Article

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: 5.43). 04/2010; 3(2):65-7. DOI: 10.1016/j.brs.2010.01.003
Source: PubMed

ABSTRACT 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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    ABSTRACT: Deep brain stimulation (DBS) is an established therapy for the management of advanced Parkinson's disease (PD). However, the coupled adjustment of pharmacologic therapy and stimulation parameter settings is a time-consuming process and treatment outcomes are not always optimal. In this study, we develop a linear function that relates the DBS parameters, the levodopa dosage, and patient-specific preoperative clinical data with the actual treatment motor outcomes. To this end, we incorporate image-based patient-specific computer models of the volume of tissue activated by DBS in a multilinear regression analysis (6 PD patients; 60 follow up visits). The resulting predictor function was highly correlated with the actual motor outcomes (r = 0.76; p < 0.05). These results demonstrate that the outcomes of a combined pharmacologic-DBS therapy can be predicted and may facilitate patient-specific treatment optimization for maximal benefits and minimal adverse effects.
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    ABSTRACT: 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.
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    ABSTRACT: Cortical modulation is likely to be involved in the various therapeutic effects of deep brain stimulation (DBS). However, it is currently difficult to predict the changes of cortical modulation during clinical adjustment of DBS. Therefore, we present a novel quantitative approach to estimate anatomical regions of DBS-evoked cortical modulation. Four different models of the subthalamic nucleus (STN) DBS were created to represent variable electrode placements (model I: dorsal border of the posterolateral STN; model II: central posterolateral STN; model III: central anteromedial STN; model IV: dorsal border of the anteromedial STN). Axonal fibers of passage near each electrode location were reconstructed using probabilistic tractography and modeled using multi-compartment cable models. Stimulation-evoked activation of local axon fibers and corresponding cortical projections were modeled and quantified. Stimulation at the border of the STN (models I and IV) led to a higher degree of fiber activation and associated cortical modulation than stimulation deeply inside the STN (models II and III). A posterolateral target (models I and II) was highly connected to cortical areas representing motor function. Additionally, model I was also associated with strong activation of fibers projecting to the cerebellum. Finally, models III and IV showed a dorsoventral difference of preferentially targeted prefrontal areas (models III: middle frontal gyrus; model IV: inferior frontal gyrus). The method described herein allows characterization of cortical modulation across different electrode placements and stimulation parameters. Furthermore, knowledge of anatomical distribution of stimulation-evoked activation targeting cortical regions may help predict efficacy and potential side effects, and therefore can be used to improve the therapeutic effectiveness of individual adjustments in DBS patients.
    Frontiers in Neuroscience 02/2015; 9:28. DOI:10.3389/fnins.2015.00028

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