Supporting clinical decision making during deep brain stimulation surgery by means of a stochastic dynamical model.
Identifieur interne : 001845 ( PubMed/Corpus ); précédent : 001844; suivant : 001846Supporting clinical decision making during deep brain stimulation surgery by means of a stochastic dynamical model.
Auteurs : Sofia D. Karamintziou ; George L. Tsirogiannis ; Pantelis G. Stathis ; George A. Tagaris ; Efstathios J. Boviatsis ; Damianos E. Sakas ; Konstantina S. NikitaSource :
- Journal of neural engineering [ 1741-2552 ] ; 2014.
English descriptors
- KwdEn :
- Aged, Computer Simulation, Decision Support Systems, Clinical, Deep Brain Stimulation (instrumentation), Deep Brain Stimulation (methods), Electrodes, Implanted, Female, Humans, Male, Middle Aged, Models, Neurological, Models, Statistical, Monitoring, Intraoperative (methods), Parkinson Disease (physiopathology), Parkinson Disease (therapy), Prosthesis Implantation (methods), Stochastic Processes, Subthalamic Nucleus (physiopathology), Subthalamic Nucleus (surgery), Treatment Outcome.
- MESH :
- instrumentation : Deep Brain Stimulation.
- methods : Deep Brain Stimulation, Monitoring, Intraoperative, Prosthesis Implantation.
- physiopathology : Parkinson Disease, Subthalamic Nucleus.
- surgery : Subthalamic Nucleus.
- therapy : Parkinson Disease.
- Aged, Computer Simulation, Decision Support Systems, Clinical, Electrodes, Implanted, Female, Humans, Male, Middle Aged, Models, Neurological, Models, Statistical, Stochastic Processes, Treatment Outcome.
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
Links to Exploration step
pubmed:25241917Le document en format XML
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<author><name sortKey="Karamintziou, Sofia D" sort="Karamintziou, Sofia D" uniqKey="Karamintziou S" first="Sofia D" last="Karamintziou">Sofia D. Karamintziou</name>
<affiliation><nlm:affiliation>Biomedical Simulations and Imaging Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str., 15780 Athens, Greece.</nlm:affiliation>
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<author><name sortKey="Tsirogiannis, George L" sort="Tsirogiannis, George L" uniqKey="Tsirogiannis G" first="George L" last="Tsirogiannis">George L. Tsirogiannis</name>
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<author><name sortKey="Stathis, Pantelis G" sort="Stathis, Pantelis G" uniqKey="Stathis P" first="Pantelis G" last="Stathis">Pantelis G. Stathis</name>
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<author><name sortKey="Tagaris, George A" sort="Tagaris, George A" uniqKey="Tagaris G" first="George A" last="Tagaris">George A. Tagaris</name>
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<author><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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<author><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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<author><name sortKey="Nikita, Konstantina S" sort="Nikita, Konstantina S" uniqKey="Nikita K" first="Konstantina S" last="Nikita">Konstantina S. Nikita</name>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">Supporting clinical decision making during deep brain stimulation surgery by means of a stochastic dynamical model.</title>
<author><name sortKey="Karamintziou, Sofia D" sort="Karamintziou, Sofia D" uniqKey="Karamintziou S" first="Sofia D" last="Karamintziou">Sofia D. Karamintziou</name>
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<author><name sortKey="Stathis, Pantelis G" sort="Stathis, Pantelis G" uniqKey="Stathis P" first="Pantelis G" last="Stathis">Pantelis G. Stathis</name>
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<author><name sortKey="Tagaris, George A" sort="Tagaris, George A" uniqKey="Tagaris G" first="George A" last="Tagaris">George A. Tagaris</name>
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<author><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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<author><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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<author><name sortKey="Nikita, Konstantina S" sort="Nikita, Konstantina S" uniqKey="Nikita K" first="Konstantina S" last="Nikita">Konstantina S. Nikita</name>
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<series><title level="j">Journal of neural engineering</title>
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<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>
<keywords scheme="MESH" qualifier="instrumentation" xml:lang="en"><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="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>
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<keywords scheme="MESH" qualifier="therapy" xml:lang="en"><term>Parkinson Disease</term>
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<term>Computer Simulation</term>
<term>Decision Support Systems, Clinical</term>
<term>Electrodes, Implanted</term>
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<term>Male</term>
<term>Middle Aged</term>
<term>Models, Neurological</term>
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<term>Treatment Outcome</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><Year>2015</Year>
<Month>06</Month>
<Day>05</Day>
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<DateRevised><Year>2014</Year>
<Month>09</Month>
<Day>23</Day>
</DateRevised>
<Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1741-2552</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>11</Volume>
<Issue>5</Issue>
<PubDate><Year>2014</Year>
<Month>Oct</Month>
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<Title>Journal of neural engineering</Title>
<ISOAbbreviation>J Neural Eng</ISOAbbreviation>
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<ArticleTitle>Supporting clinical decision making during deep brain stimulation surgery by means of a stochastic dynamical model.</ArticleTitle>
<Pagination><MedlinePgn>056019</MedlinePgn>
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<Abstract><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>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Karamintziou</LastName>
<ForeName>Sofia D</ForeName>
<Initials>SD</Initials>
<AffiliationInfo><Affiliation>Biomedical Simulations and Imaging Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str., 15780 Athens, Greece.</Affiliation>
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<Author ValidYN="Y"><LastName>Tsirogiannis</LastName>
<ForeName>George L</ForeName>
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<Author ValidYN="Y"><LastName>Stathis</LastName>
<ForeName>Pantelis G</ForeName>
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<Author ValidYN="Y"><LastName>Tagaris</LastName>
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<Author ValidYN="Y"><LastName>Boviatsis</LastName>
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<Author ValidYN="Y"><LastName>Sakas</LastName>
<ForeName>Damianos E</ForeName>
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<Author ValidYN="Y"><LastName>Nikita</LastName>
<ForeName>Konstantina S</ForeName>
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<Language>eng</Language>
<PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType>
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<ArticleDate DateType="Electronic"><Year>2014</Year>
<Month>09</Month>
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<MedlineTA>J Neural Eng</MedlineTA>
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