Real-time refinement of subthalamic nucleus targeting using bayesian decision-making on the root mean square measure
Identifieur interne : 001999 ( PascalFrancis/Corpus ); précédent : 001998; suivant : 001A00Real-time refinement of subthalamic nucleus targeting using bayesian decision-making on the root mean square measure
Auteurs : Anan Moran ; Izhar Bar-Gad ; Hagai Bergman ; Zvi IsraelSource :
- Movement disorders [ 0885-3185 ] ; 2006.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
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.
Notice en format standard (ISO 2709)
Pour connaître la documentation sur le format Inist Standard.
pA |
|
---|
Format Inist (serveur)
NO : | PASCAL 06-0518097 INIST |
---|---|
ET : | Real-time refinement of subthalamic nucleus targeting using bayesian decision-making on the root mean square measure |
AU : | MORAN (Anan); BAR-GAD (Izhar); BERGMAN (Hagai); ISRAEL (Zvi) |
AF : | Gonda Multidisciplinary Brain Research Center and Faculty of Life Sciences, Bar Ilan University/Ramat Gan/Israël (1 aut., 2 aut.); Department of Physiology, Hadassah Medical School and Interdisciplinary Center for Neural Computation, Hebrew University/Jerusalem/Israël (3 aut.); Department of Neurosurgery, Hadassah University Hospital/Jerusalem/Israël (4 aut.) |
DT : | Publication en série; Niveau analytique |
SO : | Movement disorders; ISSN 0885-3185; Etats-Unis; Da. 2006; Vol. 21; No. 9; Pp. 1425-1431; Bibl. 21 ref. |
LA : | Anglais |
EA : | 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. |
CC : | 002B17; 002B16B; 002B25J01 |
FD : | Système nerveux pathologie; Parkinson maladie; Affinement; Noyau sousthalamique; Ciblage; Prise décision; Racine carrée; Inférence; Microélectrode; Localisation; Stimulation cérébrale profonde |
FG : | Encéphale pathologie; Système nerveux central; Extrapyramidal syndrome; Maladie dégénérative; Système nerveux central pathologie |
ED : | Nervous system diseases; Parkinson disease; Refinement; Subthalamic nucleus; Targeting; Decision making; Square root; Inference; Microelectrode; Localization; Deep brain stimulation |
EG : | Cerebral disorder; Central nervous system; Extrapyramidal syndrome; Degenerative disease; Central nervous system disease |
SD : | Sistema nervioso patología; Parkinson enfermedad; Afinamiento; Núcleo subtalámico; Blancado; Toma decision; Raíz cuadrada; Inferencia; Microeléctrodo; Localización |
LO : | INIST-20953.354000158780860190 |
ID : | 06-0518097 |
Links to Exploration step
Pascal:06-0518097Le document en format XML
<record><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><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>
</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><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>
</affiliation>
</author>
<author><name sortKey="Bergman, Hagai" sort="Bergman, Hagai" uniqKey="Bergman H" first="Hagai" last="Bergman">Hagai Bergman</name>
<affiliation><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>
</affiliation>
</author>
<author><name sortKey="Israel, Zvi" sort="Israel, Zvi" uniqKey="Israel Z" first="Zvi" last="Israel">Zvi Israel</name>
<affiliation><inist:fA14 i1="03"><s1>Department of Neurosurgery, Hadassah University Hospital</s1>
<s2>Jerusalem</s2>
<s3>ISR</s3>
<sZ>4 aut.</sZ>
</inist:fA14>
</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>
</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><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>
</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><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>
</affiliation>
</author>
<author><name sortKey="Bergman, Hagai" sort="Bergman, Hagai" uniqKey="Bergman H" first="Hagai" last="Bergman">Hagai Bergman</name>
<affiliation><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>
</affiliation>
</author>
<author><name sortKey="Israel, Zvi" sort="Israel, Zvi" uniqKey="Israel Z" first="Zvi" last="Israel">Zvi Israel</name>
<affiliation><inist:fA14 i1="03"><s1>Department of Neurosurgery, Hadassah University Hospital</s1>
<s2>Jerusalem</s2>
<s3>ISR</s3>
<sZ>4 aut.</sZ>
</inist:fA14>
</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><standard h6="B"><pA><fA01 i1="01" i2="1"><s0>0885-3185</s0>
</fA01>
<fA03 i2="1"><s0>Mov. disord.</s0>
</fA03>
<fA05><s2>21</s2>
</fA05>
<fA06><s2>9</s2>
</fA06>
<fA08 i1="01" i2="1" l="ENG"><s1>Real-time refinement of subthalamic nucleus targeting using bayesian decision-making on the root mean square measure</s1>
</fA08>
<fA11 i1="01" i2="1"><s1>MORAN (Anan)</s1>
</fA11>
<fA11 i1="02" i2="1"><s1>BAR-GAD (Izhar)</s1>
</fA11>
<fA11 i1="03" i2="1"><s1>BERGMAN (Hagai)</s1>
</fA11>
<fA11 i1="04" i2="1"><s1>ISRAEL (Zvi)</s1>
</fA11>
<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>
</fA14>
<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>
</fA14>
<fA14 i1="03"><s1>Department of Neurosurgery, Hadassah University Hospital</s1>
<s2>Jerusalem</s2>
<s3>ISR</s3>
<sZ>4 aut.</sZ>
</fA14>
<fA20><s1>1425-1431</s1>
</fA20>
<fA21><s1>2006</s1>
</fA21>
<fA23 i1="01"><s0>ENG</s0>
</fA23>
<fA43 i1="01"><s1>INIST</s1>
<s2>20953</s2>
<s5>354000158780860190</s5>
</fA43>
<fA44><s0>0000</s0>
<s1>© 2006 INIST-CNRS. All rights reserved.</s1>
</fA44>
<fA45><s0>21 ref.</s0>
</fA45>
<fA47 i1="01" i2="1"><s0>06-0518097</s0>
</fA47>
<fA60><s1>P</s1>
</fA60>
<fA61><s0>A</s0>
</fA61>
<fA64 i1="01" i2="1"><s0>Movement disorders</s0>
</fA64>
<fA66 i1="01"><s0>USA</s0>
</fA66>
<fC01 i1="01" l="ENG"><s0>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.</s0>
</fC01>
<fC02 i1="01" i2="X"><s0>002B17</s0>
</fC02>
<fC02 i1="02" i2="X"><s0>002B16B</s0>
</fC02>
<fC02 i1="03" i2="X"><s0>002B25J01</s0>
</fC02>
<fC03 i1="01" i2="X" l="FRE"><s0>Système nerveux pathologie</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="ENG"><s0>Nervous system diseases</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="SPA"><s0>Sistema nervioso patología</s0>
<s5>01</s5>
</fC03>
<fC03 i1="02" i2="X" l="FRE"><s0>Parkinson maladie</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="ENG"><s0>Parkinson disease</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="SPA"><s0>Parkinson enfermedad</s0>
<s5>02</s5>
</fC03>
<fC03 i1="03" i2="X" l="FRE"><s0>Affinement</s0>
<s5>09</s5>
</fC03>
<fC03 i1="03" i2="X" l="ENG"><s0>Refinement</s0>
<s5>09</s5>
</fC03>
<fC03 i1="03" i2="X" l="SPA"><s0>Afinamiento</s0>
<s5>09</s5>
</fC03>
<fC03 i1="04" i2="X" l="FRE"><s0>Noyau sousthalamique</s0>
<s5>10</s5>
</fC03>
<fC03 i1="04" i2="X" l="ENG"><s0>Subthalamic nucleus</s0>
<s5>10</s5>
</fC03>
<fC03 i1="04" i2="X" l="SPA"><s0>Núcleo subtalámico</s0>
<s5>10</s5>
</fC03>
<fC03 i1="05" i2="X" l="FRE"><s0>Ciblage</s0>
<s5>11</s5>
</fC03>
<fC03 i1="05" i2="X" l="ENG"><s0>Targeting</s0>
<s5>11</s5>
</fC03>
<fC03 i1="05" i2="X" l="SPA"><s0>Blancado</s0>
<s5>11</s5>
</fC03>
<fC03 i1="06" i2="X" l="FRE"><s0>Prise décision</s0>
<s5>12</s5>
</fC03>
<fC03 i1="06" i2="X" l="ENG"><s0>Decision making</s0>
<s5>12</s5>
</fC03>
<fC03 i1="06" i2="X" l="SPA"><s0>Toma decision</s0>
<s5>12</s5>
</fC03>
<fC03 i1="07" i2="X" l="FRE"><s0>Racine carrée</s0>
<s5>13</s5>
</fC03>
<fC03 i1="07" i2="X" l="ENG"><s0>Square root</s0>
<s5>13</s5>
</fC03>
<fC03 i1="07" i2="X" l="SPA"><s0>Raíz cuadrada</s0>
<s5>13</s5>
</fC03>
<fC03 i1="08" i2="X" l="FRE"><s0>Inférence</s0>
<s5>14</s5>
</fC03>
<fC03 i1="08" i2="X" l="ENG"><s0>Inference</s0>
<s5>14</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA"><s0>Inferencia</s0>
<s5>14</s5>
</fC03>
<fC03 i1="09" i2="X" l="FRE"><s0>Microélectrode</s0>
<s5>15</s5>
</fC03>
<fC03 i1="09" i2="X" l="ENG"><s0>Microelectrode</s0>
<s5>15</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA"><s0>Microeléctrodo</s0>
<s5>15</s5>
</fC03>
<fC03 i1="10" i2="X" l="FRE"><s0>Localisation</s0>
<s5>16</s5>
</fC03>
<fC03 i1="10" i2="X" l="ENG"><s0>Localization</s0>
<s5>16</s5>
</fC03>
<fC03 i1="10" i2="X" l="SPA"><s0>Localización</s0>
<s5>16</s5>
</fC03>
<fC03 i1="11" i2="X" l="FRE"><s0>Stimulation cérébrale profonde</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03>
<fC03 i1="11" i2="X" l="ENG"><s0>Deep brain stimulation</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03>
<fC07 i1="01" i2="X" l="FRE"><s0>Encéphale pathologie</s0>
<s5>37</s5>
</fC07>
<fC07 i1="01" i2="X" l="ENG"><s0>Cerebral disorder</s0>
<s5>37</s5>
</fC07>
<fC07 i1="01" i2="X" l="SPA"><s0>Encéfalo patología</s0>
<s5>37</s5>
</fC07>
<fC07 i1="02" i2="X" l="FRE"><s0>Système nerveux central</s0>
<s5>38</s5>
</fC07>
<fC07 i1="02" i2="X" l="ENG"><s0>Central nervous system</s0>
<s5>38</s5>
</fC07>
<fC07 i1="02" i2="X" l="SPA"><s0>Sistema nervioso central</s0>
<s5>38</s5>
</fC07>
<fC07 i1="03" i2="X" l="FRE"><s0>Extrapyramidal syndrome</s0>
<s5>39</s5>
</fC07>
<fC07 i1="03" i2="X" l="ENG"><s0>Extrapyramidal syndrome</s0>
<s5>39</s5>
</fC07>
<fC07 i1="03" i2="X" l="SPA"><s0>Extrapiramidal síndrome</s0>
<s5>39</s5>
</fC07>
<fC07 i1="04" i2="X" l="FRE"><s0>Maladie dégénérative</s0>
<s5>40</s5>
</fC07>
<fC07 i1="04" i2="X" l="ENG"><s0>Degenerative disease</s0>
<s5>40</s5>
</fC07>
<fC07 i1="04" i2="X" l="SPA"><s0>Enfermedad degenerativa</s0>
<s5>40</s5>
</fC07>
<fC07 i1="05" i2="X" l="FRE"><s0>Système nerveux central pathologie</s0>
<s5>41</s5>
</fC07>
<fC07 i1="05" i2="X" l="ENG"><s0>Central nervous system disease</s0>
<s5>41</s5>
</fC07>
<fC07 i1="05" i2="X" l="SPA"><s0>Sistema nervosio central patología</s0>
<s5>41</s5>
</fC07>
<fN21><s1>338</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
</standard>
<server><NO>PASCAL 06-0518097 INIST</NO>
<ET>Real-time refinement of subthalamic nucleus targeting using bayesian decision-making on the root mean square measure</ET>
<AU>MORAN (Anan); BAR-GAD (Izhar); BERGMAN (Hagai); ISRAEL (Zvi)</AU>
<AF>Gonda Multidisciplinary Brain Research Center and Faculty of Life Sciences, Bar Ilan University/Ramat Gan/Israël (1 aut., 2 aut.); Department of Physiology, Hadassah Medical School and Interdisciplinary Center for Neural Computation, Hebrew University/Jerusalem/Israël (3 aut.); Department of Neurosurgery, Hadassah University Hospital/Jerusalem/Israël (4 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>Movement disorders; ISSN 0885-3185; Etats-Unis; Da. 2006; Vol. 21; No. 9; Pp. 1425-1431; Bibl. 21 ref.</SO>
<LA>Anglais</LA>
<EA>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.</EA>
<CC>002B17; 002B16B; 002B25J01</CC>
<FD>Système nerveux pathologie; Parkinson maladie; Affinement; Noyau sousthalamique; Ciblage; Prise décision; Racine carrée; Inférence; Microélectrode; Localisation; Stimulation cérébrale profonde</FD>
<FG>Encéphale pathologie; Système nerveux central; Extrapyramidal syndrome; Maladie dégénérative; Système nerveux central pathologie</FG>
<ED>Nervous system diseases; Parkinson disease; Refinement; Subthalamic nucleus; Targeting; Decision making; Square root; Inference; Microelectrode; Localization; Deep brain stimulation</ED>
<EG>Cerebral disorder; Central nervous system; Extrapyramidal syndrome; Degenerative disease; Central nervous system disease</EG>
<SD>Sistema nervioso patología; Parkinson enfermedad; Afinamiento; Núcleo subtalámico; Blancado; Toma decision; Raíz cuadrada; Inferencia; Microeléctrodo; Localización</SD>
<LO>INIST-20953.354000158780860190</LO>
<ID>06-0518097</ID>
</server>
</inist>
</record>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Santé/explor/MovDisordV3/Data/PascalFrancis/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 001999 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/PascalFrancis/Corpus/biblio.hfd -nk 001999 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien |wiki= Wicri/Santé |area= MovDisordV3 |flux= PascalFrancis |étape= Corpus |type= RBID |clé= Pascal:06-0518097 |texte= Real-time refinement of subthalamic nucleus targeting using bayesian decision-making on the root mean square measure }}
![]() | This area was generated with Dilib version V0.6.23. | ![]() |