Real-time refinement of subthalamic nucleus targeting using Bayesian decision-making on the root mean square measure.
Identifieur interne : 001735 ( Ncbi/Checkpoint ); précédent : 001734; suivant : 001736Real-time refinement of subthalamic nucleus targeting using Bayesian decision-making on the root mean square measure.
Auteurs : Anan Moran [Israël] ; Izhar Bar-Gad ; Hagai Bergman ; Zvi IsraelSource :
- Movement disorders : official journal of the Movement Disorder Society [ 0885-3185 ] ; 2006.
English descriptors
- KwdEn :
- Bayes Theorem, Brain Mapping, Deep Brain Stimulation (instrumentation), Electrodes, Implanted, Electroencephalography (instrumentation), Evoked Potentials (physiology), Expert Systems, Humans, Likelihood Functions, Magnetic Resonance Imaging (instrumentation), Mathematical Computing, Microelectrodes, Parkinson Disease (physiopathology), Parkinson Disease (rehabilitation), Probability, Software, Stereotaxic Techniques (instrumentation), Subthalamic Nucleus (physiopathology), Surgery, Computer-Assisted (instrumentation).
- 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.
DOI: 10.1002/mds.20995
PubMed: 16763982
Affiliations:
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<record><TEI><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><affiliation wicri:level="1"><nlm:affiliation>Gonda Multidisciplinary Brain Research Center and Faculty of Life Sciences, Bar Ilan University, Ramat Gan, Israel. ananmo@gmail.com</nlm:affiliation><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 Gad, Izhar" sort="Bar Gad, Izhar" uniqKey="Bar Gad 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">PubMed</idno><date when="2006">2006</date><idno type="doi">10.1002/mds.20995</idno><idno type="RBID">pubmed:16763982</idno><idno type="pmid">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></publicationStmt><sourceDesc><biblStruct><analytic><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><affiliation wicri:level="1"><nlm:affiliation>Gonda Multidisciplinary Brain Research Center and Faculty of Life Sciences, Bar Ilan University, Ramat Gan, Israel. ananmo@gmail.com</nlm:affiliation><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 Gad, Izhar" sort="Bar Gad, Izhar" uniqKey="Bar Gad 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></analytic><series><title level="j">Movement disorders : official journal of the Movement Disorder Society</title><idno type="ISSN">0885-3185</idno><imprint><date when="2006" type="published">2006</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Bayes Theorem</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>Probability</term><term>Software</term><term>Stereotaxic Techniques (instrumentation)</term><term>Subthalamic Nucleus (physiopathology)</term><term>Surgery, Computer-Assisted (instrumentation)</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></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><affiliations><list><country><li>Israël</li></country></list><tree><noCountry><name sortKey="Bar Gad, Izhar" sort="Bar Gad, Izhar" uniqKey="Bar Gad I" first="Izhar" last="Bar-Gad">Izhar Bar-Gad</name><name sortKey="Bergman, Hagai" sort="Bergman, Hagai" uniqKey="Bergman H" first="Hagai" last="Bergman">Hagai Bergman</name><name sortKey="Israel, Zvi" sort="Israel, Zvi" uniqKey="Israel Z" first="Zvi" last="Israel">Zvi Israel</name></noCountry><country name="Israël"><noRegion><name sortKey="Moran, Anan" sort="Moran, Anan" uniqKey="Moran A" first="Anan" last="Moran">Anan Moran</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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