Design and in vivo evaluation of more efficient and selective deep brain stimulation electrodes.

Published

Journal Article

Deep brain stimulation (DBS) is an effective treatment for movement disorders and a promising therapy for treating epilepsy and psychiatric disorders. Despite its clinical success, the efficiency and selectivity of DBS can be improved. Our objective was to design electrode geometries that increased the efficiency and selectivity of DBS.We coupled computational models of electrodes in brain tissue with cable models of axons of passage (AOPs), terminating axons (TAs), and local neurons (LNs); we used engineering optimization to design electrodes for stimulating these neural elements; and the model predictions were tested in vivo.Compared with the standard electrode used in the Medtronic Model 3387 and 3389 arrays, model-optimized electrodes consumed 45-84% less power. Similar gains in selectivity were evident with the optimized electrodes: 50% of parallel AOPs could be activated while reducing activation of perpendicular AOPs from 44 to 48% with the standard electrode to 0-14% with bipolar designs; 50% of perpendicular AOPs could be activated while reducing activation of parallel AOPs from 53 to 55% with the standard electrode to 1-5% with an array of cathodes; and, 50% of TAs could be activated while reducing activation of AOPs from 43 to 100% with the standard electrode to 2-15% with a distal anode. In vivo, both the geometry and polarity of the electrode had a profound impact on the efficiency and selectivity of stimulation.Model-based design is a powerful tool that can be used to improve the efficiency and selectivity of DBS electrodes.

Full Text

Duke Authors

Cited Authors

  • Howell, B; Huynh, B; Grill, WM

Published Date

  • August 2015

Published In

Volume / Issue

  • 12 / 4

Start / End Page

  • 046030 -

PubMed ID

  • 26170244

Pubmed Central ID

  • 26170244

Electronic International Standard Serial Number (EISSN)

  • 1741-2552

International Standard Serial Number (ISSN)

  • 1741-2560

Digital Object Identifier (DOI)

  • 10.1088/1741-2560/12/4/046030

Language

  • eng