MovDisordV3, PascalFrancis, Corpus, bibRecord, 001999

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 : 001A00

Real-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 Israel

Source :

Movement disorders [ 0885-3185 ] ; 2006.

RBID : Pascal:06-0518097

Descripteurs français

Pascal (Inist)
- Système nerveux pathologie, Parkinson maladie, Affinement, Noyau sousthalamique, Ciblage, Prise décision, Racine carrée, Inférence, Microélectrode, Localisation, Stimulation cérébrale profonde.

English descriptors

KwdEn :
- Decision making, Deep brain stimulation, Inference, Localization, Microelectrode, Nervous system diseases, Parkinson disease, Refinement, Square root, Subthalamic nucleus, Targeting.

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.

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C01	`01`		`ENG`	@0 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.
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C03	`06`	`X`	`SPA`	`@0 Toma decision @5 12`
C03	`07`	`X`	`FRE`	`@0 Racine carrée @5 13`
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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:06-0518097

Le document en format XML

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<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>
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<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>

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	Movement Disorders (revue)
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Movement Disorders (revue)

Real-time refinement of subthalamic nucleus targeting using bayesian decision-making on the root mean square measure

Real-time refinement of subthalamic nucleus targeting using bayesian decision-making on the root mean square measure

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