Computational Modeling of Deep Brain Stimulation in the Globus Pallidus Internus

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Computational Modeling of Deep Brain Stimulation in the Globus Pallidus Internus

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Neuromodulation is the functional modification of neural structures through the use of electrical stimulation1. Clinical applications include deep brain stimulation (DBS) for the treatment of neurological movement disorders such as Parkinson’s disease and essential tremor. The general procedure involves placing small electrodes in regions of the brain exhibiting pathological activity and then stimulating those regions with continuous pulses of electricity. Treatment outcome is strongly dependent on the precise placement of the electrodes and subsequent adjustment of the stimulation settings to fine-tune the therapy. DBS is now being used for treating dystonic movement disorders, where sustained muscle contractions cause twisting and repetitive movements and/or abnormal postures. One target of DBS for dystonia is the posteroventral globus pallidus internus (GPi). Stimulation of the GPi has yielded promising results for people with dystonia; however, specific stimulation settings providing maximum GPi activation and having minimal side-effects have yet to be determined. Here we use computational models to show how altering parameters such as electrode configuration, DBS lead placement and orientation, and stimulation voltage affects GPi modulation and activation of the cortical spinal tract (CST), the side-effect pathway. In one model, the electrode configuration of the lead was varied. Another model had the DBS lead translated 1 mm medial, lateral, anterior, and posterior from its original position to make predictions of possible motor side-effects in a non-human primate animal model. Such models can provide a framework for neurosurgeons and neurologists to improve current steering techniques that will optimize treatment outcome.


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Malaga, Karlo. (2012). Computational Modeling of Deep Brain Stimulation in the Globus Pallidus Internus. Retrieved from the University Digital Conservancy,

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