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Design and in vivo evaluation of more efficient and selective deep brain stimulation electrodes
Objective. 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...
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Published in: | Journal of neural engineering 2015-08, Vol.12 (4), p.046030-046030 |
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container_title | Journal of neural engineering |
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creator | Howell, Bryan Huynh, Brian Grill, Warren M |
description | Objective. 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. Approach. 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. Main results. 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. Significance. Model-based design is a powerful tool that can be used to improve the efficiency and selectivity of DBS electrodes. |
doi_str_mv | 10.1088/1741-2560/12/4/046030 |
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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. Approach. 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. Main results. 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. Significance. Model-based design is a powerful tool that can be used to improve the efficiency and selectivity of DBS electrodes.</description><identifier>ISSN: 1741-2560</identifier><identifier>EISSN: 1741-2552</identifier><identifier>DOI: 10.1088/1741-2560/12/4/046030</identifier><identifier>PMID: 26170244</identifier><identifier>CODEN: JNEIEZ</identifier><language>eng</language><publisher>England: IOP Publishing</publisher><subject>Activation ; Animals ; Brain - physiology ; Cats ; Computer Simulation ; Computer-Aided Design ; DBS ; Deep Brain Stimulation - instrumentation ; Design engineering ; Electric Conductivity ; electrical stimulation ; electrode ; Electrodes ; Electrodes, Implanted ; energy efficiency ; Equipment Design ; Equipment Failure Analysis ; Mathematical models ; Models, Neurological ; Reproducibility of Results ; Selectivity ; Sensitivity and Specificity ; Stimulation ; stimulation selectivity</subject><ispartof>Journal of neural engineering, 2015-08, Vol.12 (4), p.046030-046030</ispartof><rights>2015 IOP Publishing Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c471t-f75c626595a47462705642c5170c1ce9f717d1842d6240808b29dc8704154ca83</citedby><cites>FETCH-LOGICAL-c471t-f75c626595a47462705642c5170c1ce9f717d1842d6240808b29dc8704154ca83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,778,782,883,27907,27908</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26170244$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Howell, Bryan</creatorcontrib><creatorcontrib>Huynh, Brian</creatorcontrib><creatorcontrib>Grill, Warren M</creatorcontrib><title>Design and in vivo evaluation of more efficient and selective deep brain stimulation electrodes</title><title>Journal of neural engineering</title><addtitle>JNE</addtitle><addtitle>J. Neural Eng</addtitle><description>Objective. 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. Approach. 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. Main results. 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. Significance. Model-based design is a powerful tool that can be used to improve the efficiency and selectivity of DBS electrodes.</description><subject>Activation</subject><subject>Animals</subject><subject>Brain - physiology</subject><subject>Cats</subject><subject>Computer Simulation</subject><subject>Computer-Aided Design</subject><subject>DBS</subject><subject>Deep Brain Stimulation - instrumentation</subject><subject>Design engineering</subject><subject>Electric Conductivity</subject><subject>electrical stimulation</subject><subject>electrode</subject><subject>Electrodes</subject><subject>Electrodes, Implanted</subject><subject>energy efficiency</subject><subject>Equipment Design</subject><subject>Equipment Failure Analysis</subject><subject>Mathematical models</subject><subject>Models, Neurological</subject><subject>Reproducibility of Results</subject><subject>Selectivity</subject><subject>Sensitivity and Specificity</subject><subject>Stimulation</subject><subject>stimulation selectivity</subject><issn>1741-2560</issn><issn>1741-2552</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNqFkUtv1DAUhS0EoqXwE0DewWaYa8evbJBQeVWqxKZdWx7npniU2MFOIvHvyTTDqEiVurLl-52jc30IecvgIwNjtkwLtuFSwZbxrdiCUFDBM3J-fJf8-emu4Iy8KmUPUDFdw0tyxhXTwIU4J_YLlnAXqYsNDZHOYU4UZ9dNbgwp0tTSPmWk2LbBB4zjPViwQz-GGWmDONBddou0jKGfulV2P8-pwfKavGhdV_DN8bwgt9--3lz-2Fz__H51-fl644Vm46bV0iuuZC2d0EJxDVIJ7uUS0zOPdauZbpgRvFFcgAGz43XjjQbBpPDOVBfk0-o7TLseG79Eza6zQw69y39scsH-P4nhl71LsxWS84rLxeDD0SCn3xOW0faheOw6FzFNxTLDpZAAij-NqtpApaEWCypX1OdUSsb2lIiBPfRoDx3ZQ0eWcSvs2uOie_dwnZPqX3ELwFYgpMHu05Tj8rtPmr5_RLOP-JCyQ9NWfwHeVbP3</recordid><startdate>20150801</startdate><enddate>20150801</enddate><creator>Howell, Bryan</creator><creator>Huynh, Brian</creator><creator>Grill, Warren M</creator><general>IOP Publishing</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>5PM</scope></search><sort><creationdate>20150801</creationdate><title>Design and in vivo evaluation of more efficient and selective deep brain stimulation electrodes</title><author>Howell, Bryan ; Huynh, Brian ; Grill, Warren M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c471t-f75c626595a47462705642c5170c1ce9f717d1842d6240808b29dc8704154ca83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Activation</topic><topic>Animals</topic><topic>Brain - physiology</topic><topic>Cats</topic><topic>Computer Simulation</topic><topic>Computer-Aided Design</topic><topic>DBS</topic><topic>Deep Brain Stimulation - instrumentation</topic><topic>Design engineering</topic><topic>Electric Conductivity</topic><topic>electrical stimulation</topic><topic>electrode</topic><topic>Electrodes</topic><topic>Electrodes, Implanted</topic><topic>energy efficiency</topic><topic>Equipment Design</topic><topic>Equipment Failure Analysis</topic><topic>Mathematical models</topic><topic>Models, Neurological</topic><topic>Reproducibility of Results</topic><topic>Selectivity</topic><topic>Sensitivity and Specificity</topic><topic>Stimulation</topic><topic>stimulation selectivity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Howell, Bryan</creatorcontrib><creatorcontrib>Huynh, Brian</creatorcontrib><creatorcontrib>Grill, Warren M</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of neural engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Howell, Bryan</au><au>Huynh, Brian</au><au>Grill, Warren M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Design and in vivo evaluation of more efficient and selective deep brain stimulation electrodes</atitle><jtitle>Journal of neural engineering</jtitle><stitle>JNE</stitle><addtitle>J. Neural Eng</addtitle><date>2015-08-01</date><risdate>2015</risdate><volume>12</volume><issue>4</issue><spage>046030</spage><epage>046030</epage><pages>046030-046030</pages><issn>1741-2560</issn><eissn>1741-2552</eissn><coden>JNEIEZ</coden><abstract>Objective. 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. Approach. 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. Main results. 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. Significance. Model-based design is a powerful tool that can be used to improve the efficiency and selectivity of DBS electrodes.</abstract><cop>England</cop><pub>IOP Publishing</pub><pmid>26170244</pmid><doi>10.1088/1741-2560/12/4/046030</doi><tpages>17</tpages></addata></record> |
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subjects | Activation Animals Brain - physiology Cats Computer Simulation Computer-Aided Design DBS Deep Brain Stimulation - instrumentation Design engineering Electric Conductivity electrical stimulation electrode Electrodes Electrodes, Implanted energy efficiency Equipment Design Equipment Failure Analysis Mathematical models Models, Neurological Reproducibility of Results Selectivity Sensitivity and Specificity Stimulation stimulation selectivity |
title | Design and in vivo evaluation of more efficient and selective deep brain stimulation electrodes |
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