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Accuracy to Differentiate Mild Cognitive Impairment in Parkinson’s Disease Using Cortical Features

  • Juan-Miguel Morales
  • Rafael Rodriguez
  • Maylen Carballo
  • Karla Batista
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)

Abstract

Mild cognitive impairment (MCI) is common in Parkinson’s Disease (PD) patients and it is key to predict the development of dementia. There is not report of discriminant accuracy for MCI using based-surface cortical morphometry. This study used Cortical-Thickness (CT) combined to Local-Gyrification-Index (LGI) to assess discriminant accuracy for MCI stages in PD. Sixty-four patients with idiopathic PD and nineteen healthy controls (HC) were analyzed. CT and LGI were estimated using Freesurfer software. Principal Component Analysis and Lineal Discriminant Analysis (LDA) assuming a common diagonal covariance matrix (or Naive-Bayes classifier) was used with cross-validation leave-one-subject-out scheme. Accuracy, sensibility and specificity were reported to different classification analysis. CT combined to LGI limited revealed the best discrimination with accuracy of 82,98%, sensitivity of 85.71% and specificity of 80.77%. A validation process using independent and more heterogeneous data set and further longitudinal studies, are necessary to confirm our results.

Keywords

Naive-Bayes classifier PCA Accuracy Parkinson’s disease MCI Cortical Thickness Cortical Folding LGI MRI Surface-based morphometry 

References

  1. 1.
    Pedersen, K.F., Larsen, J.P., Tysnes, O.B., Alves, G.: Prognosis of Mild Cognitive Impairment in Early Parkinson Disease: The Norwegian ParkWest Study. JAMA Neurology 70(5), 580–586 (2013)CrossRefGoogle Scholar
  2. 2.
    Litvan, I., Aarsland, D., Adler, C., et al.: MDS task force on mild cognitive impairment in Parkinson’s disease: critical review of PD-MCI. Mov. Disord. 26, 1814–1824 (2011)CrossRefGoogle Scholar
  3. 3.
    Duncan, G.W., Firbank, M.J., O’Brien, J.T., Burn, D.: Magnetic resonance imaging: a biomarker for cognitive impairment in Parkinson’s disease? Movement Disorders: Official Journal of the Movement Disorder Society 28(4), 425–438 (2013)CrossRefGoogle Scholar
  4. 4.
    McGhee, D.J.M., Royle, P.L., Thompson, P.A., Wright, D.E., Zajicek, J.P., Counsell, C.E.: A systematic review of biomarkers for disease progression in Parkinson’s disease. BMC Neurology, 35 (2013)Google Scholar
  5. 5.
    Litvan, I., Goldman, J., Tröster, A., Schmand, B., et al.: Diagnostic criteria for mild cognitive impairment in Parkinson’s disease: Movement Disorder Society Task Force guidelines. Mov. Disord. 27, 349–356 (2012)CrossRefGoogle Scholar
  6. 6.
    McGill University Montreal Quebec (CA), assignee. Systems and Methods of Clinical State Prediction Utilizing Medial Images Data. US-7899225-B2 (2011)Google Scholar
  7. 7.
    Pagonabarraga, J., Corcuera-Solano, I., Vives-Gilabert, Y., Llebaria, G., García-Sánchez, C., Pascual-Sedano, B., et al.: Pattern of regional cortical thinning associated with cognitive deterioration in Parkinson’s disease. PloS One 8(1), e54980 (2013)CrossRefGoogle Scholar
  8. 8.
    Zarei, M., Ibarretxe-Bilbao, N., Compta, Y., Hough, M., Junque, C., Bargallo, N., et al.: Cortical thinning is associated with disease stages and dementia in Parkinson’s disease. Journal of Neurology, Neurosurgery, and Psychiatry (March 2013)Google Scholar
  9. 9.
    Winblad, B., Kivipelto, M., Jelic, V.: Mild cognitive impairment–beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. J. Intern. Med. 256(3), 240–246 (2004)CrossRefGoogle Scholar
  10. 10.
    Association AP. Diagnostic and Statistical Manual of Mental Disorders, 4th edn. American Psychiatric Association, Washington, DC (1994)Google Scholar
  11. 11.
    Segonne, F., Dale, A.M., Busa, E., Glessner, M., Salat, D., Hahn, H.K., et al.: A hybrid approach to the skull stripping problem in MRI. NeuroImage 22(3), 1060–1075 (2004)CrossRefGoogle Scholar
  12. 12.
    Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., et al.: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 33, 341–355 (2002)CrossRefGoogle Scholar
  13. 13.
    Salat, D., Buckner, R.L., Snyder, A.Z., Greve, D.N., Desikan, R.S., Busa, E., et al.: Thinning of the cerebral cortex in aging. Cerebral Cortex 14, 721–730 (2004)CrossRefGoogle Scholar
  14. 14.
    Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imag. 17, 87–97 (1998)CrossRefGoogle Scholar
  15. 15.
    Fischl, B., Liu, A., Dale, A.M.: Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Medical Imaging 20(1), 70–80 (2001)CrossRefGoogle Scholar
  16. 16.
    Segonne, F., Pacheco, J., Fischl, B.: Geometrically accurate topology-correction of cortical surfaces using nonseparating loops. IEEE Trans. Med. Imag. 26, 518–529 (2007)CrossRefGoogle Scholar
  17. 17.
    Fischl, B., Dale, A.M.: Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences of the United States of America 97(20), 11050–11055 (2000)CrossRefGoogle Scholar
  18. 18.
    Mea, S.: A surface-based approach to quantify local cortical gyrification. IEEE Trans. Med. Imag. 27, 161–170 (2008)CrossRefGoogle Scholar
  19. 19.
    Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Ségonne, F., Salat, D.H., et al.: Automatically Parcellating the Human Cerebral Cortex. Cerebral Cortex 14(1), 11–22 (2004)CrossRefGoogle Scholar
  20. 20.
    Pereira, J.B., Ibarretxe-Bilbao, N., Marti, M.J., Compta, Y., Junqué, C., Bargallo, N., et al.: Assessment of cortical degeneration in patients with parkinson’s disease by voxel-based morphometry, cortical folding, and cortical thickness. Human Brain Mapping (September 2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Juan-Miguel Morales
    • 1
  • Rafael Rodriguez
    • 1
  • Maylen Carballo
    • 1
  • Karla Batista
    • 1
  1. 1.Neuroimages Processing Group, International Center for Neurological RestaurationCuba

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