Comparison of methodologies for modeling directional deep brain stimulation electrodes

Computational models advance deep brain stimulation for Parkinson's disease

Deep brain stimulation(DBS) has become an effective intervention for advanced Parkinson's disease(PD), but the exact mechanism of DBS is still unclear. In this review, we discuss the history of DBS, the anatomy and internal architecture of the basal ganglia (BG), the abnormal pathological changes of the BG in PD, and how computational models can help understand and advance DBS. We also describe two types of models: mathematical theoretical models and clinical predictive models. Mathematical theoretical models simulate neurons or neural networks of BG to shed light on the mechanistic principle underlying DBS, while clinical predictive models focus more on patients' outcomes, helping to adapt treatment plans for each patient and advance novel electrode designs. Finally, we provide insights and an outlook on future technologies.

Model-Based Analysis of Pathway Recruitment During Subthalamic Deep Brain Stimulation

BACKGROUND

Subthalamic deep brain stimulation (DBS) is an established clinical therapy, but an anatomically clear definition of the underlying neural target(s) of the stimulation remains elusive. Patient-specific models of DBS are commonly used tools in the search for stimulation targets, and recent iterations of those models are focused on characterizing the brain connections that are activated by DBS.

OBJECTIVE

The goal of this study was to quantify axonal pathway activation in the subthalamic region from DBS at different electrode locations and stimulation settings.

MATERIALS AND METHODS

We used an anatomically and electrically detailed computational model of subthalamic DBS to generate recruitment curves for eight different axonal pathways of interest, at three generalized DBS electrode locations in the subthalamic nucleus (STN) (ie, central STN, dorsal STN, posterior STN). These simulations were performed with three levels of DBS electrode localization uncertainty (ie, 0.5 mm, 1.0 mm, 1.5 mm).

RESULTS

The recruitment curves highlight the diversity of pathways that are theoretically activated with subthalamic DBS, in addition to the dependence of the stimulation location and parameter settings on the pathway activation estimates. The three generalized DBS locations exhibited distinct pathway recruitment curve profiles, suggesting that each stimulation location would have a different effect on network activity patterns. We also found that the use of anodic stimuli could help limit activation of the internal capsule relative to other pathways. However, incorporating realistic levels of DBS electrode localization uncertainty in the models substantially limits their predictive capabilities.

CONCLUSIONS

Subtle differences in stimulation location and/or parameter settings can impact the collection of pathways that are activated during subthalamic DBS.

Utility of Postoperative Imaging Software for Deep Brain Stimulation Targeting in Patients with Movement Disorders

OBJECTIVE

The objective of this study was to evaluate the accuracy of the SureTune3 postoperative imaging software in determining the location of a deep brain stimulation (DBS) electrode based on clinical outcomes and the adverse effects (AEs) observed.

METHODS

Twenty-six consecutive patients with Parkinson disease (n = 17), essential tremor (n = 8), and dystonia (n = 1) who underwent bilateral DBS surgery (52 electrodes) were included in this study. Presurgical assessments were performed in all patients prior to surgery and at 3 and 6 months after surgery, using quality-of-life and clinical scales in each case. The SureTune3 software was used to evaluate the anatomical positioning of the DBS electrodes.

RESULTS

Following DBS surgery, motor and quality-of-life improvement was observed in all patients. Different AEs were detected in 12 patients, in 10 of whom (83.3%) SureTune3 related the symptoms to the positioning of an electrode. A clinical association was observed with SureTune3 for 48 of 52 (92.3%) electrodes, whereas no association was found between the AEs or clinical outcomes and the SureTune3 reconstructions for 4 of 52 electrodes (7.7%) from 4 different patients. In 2 patients, the contact chosen was modified based on the SureTune3 data, and in 2 cases, the software helped determine that second electrode replacement surgery was necessary.

CONCLUSIONS

The anatomical position of electrodes analyzed with SureTune3 software was strongly correlated with both the AEs and clinical outcomes. Thus, SureTune3 may be useful in clinical practice, and it could help improve stimulation parameters and influence decisions to undertake electrode replacement surgery.