameters that are needed for quantitative interpretation of data but that cannot be measured easily (or at all). In this paper, we present a robust finite element model that provides a deep understanding of fluid dynamics and mass transport in the EOPPP method, assesses the general applicability of EOPPP to studying enzyme activity in the ECS, and grants a simple approach to data treatment and interpretation to obtain, for example, Vmax and Km for an enzymatic reaction in the extracellular space of the tissue. This model is a valuable tool in optimizing and planning experiments without the need for costly experiments.

Estimation of dynamic metabolic activity in micro-tissue cultures from sensor recordings with an FEM model

We estimated the dynamic cell metabolic activity and the distribution of the pH value and oxygen concentration in tissue samples cultured in vitro by using real-time sensor records and a numerical simulation of the underlying reaction-diffusion processes. As an experimental tissue model, we used chicken spleen slices. A finite element method model representing the biochemical processes and including the relevant sensor data was set up. By fitting the calculated results to the measured data, we derived the spatiotemporal values of the pH value, the oxygen concentration and the absolute metabolic activity (extracellular acidification and oxygen uptake rate) of the samples. Notably, the location of the samples in relation to the sensors has a great influence on the detectable metabolic rates. The long-term vitality of the tissue samples strongly depends on their size. We further discuss the benefits and limitations of the model.

Simulations and visualizations for interpretation of brain microdialysis data during deep brain stimulation

Microdialysis of the basal ganglia was used in parallel to deep brain stimulation (DBS) for patients with Parkinson's disease. The aim of this study was to patient-specifically simulate and visualize the maximum tissue volume of influence (TVI(max)) for each microdialysis catheter and the electric field generated around each DBS electrode. The finite element method (FEM) was used for the simulations. The method allowed mapping of the anatomical origin of the microdialysis data and the electric stimulation for each patient. It was seen that the sampling and stimulation targets differed among the patients, and the results will therefore be used in the future interpretation of the biochemical data.