Ruben Cubo

Target coverage and selectivity in field steering brain stimulation

Deep Brain Stimulation (DBS) is an established treatment in Parkinsons Disease. The target area is defined based on the state and brain anatomy of the patient. The stimulation delivered via state-of-the-art DBS leads that are currently in clinical use is difficult to individualize to the patient particularities. Furthermore, the electric field generated by such a lead has a limited selectivity, resulting in stimulation of areas adjacent to the target and thus causing undesirable side effects. The goal of this study is, using actual clinical data, to compare in silico the stimulation performance of a symmetrical generic lead to a more versatile and adaptable one allowing, in particular, for asymmetric stimulation. The fraction of the volume of activated tissue in the target area and the fraction of the stimulation field that spreads beyond it are computed for a clinical data set of patients in order to quantify the lead performance. The obtained results suggest that using more versatile DBS leads might reduce the stimulation area beyond the target and thus lessen side effects for the same achieved therapeutical effect.

Model-Based Optimization of Individualized Deep Brain Stimulation Therapy

Deep learning has become a major topic of interest as systems grow increasingly complex. This paper presents a survey of mathematical models for approaches in deep brain stimulation, a key enabling technique for deep learning.

Electric field modeling and spatial control in Deep Brain Stimulation

Deep Brain Stimulation (DBS) is an established treatment, in e.g. Parkinsons Disease, whose underlying biological mechanisms are unknown. In DBS, electrical stimulation is delivered through electrodes surgically implanted into certain regions of the brain of the patient. Mathematical models aiming at a better understanding of DBS and optimization of its therapeutical effect through the simulation of the electrical field propagating in the brain tissue have been developed in the past decade. The contribution of the present study is twofold: First, an analytical approximation of the electric field produced by an emitting contact is suggested and compared to the numerical solution given by a Finite Element Method (FEM) solver. Second, the optimal stimulation settings are evaluated by fitting the field distribution to a target one to control the spread of the stimulation. Optimization results are compared to those of a geometric approach, maximizing the intersection between the target and the activated volume in the brain tissue and reducing the stimulated area beyond said target. Both methods exhibit similar performance with respect to the optimal stimuli, with the electric field control approach being faster and more versatile.

Deep Brain Stimulation therapies: A control-engineering perspective

Deep Brain Stimulation (DBS) is an established therapy for treating e.g. Parkinsons disease, essential tremor, as well as epilepsy. In DBS, chronic pulsatile electrical stimulation is administered to a certain target area of the brain through a surgically implanted lead. The stimuli parameters have to be properly tuned in order to achieve therapeutical effect that in most cases is alleviation of motor symptoms. Tuning of DBS currently is a tedious task since it is performed manually by medical personnel in a trial-and-error manner. It can be dramatically improved and expedited by means of recently developed mathematical models together with control and estimation technology. This paper presents a control engineering perspective on DBS, viewing it as a control system for minimizing the severity of the symptoms through coordinated manipulation of the stimuli parameters. The DBS model structure comprises a stimuli model, an activation model, and a symptoms model. Each of those is individualized from patient data obtained through medical imaging, electrical measurements, and objective symptom quantification. The proposed approach is illustrated by simulation and clinical data from an individualized DBS model being developed by the authors.

Accuracy of the Finite Element Method in Deep Brain Stimulation Modelling

Deep Brain Stimulation (DBS) is a widely established treatment for Parkinsons Disease where electrical pulsatile stimulation is delivered to a target area in the brain by means of an implanted electrode. To understand better how the stimuli propagate through the brain of the patient, mathematical models of various levels of sophistication have been developed using Finite Element Methods (FEM). However, the accuracy of these models, aiming mostly at stimuli tuning in and individualization of DBS systems, is still unclear. One complication is posed by the interface between the encapsulation tissue surrounding the electrode lead and the bulk brain tissue. It is usually modelled as a discontinuity in the electric conductivity, which translates into a discontinuity of the first derivative of the electric potential. The goal of this study is to analyze the accuracy of the solution yielded by the FEM tool using different interface models between the two media, comparing it to the one predicted in the literature. The obtained results suggest that, although a discontinuous conductivity will not introduce any extra numerical inaccuracies, exchanging the interface with a discontinuous conductivity for a smooth transition might yield more accurate model solutions.

Optimization-Based Contact Fault Alleviation in Deep Brain Stimulation Leads

Deep brain stimulation (DBS) is a neurosurgical treatment in, e.g., Parkinson’s Disease. Electrical stimulation in DBS is delivered to a certain target through electrodes implanted into the brain. Recent developments aiming at better stimulation target coverage and lesser side effects have led to an increase in the number of contacts in a DBS lead as well as higher hardware complexity. This paper proposes an optimization-based approach to alleviation of the fault impact on the resulting therapeutical effect in field steering DBS. Faulty contacts could be an issue given recent trends of increasing number of contacts in DBS leads. Hence, a fault detection/alleviation scheme, such as the one proposed in this paper, is necessary ensure resilience in the chronic stimulation. Two alternatives are considered and compared with the stimulation prior to the fault: one using higher amplitudes on the remaining contacts and another with alleviating contacts in the neighborhood of the faulty one. Satisfactory compensation for a faulty contact can be achieved in both ways. However, to designate alleviating contacts, a model-based optimization procedure is necessary. Results suggest that stimulating with more contacts yields configurations that are more robust to contact faults, though with reduced selectivity.

Semi-Individualized electrical models in deep brain stimulation: A variability analysis

Deep Brain Stimulation (DBS) is a well-established treatment in neurodegenerative diseases, e.g. Parkinsons Disease. It consists of delivering electrical stimuli to a target in the brain via a chronically implanted lead. To expedite the tuning of DBS stimuli to best therapeutical effect, mathematical models have been developed during recent years. The electric field produced by the stimuli in the brain for a given lead position is evaluated by numerically solving a Partial Differential Equation with the medium conductivity as a parameter. The latter is patient- and target-specific but difficult to measure in vivo. Estimating brain tissue conductivity through medical imaging is feasible but time consuming due to registration, segmentation and post-processing. On the other hand, brain atlases are readily available and processed. This study analyzes how alternations in the conductivity due to inter-patient variability or lead position uncertainties affect both the stimulation shape and the activation of a given target. Results suggest that stimulation shapes are similar, with a Dices Coefficient between 93.2 and 98.8%, with a higher similarity at lower depths. On the other hand, activation shows a significant variation of 17 percentage points, with most of it being at deeper positions as well. It is concluded that, as long as the lead is not too deep, atlases can be used for conductivity maps with acceptable accuracy instead of fully individualized though medical imaging models.