Applications of neural network analyses to in vivo 1H magnetic resonance spectroscopy of Parkinson disease patients
Identifieur interne : 001935 ( Main/Exploration ); précédent : 001934; suivant : 001936Applications of neural network analyses to in vivo 1H magnetic resonance spectroscopy of Parkinson disease patients
Auteurs : David Axelson [Norvège, Canada] ; Inger Johanne Bakken [Norvège] ; Ingrid Susann Gribbestad [Norvège] ; Benny Ehrnholm [Norvège] ; Gunnar Nilsen [Norvège] ; Jan Aasly [Norvège]Source :
- Journal of Magnetic Resonance Imaging [ 1053-1807 ] ; 2002-07.
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Abstract
Purpose: To apply neural network analyses to in vivo magnetic resonance spectra of controls and Parkinson disease (PD) patients for the purpose of classification. Materials and Methods: Ninety‐seven in vivo proton magnetic resonance spectra of the basal ganglia were recorded from 31 patients with (PD) and 14 age‐matched healthy volunteers on a 1.5‐T imager. The PD patients were grouped as follows: probable PD (N = 15), possible PD (N = 11), and atypical PD (N = 5). Total acquisition times of approximately five minutes were achieved with a TE (echo time) of 135 msec, a TR (repetition time) of 2000 msec, and 128 scan averages. Neural network (back propagation, Kohonen, probabilistic, and radial basis function) and related (generative topographic mapping) data analyses were performed. Results: Conventional data analysis showed no statistically significant differences in metabolite ratios based on measuring signal intensities. The trained networks could distinguish control from PD with considerable accuracy (true positive fraction 0.971, true negative fraction 0.933). When four classes were defined, approximately 88% of the predictions were correct. The multivariate analysis indicated metabolic changes in the basal ganglia in PD. Conclusion: A variety of neural network and related approaches can be successfully applied to both qualitative visualization and classification of in vivo spectra of PD patients. J. Magn. Reson. Imaging 2002;16:13–20. © 2002 Wiley ‐Liss, Inc.
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DOI: 10.1002/jmri.10125
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nuts="2">Trøndelag</region></placeName></affiliation></author></analytic><monogr></monogr><series><title level="j">Journal of Magnetic Resonance Imaging</title><title level="j" type="sub">An Official Journal of the International Society for Magnetic Resonance in Medicine</title><title level="j" type="abbrev">J. Magn. Reson. Imaging</title><idno type="ISSN">1053-1807</idno><idno type="eISSN">1522-2586</idno><imprint><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><pubPlace>New York</pubPlace><date type="published" when="2002-07">2002-07</date><biblScope unit="volume">16</biblScope><biblScope unit="issue">1</biblScope><biblScope unit="page" from="13">13</biblScope><biblScope unit="page" to="20">20</biblScope></imprint><idno type="ISSN">1053-1807</idno></series><idno type="istex">F2687287E20BF648A546E546EA08D199B7391AA3</idno><idno type="DOI">10.1002/jmri.10125</idno><idno type="ArticleID">JMRI10125</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1053-1807</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Parkinson disease</term><term>artificial neural network</term><term>in vivo</term><term>multivariate analysis</term><term>proton MR spectroscopy</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Purpose: To apply neural network analyses to in vivo magnetic resonance spectra of controls and Parkinson disease (PD) patients for the purpose of classification. Materials and Methods: Ninety‐seven in vivo proton magnetic resonance spectra of the basal ganglia were recorded from 31 patients with (PD) and 14 age‐matched healthy volunteers on a 1.5‐T imager. The PD patients were grouped as follows: probable PD (N = 15), possible PD (N = 11), and atypical PD (N = 5). Total acquisition times of approximately five minutes were achieved with a TE (echo time) of 135 msec, a TR (repetition time) of 2000 msec, and 128 scan averages. Neural network (back propagation, Kohonen, probabilistic, and radial basis function) and related (generative topographic mapping) data analyses were performed. Results: Conventional data analysis showed no statistically significant differences in metabolite ratios based on measuring signal intensities. The trained networks could distinguish control from PD with considerable accuracy (true positive fraction 0.971, true negative fraction 0.933). When four classes were defined, approximately 88% of the predictions were correct. The multivariate analysis indicated metabolic changes in the basal ganglia in PD. Conclusion: A variety of neural network and related approaches can be successfully applied to both qualitative visualization and classification of in vivo spectra of PD patients. J. Magn. Reson. 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