Serveur d'exploration MERS

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Supporting clinical decision making during deep brain stimulation surgery by means of a stochastic dynamical model.

Identifieur interne : 000F04 ( Ncbi/Merge ); précédent : 000F03; suivant : 000F05

Supporting clinical decision making during deep brain stimulation surgery by means of a stochastic dynamical model.

Auteurs : Sofia D. Karamintziou [Grèce] ; George L. Tsirogiannis ; Pantelis G. Stathis ; George A. Tagaris ; Efstathios J. Boviatsis ; Damianos E. Sakas ; Konstantina S. Nikita

Source :

RBID : pubmed:25241917

Descripteurs français

English descriptors

Abstract

During deep brain stimulation (DBS) surgery for the treatment of advanced Parkinson's disease (PD), microelectrode recording (MER) in conjunction with functional stimulation techniques are commonly applied for accurate electrode implantation. However, the development of automatic methods for clinical decision making has to date been characterized by the absence of a robust single-biomarker approach. Moreover, it has only been restricted to the framework of MER without encompassing intraoperative macrostimulation. Here, we propose an integrated series of novel single-biomarker approaches applicable to the entire electrophysiological procedure by means of a stochastic dynamical model.

DOI: 10.1088/1741-2560/11/5/056019
PubMed: 25241917

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pubmed:25241917

Le document en format XML

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<name sortKey="Boviatsis, Efstathios J" sort="Boviatsis, Efstathios J" uniqKey="Boviatsis E" first="Efstathios J" last="Boviatsis">Efstathios J. Boviatsis</name>
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<name sortKey="Sakas, Damianos E" sort="Sakas, Damianos E" uniqKey="Sakas D" first="Damianos E" last="Sakas">Damianos E. Sakas</name>
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<term>Aged</term>
<term>Computer Simulation</term>
<term>Decision Support Systems, Clinical</term>
<term>Deep Brain Stimulation (instrumentation)</term>
<term>Deep Brain Stimulation (methods)</term>
<term>Electrodes, Implanted</term>
<term>Female</term>
<term>Humans</term>
<term>Male</term>
<term>Middle Aged</term>
<term>Models, Neurological</term>
<term>Models, Statistical</term>
<term>Monitoring, Intraoperative (methods)</term>
<term>Parkinson Disease (physiopathology)</term>
<term>Parkinson Disease (therapy)</term>
<term>Prosthesis Implantation (methods)</term>
<term>Stochastic Processes</term>
<term>Subthalamic Nucleus (physiopathology)</term>
<term>Subthalamic Nucleus (surgery)</term>
<term>Treatment Outcome</term>
</keywords>
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<term>Adulte d'âge moyen</term>
<term>Femelle</term>
<term>Humains</term>
<term>Implantation de prothèse ()</term>
<term>Maladie de Parkinson ()</term>
<term>Maladie de Parkinson (physiopathologie)</term>
<term>Modèles neurologiques</term>
<term>Modèles statistiques</term>
<term>Mâle</term>
<term>Noyau subthalamique ()</term>
<term>Noyau subthalamique (physiopathologie)</term>
<term>Processus stochastiques</term>
<term>Résultat thérapeutique</term>
<term>Simulation numérique</term>
<term>Stimulation cérébrale profonde ()</term>
<term>Stimulation cérébrale profonde (instrumentation)</term>
<term>Sujet âgé</term>
<term>Surveillance peropératoire ()</term>
<term>Systèmes d'aide à la décision clinique</term>
<term>Électrodes implantées</term>
</keywords>
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<term>Deep Brain Stimulation</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en">
<term>Deep Brain Stimulation</term>
<term>Monitoring, Intraoperative</term>
<term>Prosthesis Implantation</term>
</keywords>
<keywords scheme="MESH" qualifier="physiopathologie" xml:lang="fr">
<term>Maladie de Parkinson</term>
<term>Noyau subthalamique</term>
</keywords>
<keywords scheme="MESH" qualifier="physiopathology" xml:lang="en">
<term>Parkinson Disease</term>
<term>Subthalamic Nucleus</term>
</keywords>
<keywords scheme="MESH" qualifier="surgery" xml:lang="en">
<term>Subthalamic Nucleus</term>
</keywords>
<keywords scheme="MESH" qualifier="therapy" xml:lang="en">
<term>Parkinson Disease</term>
</keywords>
<keywords scheme="MESH" xml:lang="en">
<term>Aged</term>
<term>Computer Simulation</term>
<term>Decision Support Systems, Clinical</term>
<term>Electrodes, Implanted</term>
<term>Female</term>
<term>Humans</term>
<term>Male</term>
<term>Middle Aged</term>
<term>Models, Neurological</term>
<term>Models, Statistical</term>
<term>Stochastic Processes</term>
<term>Treatment Outcome</term>
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<term>Adulte d'âge moyen</term>
<term>Femelle</term>
<term>Humains</term>
<term>Implantation de prothèse</term>
<term>Maladie de Parkinson</term>
<term>Modèles neurologiques</term>
<term>Modèles statistiques</term>
<term>Mâle</term>
<term>Noyau subthalamique</term>
<term>Processus stochastiques</term>
<term>Résultat thérapeutique</term>
<term>Simulation numérique</term>
<term>Stimulation cérébrale profonde</term>
<term>Sujet âgé</term>
<term>Surveillance peropératoire</term>
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<front>
<div type="abstract" xml:lang="en">During deep brain stimulation (DBS) surgery for the treatment of advanced Parkinson's disease (PD), microelectrode recording (MER) in conjunction with functional stimulation techniques are commonly applied for accurate electrode implantation. However, the development of automatic methods for clinical decision making has to date been characterized by the absence of a robust single-biomarker approach. Moreover, it has only been restricted to the framework of MER without encompassing intraoperative macrostimulation. Here, we propose an integrated series of novel single-biomarker approaches applicable to the entire electrophysiological procedure by means of a stochastic dynamical model.</div>
</front>
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<DateCompleted>
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<Month>06</Month>
<Day>05</Day>
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<Year>2014</Year>
<Month>09</Month>
<Day>23</Day>
</DateRevised>
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<ISSN IssnType="Electronic">1741-2552</ISSN>
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<Volume>11</Volume>
<Issue>5</Issue>
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<Title>Journal of neural engineering</Title>
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<ArticleTitle>Supporting clinical decision making during deep brain stimulation surgery by means of a stochastic dynamical model.</ArticleTitle>
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<AbstractText Label="OBJECTIVE" NlmCategory="OBJECTIVE">During deep brain stimulation (DBS) surgery for the treatment of advanced Parkinson's disease (PD), microelectrode recording (MER) in conjunction with functional stimulation techniques are commonly applied for accurate electrode implantation. However, the development of automatic methods for clinical decision making has to date been characterized by the absence of a robust single-biomarker approach. Moreover, it has only been restricted to the framework of MER without encompassing intraoperative macrostimulation. Here, we propose an integrated series of novel single-biomarker approaches applicable to the entire electrophysiological procedure by means of a stochastic dynamical model.</AbstractText>
<AbstractText Label="APPROACH" NlmCategory="METHODS">The methods are applied to MER data pertinent to ten DBS procedures. Considering the presence of measurement noise, we initially employ a multivariate phase synchronization index for automatic delineation of the functional boundaries of the subthalamic nucleus (STN) and determination of the acceptable MER trajectories. By introducing the index into a nonlinear stochastic model, appropriately fitted to pre-selected MERs, we simulate the neuronal response to periodic stimuli (130 Hz), and examine the Lyapunov exponent as an indirect indicator of the clinical effectiveness yielded by stimulation at the corresponding sites.</AbstractText>
<AbstractText Label="MAIN RESULTS" NlmCategory="RESULTS">Compared with the gold-standard dataset of annotations made intraoperatively by clinical experts, the STN detection methodology demonstrates a false negative rate of 4.8% and a false positive rate of 0%, across all trajectories. Site eligibility for implantation of the DBS electrode, as implicitly determined through the Lyapunov exponent of the proposed stochastic model, displays a sensitivity of 71.43%.</AbstractText>
<AbstractText Label="SIGNIFICANCE" NlmCategory="CONCLUSIONS">The suggested comprehensive method exhibits remarkable performance in automatically determining both the acceptable MER trajectories and the optimal stimulation sites, thereby having the potential to accelerate precise target finalization during DBS surgery for PD.</AbstractText>
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<Year>2014</Year>
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