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Applications of neural network analyses to in vivo 1 H magnetic resonance spectroscopy of Parkinson disease patients
Author(s) -
Axelson David,
Bakken Inger Johanne,
Susann Gribbestad Ingrid,
Ehrnholm Benny,
Nilsen Gunnar,
Aasly Jan
Publication year - 2002
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.10125
Subject(s) - in vivo , magnetic resonance imaging , nuclear magnetic resonance , basal ganglia , proton magnetic resonance , nuclear medicine , parkinson's disease , echo time , pattern recognition (psychology) , medicine , pathology , artificial intelligence , radiology , computer science , physics , biology , disease , central nervous system , microbiology and biotechnology
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.