Real‐time refinement of subthalamic nucleus targeting using Bayesian decision‐making on the root mean square measure
Identifieur interne : 003222 ( Main/Curation ); précédent : 003221; suivant : 003223Real‐time refinement of subthalamic nucleus targeting using Bayesian decision‐making on the root mean square measure
Auteurs : Anan Moran [Israël] ; Izhar Bar-Gad [Israël] ; Hagai Bergman [Israël] ; Zvi Israel [Israël]Source :
- Movement Disorders [ 0885-3185 ] ; 2006-09.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
- Bayes Theorem, Bayesian inference, Brain Mapping, Decision making, Deep Brain Stimulation (instrumentation), Deep brain stimulation, Electrodes, Implanted, Electroencephalography (instrumentation), Evoked Potentials (physiology), Expert Systems, Humans, Inference, Likelihood Functions, Localization, Magnetic Resonance Imaging (instrumentation), Mathematical Computing, Microelectrode, Microelectrodes, Nervous system diseases, Parkinson Disease (physiopathology), Parkinson Disease (rehabilitation), Parkinson disease, Parkinson's disease, Probability, Refinement, Software, Square root, Stereotaxic Techniques (instrumentation), Subthalamic Nucleus (physiopathology), Subthalamic nucleus, Surgery, Computer-Assisted (instrumentation), Targeting, deep brain stimulation, microelectrode recording, root mean square, target localization.
- MESH :
- instrumentation : Deep Brain Stimulation, Electroencephalography, Magnetic Resonance Imaging, Stereotaxic Techniques, Surgery, Computer-Assisted.
- physiology : Evoked Potentials.
- physiopathology : Parkinson Disease, Subthalamic Nucleus.
- rehabilitation : Parkinson Disease.
- Bayes Theorem, Brain Mapping, Electrodes, Implanted, Expert Systems, Humans, Likelihood Functions, Mathematical Computing, Microelectrodes, Probability, Software.
Abstract
The subthalamic nucleus (STN) is a major target for treatment of advanced Parkinson's disease patients undergoing deep brain stimulation surgery. Microelectrode recording (MER) is used in many cases to identify the target nucleus. A real‐time procedure for identifying the entry and exit points of the STN would improve the outcome of this targeting procedure. We used the normalized root mean square (NRMS) of a short (5 seconds) MER sampled signal and the estimated anatomical distance to target (EDT) as the basis for this procedure. Electrode tip location was defined intraoperatively by an expert neurophysiologist to be before, within, or after the STN. Data from 46 trajectories of 27 patients were used to calculate the Bayesian posterior probability of being in each of these locations, given RMS‐EDT pair values. We tested our predictions on each trajectory using a bootstrapping technique, with the rest of the trajectories serving as a training set and found the error in predicting the STN entry to be (mean ± SD) 0.18 ± 0.84, and 0.50 ± 0.59 mm for STN exit point, which yields a 0.30 ± 0.28 mm deviation from the expert's target center. The simplicity and computational ease of RMS calculation, its spike sorting‐independent nature and tolerance to electrode parameters of this Bayesian predictor, can lead directly to the development of a fully automated intraoperative physiological procedure for the refinement of imaging estimates of STN borders. © 2006 Movement Disorder Society
Url:
DOI: 10.1002/mds.20995
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ISTEX:F649E9403505138A799145593BAAF884D378A715Le document en format XML
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Disord.</title><idno type="ISSN">0885-3185</idno><idno type="eISSN">1531-8257</idno><imprint><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><pubPlace>Hoboken</pubPlace><date type="published" when="2006-09">2006-09</date><biblScope unit="vol">21</biblScope><biblScope unit="issue">9</biblScope><biblScope unit="page" from="1425">1425</biblScope><biblScope unit="page" to="1431">1431</biblScope></imprint><idno type="ISSN">0885-3185</idno></series><idno type="istex">F649E9403505138A799145593BAAF884D378A715</idno><idno type="DOI">10.1002/mds.20995</idno><idno type="ArticleID">MDS20995</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0885-3185</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Bayes Theorem</term><term>Bayesian inference</term><term>Brain Mapping</term><term>Decision making</term><term>Deep Brain Stimulation (instrumentation)</term><term>Deep brain stimulation</term><term>Electrodes, Implanted</term><term>Electroencephalography (instrumentation)</term><term>Evoked Potentials (physiology)</term><term>Expert Systems</term><term>Humans</term><term>Inference</term><term>Likelihood Functions</term><term>Localization</term><term>Magnetic Resonance Imaging (instrumentation)</term><term>Mathematical Computing</term><term>Microelectrode</term><term>Microelectrodes</term><term>Nervous system diseases</term><term>Parkinson Disease (physiopathology)</term><term>Parkinson Disease (rehabilitation)</term><term>Parkinson disease</term><term>Parkinson's disease</term><term>Probability</term><term>Refinement</term><term>Software</term><term>Square root</term><term>Stereotaxic Techniques (instrumentation)</term><term>Subthalamic Nucleus (physiopathology)</term><term>Subthalamic nucleus</term><term>Surgery, Computer-Assisted (instrumentation)</term><term>Targeting</term><term>deep brain stimulation</term><term>microelectrode recording</term><term>root mean square</term><term>target localization</term></keywords><keywords scheme="MESH" qualifier="instrumentation" xml:lang="en"><term>Deep Brain Stimulation</term><term>Electroencephalography</term><term>Magnetic Resonance Imaging</term><term>Stereotaxic Techniques</term><term>Surgery, Computer-Assisted</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Evoked Potentials</term></keywords><keywords scheme="MESH" qualifier="physiopathology" xml:lang="en"><term>Parkinson Disease</term><term>Subthalamic Nucleus</term></keywords><keywords scheme="MESH" qualifier="rehabilitation" xml:lang="en"><term>Parkinson Disease</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Bayes Theorem</term><term>Brain Mapping</term><term>Electrodes, Implanted</term><term>Expert Systems</term><term>Humans</term><term>Likelihood Functions</term><term>Mathematical Computing</term><term>Microelectrodes</term><term>Probability</term><term>Software</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Affinement</term><term>Ciblage</term><term>Inférence</term><term>Localisation</term><term>Microélectrode</term><term>Noyau sousthalamique</term><term>Parkinson maladie</term><term>Prise décision</term><term>Racine carrée</term><term>Stimulation cérébrale profonde</term><term>Système nerveux pathologie</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The subthalamic nucleus (STN) is a major target for treatment of advanced Parkinson's disease patients undergoing deep brain stimulation surgery. Microelectrode recording (MER) is used in many cases to identify the target nucleus. A real‐time procedure for identifying the entry and exit points of the STN would improve the outcome of this targeting procedure. We used the normalized root mean square (NRMS) of a short (5 seconds) MER sampled signal and the estimated anatomical distance to target (EDT) as the basis for this procedure. Electrode tip location was defined intraoperatively by an expert neurophysiologist to be before, within, or after the STN. Data from 46 trajectories of 27 patients were used to calculate the Bayesian posterior probability of being in each of these locations, given RMS‐EDT pair values. We tested our predictions on each trajectory using a bootstrapping technique, with the rest of the trajectories serving as a training set and found the error in predicting the STN entry to be (mean ± SD) 0.18 ± 0.84, and 0.50 ± 0.59 mm for STN exit point, which yields a 0.30 ± 0.28 mm deviation from the expert's target center. The simplicity and computational ease of RMS calculation, its spike sorting‐independent nature and tolerance to electrode parameters of this Bayesian predictor, can lead directly to the development of a fully automated intraoperative physiological procedure for the refinement of imaging estimates of STN borders. © 2006 Movement Disorder Society</div></front></TEI><double idat="0885-3185:2006:Moran A:real:time:refinement"><INIST><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" level="a">Real-time refinement of subthalamic nucleus targeting using bayesian decision-making on the root mean square measure</title><author><name sortKey="Moran, Anan" sort="Moran, Anan" uniqKey="Moran A" first="Anan" last="Moran">Anan Moran</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Gonda Multidisciplinary Brain Research Center and Faculty of Life Sciences, Bar Ilan University</s1><s2>Ramat Gan</s2><s3>ISR</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>Israël</country><wicri:noRegion>Ramat Gan</wicri:noRegion></affiliation></author><author><name sortKey="Bar Gad, Izhar" sort="Bar Gad, Izhar" uniqKey="Bar Gad I" first="Izhar" last="Bar-Gad">Izhar Bar-Gad</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Gonda Multidisciplinary Brain Research Center and Faculty of Life Sciences, Bar Ilan University</s1><s2>Ramat Gan</s2><s3>ISR</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>Israël</country><wicri:noRegion>Ramat Gan</wicri:noRegion></affiliation></author><author><name sortKey="Bergman, Hagai" sort="Bergman, Hagai" uniqKey="Bergman H" first="Hagai" last="Bergman">Hagai Bergman</name><affiliation wicri:level="1"><inist:fA14 i1="02"><s1>Department of Physiology, Hadassah Medical School and Interdisciplinary Center for Neural Computation, Hebrew University</s1><s2>Jerusalem</s2><s3>ISR</s3><sZ>3 aut.</sZ></inist:fA14><country>Israël</country><wicri:noRegion>Jerusalem</wicri:noRegion></affiliation></author><author><name sortKey="Israel, Zvi" sort="Israel, Zvi" uniqKey="Israel Z" first="Zvi" last="Israel">Zvi Israel</name><affiliation wicri:level="1"><inist:fA14 i1="03"><s1>Department of Neurosurgery, Hadassah University Hospital</s1><s2>Jerusalem</s2><s3>ISR</s3><sZ>4 aut.</sZ></inist:fA14><country>Israël</country><wicri:noRegion>Jerusalem</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">INIST</idno><idno type="inist">06-0518097</idno><date when="2006">2006</date><idno type="stanalyst">PASCAL 06-0518097 INIST</idno><idno type="RBID">Pascal:06-0518097</idno><idno type="wicri:Area/PascalFrancis/Corpus">001999</idno><idno type="wicri:Area/PascalFrancis/Curation">001322</idno><idno type="wicri:Area/PascalFrancis/Checkpoint">001925</idno><idno type="wicri:doubleKey">0885-3185:2006:Moran A:real:time:refinement</idno><idno type="wicri:Area/Main/Merge">004A20</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Real-time refinement of subthalamic nucleus targeting using bayesian decision-making on the root mean square measure</title><author><name sortKey="Moran, Anan" sort="Moran, Anan" uniqKey="Moran A" first="Anan" last="Moran">Anan Moran</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Gonda Multidisciplinary Brain Research Center and Faculty of Life Sciences, Bar Ilan University</s1><s2>Ramat Gan</s2><s3>ISR</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>Israël</country><wicri:noRegion>Ramat Gan</wicri:noRegion></affiliation></author><author><name sortKey="Bar Gad, Izhar" sort="Bar Gad, Izhar" uniqKey="Bar Gad I" first="Izhar" last="Bar-Gad">Izhar Bar-Gad</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Gonda Multidisciplinary Brain Research Center and Faculty of Life Sciences, Bar Ilan University</s1><s2>Ramat Gan</s2><s3>ISR</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>Israël</country><wicri:noRegion>Ramat Gan</wicri:noRegion></affiliation></author><author><name sortKey="Bergman, Hagai" sort="Bergman, Hagai" uniqKey="Bergman H" first="Hagai" last="Bergman">Hagai Bergman</name><affiliation wicri:level="1"><inist:fA14 i1="02"><s1>Department of Physiology, Hadassah Medical School and Interdisciplinary Center for Neural Computation, Hebrew University</s1><s2>Jerusalem</s2><s3>ISR</s3><sZ>3 aut.</sZ></inist:fA14><country>Israël</country><wicri:noRegion>Jerusalem</wicri:noRegion></affiliation></author><author><name sortKey="Israel, Zvi" sort="Israel, Zvi" uniqKey="Israel Z" first="Zvi" last="Israel">Zvi Israel</name><affiliation wicri:level="1"><inist:fA14 i1="03"><s1>Department of Neurosurgery, Hadassah University Hospital</s1><s2>Jerusalem</s2><s3>ISR</s3><sZ>4 aut.</sZ></inist:fA14><country>Israël</country><wicri:noRegion>Jerusalem</wicri:noRegion></affiliation></author></analytic><series><title level="j" type="main">Movement disorders</title><title level="j" type="abbreviated">Mov. disord.</title><idno type="ISSN">0885-3185</idno><imprint><date when="2006">2006</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Movement disorders</title><title level="j" type="abbreviated">Mov. disord.</title><idno type="ISSN">0885-3185</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Decision making</term><term>Deep brain stimulation</term><term>Inference</term><term>Localization</term><term>Microelectrode</term><term>Nervous system diseases</term><term>Parkinson disease</term><term>Refinement</term><term>Square root</term><term>Subthalamic nucleus</term><term>Targeting</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Système nerveux pathologie</term><term>Parkinson maladie</term><term>Affinement</term><term>Noyau sousthalamique</term><term>Ciblage</term><term>Prise décision</term><term>Racine carrée</term><term>Inférence</term><term>Microélectrode</term><term>Localisation</term><term>Stimulation cérébrale profonde</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The subthalamic nucleus (STN) is a major target for treatment of advanced Parkinson's disease patients undergoing deep brain stimulation surgery. Microelectrode recording (MER) is used in many cases to identify the target nucleus. A real-time procedure for identifying the entry and exit points of the STN would improve the outcome of this targeting procedure. We used the normalized root mean square (NRMS) of a short (5 seconds) MER sampled signal and the estimated anatomical distance to target (EDT) as the basis for this procedure. Electrode tip location was defined intraoperatively by an expert neurophysiologist to be before, within, or after the STN. Data from 46 trajectories of 27 patients were used to calculate the Bayesian posterior probability of being in each of these locations, given RMS-EDT pair values. We tested our predictions on each trajectory using a bootstrapping technique, with the rest of the trajectories serving as a training set and found the error in predicting the STN entry to be (mean ± SD) 0.18 ± 0.84, and 0.50 ± 0.59 mm for STN exit point, which yields a 0.30 ± 0.28 mm deviation from the expert's target center. The simplicity and computational ease of RMS calculation, its spike sorting-independent nature and tolerance to electrode parameters of this Bayesian predictor, can lead directly to the development of a fully automated intraoperative physiological procedure for the refinement of imaging estimates of STN borders.</div></front></TEI></INIST><ISTEX><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Real‐time refinement of subthalamic nucleus targeting using Bayesian decision‐making on the root mean square measure</title><author><name sortKey="Moran, Anan" sort="Moran, Anan" uniqKey="Moran A" first="Anan" last="Moran">Anan Moran</name></author><author><name sortKey="Bar Ad, Izhar" sort="Bar Ad, Izhar" uniqKey="Bar Ad I" first="Izhar" last="Bar-Gad">Izhar Bar-Gad</name></author><author><name sortKey="Bergman, Hagai" sort="Bergman, Hagai" uniqKey="Bergman H" first="Hagai" last="Bergman">Hagai Bergman</name></author><author><name sortKey="Israel, Zvi" sort="Israel, Zvi" uniqKey="Israel Z" first="Zvi" last="Israel">Zvi Israel</name></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:F649E9403505138A799145593BAAF884D378A715</idno><date when="2006" year="2006">2006</date><idno type="doi">10.1002/mds.20995</idno><idno type="url">https://api.istex.fr/document/F649E9403505138A799145593BAAF884D378A715/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">000B20</idno><idno type="wicri:Area/Istex/Curation">000B20</idno><idno type="wicri:Area/Istex/Checkpoint">001C81</idno><idno type="wicri:doubleKey">0885-3185:2006:Moran A:real:time:refinement</idno><idno type="wicri:source">PubMed</idno><idno type="RBID">pubmed:16763982</idno><idno type="wicri:Area/PubMed/Corpus">002C01</idno><idno type="wicri:Area/PubMed/Curation">002C01</idno><idno type="wicri:Area/PubMed/Checkpoint">002B22</idno><idno type="wicri:Area/Ncbi/Merge">001735</idno><idno type="wicri:Area/Ncbi/Curation">001735</idno><idno type="wicri:Area/Ncbi/Checkpoint">001735</idno><idno type="wicri:doubleKey">0885-3185:2006:Moran A:real:time:refinement</idno><idno type="wicri:Area/Main/Merge">004535</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Real‐time refinement of subthalamic nucleus targeting using Bayesian decision‐making on the root mean square measure</title><author><name sortKey="Moran, Anan" sort="Moran, Anan" uniqKey="Moran A" first="Anan" last="Moran">Anan Moran</name><affiliation wicri:level="1"><country xml:lang="fr">Israël</country><wicri:regionArea>Gonda Multidisciplinary Brain Research Center and Faculty of Life Sciences, Bar Ilan University, Ramat Gan</wicri:regionArea><wicri:noRegion>Ramat Gan</wicri:noRegion></affiliation></author><author><name sortKey="Bar Ad, Izhar" sort="Bar Ad, Izhar" uniqKey="Bar Ad I" first="Izhar" last="Bar-Gad">Izhar Bar-Gad</name><affiliation wicri:level="1"><country xml:lang="fr">Israël</country><wicri:regionArea>Gonda Multidisciplinary Brain Research Center and Faculty of Life Sciences, Bar Ilan University, Ramat Gan</wicri:regionArea><wicri:noRegion>Ramat Gan</wicri:noRegion></affiliation></author><author><name sortKey="Bergman, Hagai" sort="Bergman, Hagai" uniqKey="Bergman H" first="Hagai" last="Bergman">Hagai Bergman</name><affiliation wicri:level="1"><country xml:lang="fr">Israël</country><wicri:regionArea>Department of Physiology, Hadassah Medical School and Interdisciplinary Center for Neural Computation, Hebrew University, Jerusalem</wicri:regionArea><wicri:noRegion>Jerusalem</wicri:noRegion></affiliation></author><author><name sortKey="Israel, Zvi" sort="Israel, Zvi" uniqKey="Israel Z" first="Zvi" last="Israel">Zvi Israel</name><affiliation wicri:level="1"><country xml:lang="fr">Israël</country><wicri:regionArea>Department of Neurosurgery, Hadassah University Hospital, Jerusalem</wicri:regionArea><wicri:noRegion>Jerusalem</wicri:noRegion></affiliation></author></analytic><monogr></monogr><series><title level="j">Movement Disorders</title><title level="j" type="sub">Official Journal of the Movement Disorder Society</title><title level="j" type="abbrev">Mov. Disord.</title><idno type="ISSN">0885-3185</idno><idno type="eISSN">1531-8257</idno><imprint><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><pubPlace>Hoboken</pubPlace><date type="published" when="2006-09">2006-09</date><biblScope unit="vol">21</biblScope><biblScope unit="issue">9</biblScope><biblScope unit="page" from="1425">1425</biblScope><biblScope unit="page" to="1431">1431</biblScope></imprint><idno type="ISSN">0885-3185</idno></series><idno type="istex">F649E9403505138A799145593BAAF884D378A715</idno><idno type="DOI">10.1002/mds.20995</idno><idno type="ArticleID">MDS20995</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0885-3185</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Bayes Theorem</term><term>Bayesian inference</term><term>Brain Mapping</term><term>Deep Brain Stimulation (instrumentation)</term><term>Electrodes, Implanted</term><term>Electroencephalography (instrumentation)</term><term>Evoked Potentials (physiology)</term><term>Expert Systems</term><term>Humans</term><term>Likelihood Functions</term><term>Magnetic Resonance Imaging (instrumentation)</term><term>Mathematical Computing</term><term>Microelectrodes</term><term>Parkinson Disease (physiopathology)</term><term>Parkinson Disease (rehabilitation)</term><term>Parkinson's disease</term><term>Probability</term><term>Software</term><term>Stereotaxic Techniques (instrumentation)</term><term>Subthalamic Nucleus (physiopathology)</term><term>Surgery, Computer-Assisted (instrumentation)</term><term>deep brain stimulation</term><term>microelectrode recording</term><term>root mean square</term><term>target localization</term></keywords><keywords scheme="MESH" qualifier="instrumentation" xml:lang="en"><term>Deep Brain Stimulation</term><term>Electroencephalography</term><term>Magnetic Resonance Imaging</term><term>Stereotaxic Techniques</term><term>Surgery, Computer-Assisted</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Evoked Potentials</term></keywords><keywords scheme="MESH" qualifier="physiopathology" xml:lang="en"><term>Parkinson Disease</term><term>Subthalamic Nucleus</term></keywords><keywords scheme="MESH" qualifier="rehabilitation" xml:lang="en"><term>Parkinson Disease</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Bayes Theorem</term><term>Brain Mapping</term><term>Electrodes, Implanted</term><term>Expert Systems</term><term>Humans</term><term>Likelihood Functions</term><term>Mathematical Computing</term><term>Microelectrodes</term><term>Probability</term><term>Software</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The subthalamic nucleus (STN) is a major target for treatment of advanced Parkinson's disease patients undergoing deep brain stimulation surgery. Microelectrode recording (MER) is used in many cases to identify the target nucleus. A real‐time procedure for identifying the entry and exit points of the STN would improve the outcome of this targeting procedure. We used the normalized root mean square (NRMS) of a short (5 seconds) MER sampled signal and the estimated anatomical distance to target (EDT) as the basis for this procedure. Electrode tip location was defined intraoperatively by an expert neurophysiologist to be before, within, or after the STN. Data from 46 trajectories of 27 patients were used to calculate the Bayesian posterior probability of being in each of these locations, given RMS‐EDT pair values. We tested our predictions on each trajectory using a bootstrapping technique, with the rest of the trajectories serving as a training set and found the error in predicting the STN entry to be (mean ± SD) 0.18 ± 0.84, and 0.50 ± 0.59 mm for STN exit point, which yields a 0.30 ± 0.28 mm deviation from the expert's target center. The simplicity and computational ease of RMS calculation, its spike sorting‐independent nature and tolerance to electrode parameters of this Bayesian predictor, can lead directly to the development of a fully automated intraoperative physiological procedure for the refinement of imaging estimates of STN borders. © 2006 Movement Disorder Society</div></front></TEI></ISTEX></double></record>Pour manipuler ce document sous Unix (Dilib)
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