Real‐time refinement of subthalamic nucleus targeting using Bayesian decision‐making on the root mean square measure
Identifieur interne : 003222 ( Main/Exploration ); 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
Affiliations:
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Le document en format XML
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<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>
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<term>Refinement</term>
<term>Software</term>
<term>Square root</term>
<term>Stereotaxic Techniques (instrumentation)</term>
<term>Subthalamic Nucleus (physiopathology)</term>
<term>Subthalamic nucleus</term>
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<term>Targeting</term>
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<term>microelectrode recording</term>
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<term>Humans</term>
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<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>
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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. © 2006 Movement Disorder Society</div>
</front>
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