Improvement of Pyramidal Tract Side Effect Prediction Using a Data-Driven Method in Subthalamic Stimulation
Identifieur interne : 000054 ( Main/Exploration ); précédent : 000053; suivant : 000055Improvement of Pyramidal Tract Side Effect Prediction Using a Data-Driven Method in Subthalamic Stimulation
Auteurs : Clement Baumgarten ; Yulong Zhao [France] ; Paul Sauleau [France] ; Cécile Malrain [France] ; Pierre Jannin [France] ; Claire Haegelen [France]Source :
Abstract
OBJECTIVE: Subthalamic nucleus deep brain stimulation (STN DBS) is limited by the occurrence of pyramidal tract side effect (PTSE) induced by electrical activation of the pyramidal tract. Predictive models are needed to assist the surgeon during the electrode trajectory pre-planning. The objective of the study was to compare two methods of PTSE prediction based on clinical assessment of PTSE induced by STN DBS in patients with Parkinson's disease. METHODS: Two clinicians assessed PTSE postoperatively in 20 patients implanted for at least 3 months in the STN. The resulting dataset of electro-clinical tests was used to evaluate two methods of PTSE prediction. The first method was based on the volume of tissue activated (VTA) modeling and the second one was a data-driven based method named PyMAN (Pyramidal tract side effect Model based on Artificial Neural network) developed in our laboratory. This method was based on the nonlinear correlation between the PTSE current threshold and the 3D electrode coordinates. PTSE prediction from both methods was compared using Mann-Whitney U test. RESULTS: 1696 electro-clinical tests were used to design and compare the two methods. Sensitivity, specificity, positive and negative predictive values were significantly higher with the PyMAN method than with the VTA-based method (P \textless0.05). CONCLUSION: The PyMAN method was more effective than the VTA-based method to predict PTSE. SIGNIFICANCE: This data-driven tool could help the neurosurgeon in predicting adverse side-effects induced by DBS during the electrode trajectory pre-planning.
Url:
DOI: 10.1109/TBME.2016.2638018
Affiliations:
- France
- Pays de la Loire, Région Bretagne
- Nantes, Rennes
- Université de Nantes, Université de Rennes 1, Université européenne de Bretagne
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CS 46510 - 35065 Rennes cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-rennes1.fr/</ref></desc></org></tutelle><tutelle active="#struct-300129" type="direct"><org type="institution" xml:id="struct-300129" status="VALID"><orgName>Hôpital Pontchaillou</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-301327" type="direct"><org type="institution" xml:id="struct-301327" status="VALID"><orgName>CHU Pontchaillou [Rennes]</orgName><desc><address><addrLine>2 Rue Henri le Guilloux, 35000 Rennes</addrLine><country key="FR"></country></address><ref type="url">http://www.chu-rennes.fr/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Rennes</settlement><region type="region" nuts="2">Région Bretagne</region></placeName><orgName type="university">Université de Rennes 1</orgName><orgName type="institution" wicri:auto="newGroup">Université européenne de Bretagne</orgName></affiliation></author></analytic><idno type="DOI">10.1109/TBME.2016.2638018</idno></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">OBJECTIVE: Subthalamic nucleus deep brain stimulation (STN DBS) is limited by the occurrence of pyramidal tract side effect (PTSE) induced by electrical activation of the pyramidal tract. Predictive models are needed to assist the surgeon during the electrode trajectory pre-planning. The objective of the study was to compare two methods of PTSE prediction based on clinical assessment of PTSE induced by STN DBS in patients with Parkinson's disease. METHODS: Two clinicians assessed PTSE postoperatively in 20 patients implanted for at least 3 months in the STN. The resulting dataset of electro-clinical tests was used to evaluate two methods of PTSE prediction. The first method was based on the volume of tissue activated (VTA) modeling and the second one was a data-driven based method named PyMAN (Pyramidal tract side effect Model based on Artificial Neural network) developed in our laboratory. This method was based on the nonlinear correlation between the PTSE current threshold and the 3D electrode coordinates. PTSE prediction from both methods was compared using Mann-Whitney U test. RESULTS: 1696 electro-clinical tests were used to design and compare the two methods. Sensitivity, specificity, positive and negative predictive values were significantly higher with the PyMAN method than with the VTA-based method (P \textless0.05). CONCLUSION: The PyMAN method was more effective than the VTA-based method to predict PTSE. SIGNIFICANCE: This data-driven tool could help the neurosurgeon in predicting adverse side-effects induced by DBS during the electrode trajectory pre-planning.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Pays de la Loire</li><li>Région Bretagne</li></region><settlement><li>Nantes</li><li>Rennes</li></settlement><orgName><li>Université de Nantes</li><li>Université de Rennes 1</li><li>Université européenne de Bretagne</li></orgName></list><tree><noCountry><name sortKey="Baumgarten, Clement" sort="Baumgarten, Clement" uniqKey="Baumgarten C" first="Clement" last="Baumgarten">Clement Baumgarten</name></noCountry><country name="France"><noRegion><name sortKey="Zhao, Yulong" sort="Zhao, Yulong" uniqKey="Zhao Y" first="Yulong" last="Zhao">Yulong Zhao</name></noRegion><name sortKey="Haegelen, Claire" sort="Haegelen, Claire" uniqKey="Haegelen C" first="Claire" last="Haegelen">Claire Haegelen</name><name sortKey="Jannin, Pierre" sort="Jannin, Pierre" uniqKey="Jannin P" first="Pierre" last="Jannin">Pierre Jannin</name><name sortKey="Malrain, Cecile" sort="Malrain, Cecile" uniqKey="Malrain C" first="Cécile" last="Malrain">Cécile Malrain</name><name sortKey="Sauleau, Paul" sort="Sauleau, Paul" uniqKey="Sauleau P" first="Paul" last="Sauleau">Paul Sauleau</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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