TY - GEN
T1 - Effect of Biophysical Model Complexity on Predictions of Volume of Tissue Activated (VTA) during Deep Brain Stimulation
AU - Jiang, Fuchang
AU - Nguyen, Bach T.
AU - Elahi, Behzad
AU - Pilitsis, Julie
AU - Golestanirad, Laleh
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - 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.
AB - 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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U2 - 10.1109/EMBC44109.2020.9175300
DO - 10.1109/EMBC44109.2020.9175300
M3 - Conference contribution
C2 - 33018788
AN - SCOPUS:85091034697
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3629
EP - 3633
BT - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Y2 - 20 July 2020 through 24 July 2020
ER -