Automated Atlas Fitting for Deep Brain Stimulation Surgery Based on Microelectrode Neuronal Recordings
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064165%3A_____%2F19%3A10384075" target="_blank" >RIV/00064165:_____/19:10384075 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21230/19:00322045 RIV/00216208:11110/19:10384075 RIV/00023752:_____/19:43919638
Result on the web
<a href="https://doi.org/10.1007/978-981-10-9023-3_19" target="_blank" >https://doi.org/10.1007/978-981-10-9023-3_19</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-981-10-9023-3_19" target="_blank" >10.1007/978-981-10-9023-3_19</a>
Alternative languages
Result language
angličtina
Original language name
Automated Atlas Fitting for Deep Brain Stimulation Surgery Based on Microelectrode Neuronal Recordings
Original language description
Introduction: The deep brain stimulation (DBS) is a treatment technique for late-stage Parkinson's disease (PD), based on chronic electrical stimulation of neural tissue through implanted electrodes. To achieve high level of symptom suppression with low side effects, precise electrode placement is necessary, although difficult due to small size of the target nucleus and various sources of inaccuracy, especially brain shift and electrode bending. To increase accuracy of electrode placement, electrophysiological recording using several parallel microelectrodes (MER) is used intraoperatively in most centers. Location of the target nucleus is identified from manual expert evaluation of characteristic neuronal activity. Existing studies have presented several models to classify individual recordings or trajectories automatically. In this study, we extend this approach by fitting a 3D anatomical atlas to the recorded electrophysiological activity, thus adding topological information. Methods: We developed a probabilistic model of neuronal activity in the vicinity the subthalamic nucleus (STN), based on normalized signal energy. The model is used to find a maximum-likelihood transformation of an anatomical surface-based atlas to the recorded activity. The resulting atlas fit is compared to atlas position estimated from pre-operative MRI scans. Accuracy of STN classification is then evaluated in a leave-one-subject-out scenario using expert MER annotation. Results: In an evaluation on a set of 27 multi-electrode trajectories from 15 PD patients, the proposed method showed higher accuracy in STN-nonSTN classification (88.1%) compared to the reference methods (78.7%) with an even more pronounced advantage in sensitivity (69.0% vs 44.6%). Conclusion: The proposed method allows electrophysiology-based refinement of atlas position of the STN and represents a promising direction in refining accuracy of MER localization in clinical DBS setting, as well as in research of DBS mechanisms.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
30103 - Neurosciences (including psychophysiology)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
World Congress on Medical Physics and Biomedical Engineering 2018
ISBN
978-981-10-9022-6
ISSN
1680-0737
e-ISSN
—
Number of pages
7
Pages from-to
105-111
Publisher name
Springer
Place of publication
Singapore
Event location
Prague
Event date
Jun 3, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000449744300019