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 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: Synchronization of populations of neurons is a hallmark of several brain diseases. Coordinated reset (CR) stimulation is a model-based stimulation technique which specifically counteracts abnormal synchrony by desynchronization. Electrical CR stimulation, e.g., for the treatment of Parkinson's disease (PD), is administered via depth electrodes. In order to get a deeper understanding of this technique, we extended the top-down approach of previous studies and constructed a large-scale computational model of the respective brain areas. Furthermore, we took into account the spatial anatomical properties of the simulated brain structures and incorporated a detailed numerical representation of 2 · 10(4) simulated neurons. We simulated the subthalamic nucleus (STN) and the globus pallidus externus (GPe). Connections within the STN were governed by spike-timing dependent plasticity (STDP). In this way, we modeled the physiological and pathological activity of the considered brain structures. In particular, we investigated how plasticity could be exploited and how the model could be shifted from strongly synchronized (pathological) activity to strongly desynchronized (healthy) activity of the neuronal populations via CR stimulation of the STN neurons. Furthermore, we investigated the impact of specific stimulation parameters especially the electrode position on the stimulation outcome. Our model provides a step forward toward a biophysically realistic model of the brain areas relevant to the emergence of pathological neuronal activity in PD. Furthermore, our model constitutes a test bench for the optimization of both stimulation parameters and novel electrode geometries for efficient CR stimulation.
    Frontiers in Computational Neuroscience 11/2014; 8:154. DOI:10.3389/fncom.2014.00154 · 2.23 Impact Factor

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