ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2020
Effect of Biophysical Model Complexity on Predictions of Volume of Tissue Activated (VTA) during Deep Brain Stimulation.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Variations in Determining Actual Orientations of Segmented Deep Brain Stimulation Leads Using the DiODe Algorithm: A Retrospective Study Across Different Lead Designs and Medical Institutions.Stereotactic and functional neurosurgery · 2023Article
- Patient-specific modeling of the volume of tissue activated (VTA) is associated with clinical outcome of DBS in patients with an obsessive-compulsive disorder.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2021Article
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Authors and funding
5 authors.
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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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Registered trials
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