Rough Set Rules Help to Optimize Parameters of Deep Brain Stimulation in Parkinson’s Patients

  • Artur Szymański
  • Andrzej W. Przybyszewski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8609)


Deep brain stimulation (DBS) is a well established method used as treatment in patients with advanced Parkinson’s disease (PD). Our main purpose is to increase precision of DBS method by determining which parts of cortex are stimulated in different set-ups. In this paper we have analyzed MRIs that are performed as a standard procedure before and after the DBS surgery. We have used 3D Slicer for registration of MRIs with anatomical brain atlas. In addition, we have generated trajectories of neural tracts (tractography) connecting STN with cortex using data colected by DTI (Diffusion Tensor Imaging). In the following step we have used Rougt Set Theory to compare MRI data with neurological findings acquired by neurologists. We have tested prediction of DBS electrode contact’s position and stimulating parameters in individual patients on improvements of particular neurological symptoms. Our results may give a basis to set optimal parameters of stimulation and electrode’s position in order to obtain the most effective PD treatment.


Deep Brain Stimulation Parkinson’s disease 3D image analysis RSES MRI DTI 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aziz, T.Z., Peggs, D., Sambrook, M.A., Crossman, A.R.: Lesion of the subthalamic nucleus for the alleviation of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced parkinsonism in the primate. Mov. Disord. Off. J. Mov. Disord. Soc. 6, 288–292 (1991)CrossRefGoogle Scholar
  2. 2.
    Langston, J.W., Ballard, P., Tetrud, J.W., Irwin, I.: Chronic Parkinsonism in humans due to a product of meperidine-analog synthesis. Science 219, 979–980 (1983)CrossRefGoogle Scholar
  3. 3.
    Plaha, P., Ben-Shlomo, Y., Patel, N.K., Gill, S.S.: Stimulation of the caudal zona incerta is superior to stimulation of the subthalamic nucleus in improving contralateral parkinsonism. Brain 129, 1732–1747 (2006)CrossRefGoogle Scholar
  4. 4.
    Limousin, P., Pollak, P., Benazzouz, A., Hoffmann, D., Le Bas, J.-F., Perret, J.E., Benabid, A.-L., Broussolle, E.: Effect on parkinsonian signs and symptoms of bilateral subthalamic nucleus stimulation. The Lancet. 345, 91–95 (1995)CrossRefGoogle Scholar
  5. 5.
    Ciecierski, K., Raś, Z.W., Przybyszewski, A.W.: Selection of the Optimal Microelectrode during DBS Surgery in Parkinson’s Patients. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds.) ISMIS 2011. LNCS, vol. 6804, pp. 554–564. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  6. 6.
    Mallet, L., Schüpbach, M., N’Diaye, K., Remy, P., Bardinet, E., Czernecki, V., Welter, M.-L., Pelissolo, A., Ruberg, M., Agid, Y., Yelnik, J.: Stimulation of subterritories of the sub-thalamic nucleus reveals its role in the integration of the emotional and motor aspects of behavior. Proc. Natl. Acad. Sci. 104, 10661–10666 (2007)CrossRefGoogle Scholar
  7. 7.
    Lambert, C., Zrinzo, L., Nagy, Z., Lutti, A., Hariz, M., Foltynie, T., Draganski, B., Ash-burner, J., Frackowiak, R.: Confirmation of functional zones within the human subthalamic nucleus: patterns of connectivity and sub-parcellation using diffusion weighted imaging. NeuroImage 60, 83–94 (2012)CrossRefGoogle Scholar
  8. 8.
    Tosun, D., Rettmann, M.E., Prince, J.L.: Mapping techniques for aligning sulci across multiple brains. Med. Image Anal. 8, 295–309 (2004)CrossRefGoogle Scholar
  9. 9.
    Fischl, B., Sereno, M.I., Tootell, R.B., Dale, A.M.: High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8, 272–284 (1999)CrossRefGoogle Scholar
  10. 10.
    Lester, H., Arridge, S.R.: A survey of hierarchical non-linear medical image registration. Pattern Recognit. 32, 129–149 (1999)CrossRefGoogle Scholar
  11. 11.
    Talos, I.-F., Jakab, M., Kikinis, R., Shenton, M.E.: SPL-PNL Brain Atlas. SPL (2008)Google Scholar
  12. 12.
    Krauth, A., Blanc, R., Poveda, A., Jeanmonod, D., Morel, A., Székely, G.: A mean three-dimensional atlas of the human thalamus: Generation from multiple histological data. NeuroImage 49, 2053–2062 (2010)CrossRefGoogle Scholar
  13. 13.
    Cauda, F., Giuliano, G., Federico, D., Sergio, D., Katiuscia, S.: Discovering the soma-totopic organization of the motor areas of the medial wall using low-frequency BOLD fluctuations. Hum. Brain Mapp. 32, 1566–1579 (2011)CrossRefGoogle Scholar
  14. 14.
    Mayer, A.R., Zimbelman, J.L., Watanabe, Y., Rao, S.M.: Somatotopic organization of the medial wall of the cerebral hemispheres: a 3 Tesla fMRI study. Neuroreport 12, 3811–3814 (2001)CrossRefGoogle Scholar
  15. 15.
    Pawlak, Z.: Rough Set Theory and Its Applications to Data Analysis. Cybern. Syst. 29, 661–688 (1998)CrossRefzbMATHGoogle Scholar
  16. 16.
    Movement Disorder Society Task Force on Rating Scales for Parkinson’s Disease: The Unified Parkinson’s Disease Rating Scale (UPDRS): status and recommendations. Mov. Disord. Off. J. Mov. Disord. Soc. 18, 738–750 (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Artur Szymański
    • 1
  • Andrzej W. Przybyszewski
    • 1
    • 2
  1. 1.Polish-Japanese Institute of Information TechnologyWarszawaPoland
  2. 2.Dept. NeurologyUniversity of Massachusetts Medical SchoolWorcesterUSA

Personalised recommendations