Differentiating Early and Late Stage Parkinson's Disease Patients from Healthy Controls

Authors

  • Deborah Mudali Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, The Netherlands;
  • Michael Biehl University of Groningen, University Medical Center Groningen, Neuroimaging Center, The Netherlands
  • Sanne Meles Department of Neurology, University Medical Center Groningen, University of Groningen, The Netherlands
  • Remco Renken University of Groningen, University Medical Center Groningen, Neuroimaging Center, The Netherlands
  • David Garcia Garcia Neurosciences Area, CIMA, Department of Neurology and Neurosurgery, Cl´ınica Universidad de Navarra, University of Navarra, Pamplona, Spain; Departamento de Bioingenier´ıa e Ingenier´ıa Aeroespacial. Universidad Carlos III de Madrid. Instituto de Investigaci´on Sanitaria Gregorio Mara˜non. Madrid. Spain;
  • Pedro Clavero Neurosciences Area, CIMA, Department of Neurology and Neurosurgery, Cl´ınica Universidad de Navarra, University of Navarra, Pamplona, Spain; Departamento de Bioingenier´ıa e Ingenier´ıa Aeroespacial. Universidad Carlos III de Madrid. Instituto de Investigaci´on Sanitaria Gregorio Mara˜non. Madrid. Spain;
  • Javier Arbizu Neurosciences Area, CIMA, Department of Neurology and Neurosurgery, Cl´ınica Universidad de Navarra, University of Navarra, Pamplona, Spain;
  • Jose A Obeso Neurosciences Area, CIMA, Department of Neurology and Neurosurgery, Cl´ınica Universidad de Navarra, University of Navarra, Pamplona, Spain; Centro de Investigaci´on Biom´edica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain; CINAC, HM Puerta del Sur, Hospitales de Madrid, and Medical School, CEU-San Pablo University, Madrid, Spain;
  • M.C. Rodriguez Oroz Neurosciences Area, CIMA, Department of Neurology and Neurosurgery, Cl´ınica Universidad de Navarra, University of Navarra, Pamplona, Spain; Centro de Investigaci´on Biom´edica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain; Department of Neurology and Neuroscience, University Hospital Donostia, BioDonostia Research Institute, SanSebastian; Ikerbasque, Basque Foundation for Science, Bilbao, Spain;
  • Klaus L Leenders Department of Neurology, University Medical Center Groningen, University of Groningen, The Netherlands;
  • Jos B.T.M. Roerdink Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, The Netherlands; University of Groningen, University Medical Center Groningen, Neuroimaging Center, The Netherlands;

DOI:

https://doi.org/10.14738/jbemi.36.2280

Keywords:

Parkinson’s disease, SSM/PCA, decision tree classification

Abstract

Parkinson’s disease (PD) is a neurodegenerative disease which is difficult to diagnose at an early stage. Brain imaging techniques like [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) may aid to identify disease-related changes in cerebral glucose metabolism. The scaled subprofile model with principal component analysis (SSM/PCA) is applied to FDG-PET data to extract features and corresponding patterns of glucose metabolism which can be used to  distinguish PD subjects from healthy controls. From a previous study, the decision tree (DT) classifier’s performance to separate the PD group from healthy controls was below chance level.  This could be attributed to the small number of subjects in the dataset, combined with the early disease progression. In this study, we make use of an additional PD dataset, consisting of subject brain images obtained at a later disease stage. The features extracted by the SSM/PCA method are used for distinguishing PD subjects from healthy controls using three classification methods,  that is, decision trees, Generalized Matrix Learning Vector Quantization (GMLVQ), and Support  Vector Machine (SVM) with linear kernel. The classifiers are validated to determine their capability of classification given new subject data. We compare the classifiers’ performances on  the distinct early-stage and late-stage datasets, as well on the combined datasets. We also use  the early and late-stage datasets interchangeably for training and testing the classifiers. We find  that the DT classification performance on the late-stage dataset is considerably better than in the  previous study, where we used early-stage data. For early-stage patients, the application of the  GMLVQ and SVM classifiers gives a significant improvement as compared to the DT classifier.

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Published

2016-12-30

How to Cite

Mudali, D., Biehl, M., Meles, S., Renken, R., Garcia Garcia, D., Clavero, P., Arbizu, J., Obeso, J. A., Oroz, M. R., Leenders, K. L., & Roerdink, J. B. (2016). Differentiating Early and Late Stage Parkinson’s Disease Patients from Healthy Controls. Journal of Biomedical Engineering and Medical Imaging, 3(6), 33. https://doi.org/10.14738/jbemi.36.2280

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