Delimiting subterritories of the human subthalamic nucleus by means of microelectrode recordings and a Hidden Markov Model.
Identifieur interne : 001C48 ( PubMed/Curation ); précédent : 001C47; suivant : 001C49Delimiting subterritories of the human subthalamic nucleus by means of microelectrode recordings and a Hidden Markov Model.
Auteurs : Adam Zaidel [Israël] ; Alexander Spivak ; Lavi Shpigelman ; Hagai Bergman ; Zvi IsraelSource :
- Movement disorders : official journal of the Movement Disorder Society [ 1531-8257 ] ; 2009.
English descriptors
- KwdEn :
- MESH :
- methods : Deep Brain Stimulation.
- physiology : Action Potentials, Subthalamic Nucleus.
- therapy : Parkinson Disease.
- Aged, Algorithms, Beta Rhythm, Electrodes, Implanted, Female, Humans, Male, Markov Chains, Microelectrodes, Middle Aged, Reproducibility of Results, Spectrum Analysis.
Abstract
Positive therapeutic response without adverse side effects to subthalamic nucleus deep brain stimulation (STN DBS) for Parkinson's disease (PD) depends to a large extent on electrode location within the STN. The sensorimotor region of the STN (seemingly the preferred location for STN DBS) lies dorsolaterally, in a region also marked by distinct beta (13-30 Hz) oscillations in the parkinsonian state. In this study, we present a real-time method to accurately demarcate subterritories of the STN during surgery, based on microelectrode recordings (MERs) and a Hidden Markov Model (HMM). Fifty-six MER trajectories were used, obtained from 21 PD patients who underwent bilateral STN DBS implantation surgery. Root mean square (RMS) and power spectral density (PSD) of the MERs were used to train and test an HMM in identifying the dorsolateral oscillatory region (DLOR) and nonoscillatory subterritories within the STN. The HMM demarcations were compared to the decisions of a human expert. The HMM identified STN-entry, the ventral boundary of the DLOR, and STN-exit with an error of -0.09 +/- 0.35, -0.27 +/- 0.58, and -0.20 +/- 0.33 mm, respectively (mean +/- standard deviation), and with detection reliability (error < 1 mm) of 95, 86, and 91%, respectively. The HMM was successful despite a very coarse clustering method and was robust to parameter variation. Thus, using an HMM in conjunction with RMS and PSD measures of intraoperative MER can provide improved refinement of STN entry and exit in comparison with previously reported automatic methods, and introduces a novel (intra-STN) detection of a distinct DLOR-ventral boundary.
DOI: 10.1002/mds.22674
PubMed: 19533755
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The sensorimotor region of the STN (seemingly the preferred location for STN DBS) lies dorsolaterally, in a region also marked by distinct beta (13-30 Hz) oscillations in the parkinsonian state. In this study, we present a real-time method to accurately demarcate subterritories of the STN during surgery, based on microelectrode recordings (MERs) and a Hidden Markov Model (HMM). Fifty-six MER trajectories were used, obtained from 21 PD patients who underwent bilateral STN DBS implantation surgery. Root mean square (RMS) and power spectral density (PSD) of the MERs were used to train and test an HMM in identifying the dorsolateral oscillatory region (DLOR) and nonoscillatory subterritories within the STN. The HMM demarcations were compared to the decisions of a human expert. The HMM identified STN-entry, the ventral boundary of the DLOR, and STN-exit with an error of -0.09 +/- 0.35, -0.27 +/- 0.58, and -0.20 +/- 0.33 mm, respectively (mean +/- standard deviation), and with detection reliability (error < 1 mm) of 95, 86, and 91%, respectively. The HMM was successful despite a very coarse clustering method and was robust to parameter variation. Thus, using an HMM in conjunction with RMS and PSD measures of intraoperative MER can provide improved refinement of STN entry and exit in comparison with previously reported automatic methods, and introduces a novel (intra-STN) detection of a distinct DLOR-ventral boundary.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">19533755</PMID><DateCreated><Year>2009</Year><Month>09</Month><Day>17</Day></DateCreated><DateCompleted><Year>2009</Year><Month>12</Month><Day>04</Day></DateCompleted><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1531-8257</ISSN><JournalIssue CitedMedium="Internet"><Volume>24</Volume><Issue>12</Issue><PubDate><Year>2009</Year><Month>Sep</Month><Day>15</Day></PubDate></JournalIssue><Title>Movement disorders : official journal of the Movement Disorder Society</Title><ISOAbbreviation>Mov. Disord.</ISOAbbreviation></Journal><ArticleTitle>Delimiting subterritories of the human subthalamic nucleus by means of microelectrode recordings and a Hidden Markov Model.</ArticleTitle><Pagination><MedlinePgn>1785-93</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1002/mds.22674</ELocationID><Abstract><AbstractText>Positive therapeutic response without adverse side effects to subthalamic nucleus deep brain stimulation (STN DBS) for Parkinson's disease (PD) depends to a large extent on electrode location within the STN. The sensorimotor region of the STN (seemingly the preferred location for STN DBS) lies dorsolaterally, in a region also marked by distinct beta (13-30 Hz) oscillations in the parkinsonian state. In this study, we present a real-time method to accurately demarcate subterritories of the STN during surgery, based on microelectrode recordings (MERs) and a Hidden Markov Model (HMM). Fifty-six MER trajectories were used, obtained from 21 PD patients who underwent bilateral STN DBS implantation surgery. Root mean square (RMS) and power spectral density (PSD) of the MERs were used to train and test an HMM in identifying the dorsolateral oscillatory region (DLOR) and nonoscillatory subterritories within the STN. The HMM demarcations were compared to the decisions of a human expert. The HMM identified STN-entry, the ventral boundary of the DLOR, and STN-exit with an error of -0.09 +/- 0.35, -0.27 +/- 0.58, and -0.20 +/- 0.33 mm, respectively (mean +/- standard deviation), and with detection reliability (error < 1 mm) of 95, 86, and 91%, respectively. The HMM was successful despite a very coarse clustering method and was robust to parameter variation. 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