Patient-specific models of deep brain stimulation: Influence of field model complexity on neural activation predictions

Department of Biomedical Engineering, Cleveland Clinic Foundation, Cleveland, OH 44195, USA.
Brain Stimulation (Impact Factor: 4.4). 04/2010; 3(2):65-7. DOI: 10.1016/j.brs.2010.01.003
Source: PubMed


Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has become the surgical therapy of choice for medically intractable Parkinson's disease. However, quantitative understanding of the interaction between the electric field generated by DBS and the underlying neural tissue is limited. Recently, computational models of varying levels of complexity have been used to study the neural response to DBS. The goal of this study was to evaluate the quantitative impact of incrementally incorporating increasing levels of complexity into computer models of STN DBS. Our analysis focused on the direct activation of experimentally measureable fiber pathways within the internal capsule (IC). Our model system was customized to an STN DBS patient and stimulation thresholds for activation of IC axons were calculated with electric field models that ranged from an electrostatic, homogenous, isotropic model to one that explicitly incorporated the voltage-drop and capacitance of the electrode-electrolyte interface, tissue encapsulation of the electrode, and diffusion-tensor based 3D tissue anisotropy and inhomogeneity. The model predictions were compared to experimental IC activation defined from electromyographic (EMG) recordings from eight different muscle groups in the contralateral arm and leg of the STN DBS patient. Coupled evaluation of the model and experimental data showed that the most realistic predictions of axonal thresholds were achieved with the most detailed model. Furthermore, the more simplistic neurostimulation models substantially overestimated the spatial extent of neural activation.

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    • "To the best of our knowledge, our study is the first to reveal a continuous decrease of therapeutic impedance values from 1036U to 786U within 10 years of STN-DBS, and it corresponds well to previous findings of a continuous decline of impedance over time [8]. As the stimulation settings remained constant over time and therefore cannot explain our findings, progressive neurodegeneration may be hypothesized as a possible explanation, because an associated increase of mean diffusivity and isotropy [9] would directly affect brain conductivity and hence impedance measurements [7]. Clinicians should be aware of such development , since a decline of impedance as observed in our study goes along with a substantial enlargement of the local volume of tissue activated, as long as voltage-controlled stimulation is applied. "
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    ABSTRACT: This study was conducted to better understand the development of clinical efficacy and impedance levels in the long-term course of deep brain stimulation (DBS) of the subthalamic nucleus (STN) in Parkinson's disease (PD). In this retrospective study of twenty PD patients, the motor part of the Unified Parkinson's Disease Rating Scale was periodically assessed i) after withdrawal of medication and inactivated stimulation, ii) after withdrawal of medication with activated stimulation and iii) after challenge with l-Dopa during activated stimulation up to 13 years after surgery. STN-DBS with or without medication significantly improved motor function up to 13 years after surgery. The contribution of axial symptoms increased over time. While the stimulation parameters were kept constant, the therapeutic impedances progressively declined. STN-DBS in PD remains effective in the long-term course of the disease. Constant current stimulation might be preferable over voltage-controlled stimulation, as it would alleviate the impact of impedance changes on the volume of tissue activated. Copyright © 2015. Published by Elsevier Ltd.
    No preview · Article · Jul 2015 · Parkinsonism & Related Disorders
    • "The simplified model, however, did not reproduce all of the experimental findings. It is possible that discrepancies between the model and experiments may be due to anisotropy and heterogeneity in the conductivity of brain tissue and/or the geometry of the target neural elements (figure 9), and future computational models should account for these features when designing electrodes for specific DBS applications (Butson et al 2007, Chaturvedi et al 2010, Frankemolle et al 2010). "
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    ABSTRACT: Deep brain stimulation (DBS) is an effective treatment for movement disorders and a promising therapy for treating epilepsy and psychiatric disorders. Despite its clinical success, the efficiency and selectivity of DBS can be improved. Our objective was to design electrode geometries that increased the efficiency and selectivity of DBS. We coupled computational models of electrodes in brain tissue with cable models of axons of passage (AOPs), terminating axons (TAs), and local neurons (LNs); we used engineering optimization to design electrodes for stimulating these neural elements; and the model predictions were tested in vivo. Compared with the standard electrode used in the Medtronic Model 3387 and 3389 arrays, model-optimized electrodes consumed 45-84% less power. Similar gains in selectivity were evident with the optimized electrodes: 50% of parallel AOPs could be activated while reducing activation of perpendicular AOPs from 44 to 48% with the standard electrode to 0-14% with bipolar designs; 50% of perpendicular AOPs could be activated while reducing activation of parallel AOPs from 53 to 55% with the standard electrode to 1-5% with an array of cathodes; and, 50% of TAs could be activated while reducing activation of AOPs from 43 to 100% with the standard electrode to 2-15% with a distal anode. In vivo, both the geometry and polarity of the electrode had a profound impact on the efficiency and selectivity of stimulation. Model-based design is a powerful tool that can be used to improve the efficiency and selectivity of DBS electrodes.
    No preview · Article · Jul 2015 · Journal of Neural Engineering
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    • "Model predictions of the voltage distribution in the brain during DBS were validated by in vivo recordings in a non-human primate [8]. Model predictions of the VTA/SFM were indirectly validated by detecting stimulation-induced side effects such as STN DBS-induced activation of the corticospinal tract [3] [5]. These experiments and others provided evidence to demonstrate the accuracy of the modeling approach. "

    Full-text · Article · Jun 2015 · Brain Stimulation
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