Calculating deep brain stimulation amplitudes and power consumption by constrained optimization.

OBJECTIVE: Deep brain stimulation (DBS) consists of delivering electrical stimuli to a brain target via an implanted lead to treat neurological and psychiatric conditions. Individualized stimulation is vital to ensure therapeutic results, since DBS may otherwise become ineffective or cause undesirable side effects. Since the DBS pulse generator is battery-driven, power consumption incurred by the stimulation is important. In this study, target coverage and power consumption are compared over a patient population for clinical and model-based patient-specific settings calculated by constrained optimization. APPROACH: Brain models for five patients undergoing bilateral DBS were built. Mathematical optimization of activated tissue volume was utilized to calculate stimuli amplitudes, with and without specifying the volumes, where stimulation was not allowed to avoid side effects. Power consumption was estimated using measured impedance values and battery life under both clinical and optimized settings. RESULTS: It was observed that clinical settings were generally less aggressive than the ones suggested by unconstrained model-based optimization, especially under asymmetrical stimulation. The DBS settings satisfying the constraints were close to the clinical values. SIGNIFICANCE: The use of mathematical models to suggest optimal patient-specific DBS settings that observe technological and safety constraints can save time in clinical practice. It appears though that the considered safety constraints based on brain anatomy depend on the patient and further research into it is needed. This work highlights the need of specifying the brain volumes to be avoided by stimulation while optimizing the DBS amplitude, in contrast to minimizing general stimuli overspill, and applies the technique to a cohort of patients. It also stresses the importance of considering power consumption in DBS optimization, since it increases with the square of the stimuli amplitude and also critically affects battery life through pulse frequency and duty cycle.

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.

Target coverage and selectivity in field steering brain stimulation.

Deep Brain Stimulation (DBS) is an established treatment in Parkinson's 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.