Abstract
Deep brain stimulation (DBS) has evolved to an important treatment for several drug-resistant neurological and psychiatric disorders, such as epilepsy, Parkinson's disease, essential tremor and dystonia. Despite general effectiveness of DBS, however, its mechanisms of action are not completely understood. Simulations are commonly used to predict the volume of tissue activated (VTA) around DBS electrodes, which in turn helps interpreting clinical outcomes and understand therapeutic mechanisms. Computational models are commonly used to visualize the extend of volume of activated tissue (VTA) for different stimulation schemes, which in turn helps interpreting and understanding the outcomes. The degree of model complexity, however, can affect the predicted VTA. In this work we investigate the effect of volume conductor model complexity on the predicted VTA, when the VTA is estimated from activation function field metrics. Our results can help clinicians to decide what level of model complexity is suitable for their specific need.
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