Advertisement

DTI Helps to Predict Parkinson’s Patient’s Symptoms Using Data Mining Techniques

  • Artur ChudzikEmail author
  • Artur Szymański
  • Jerzy Paweł Nowacki
  • Andrzej W. Przybyszewski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11432)

Abstract

Deep Brain Stimulation (DBS) is commonly used to treat, inter alia, movement disorder symptoms in patients with Parkinson’s disease, dystonia or essential tremor. The procedure stimulates a targeted region of the brain through implanted leads that are powered by a device called an implantable pulse generator (IPG). The mentioned targeted region is mainly chosen to be subthalamic nucleus (STN) during most of the operations. STN is a nucleus in the midbrain with a size of 3 mm × 5 mm × 9 mm that consist of parts with different physiological functions. The purpose of the study was to predict Parkinson’s patient’s symptoms defined by Unified Parkinson’s Disease Rating Scale (UPDRS) that may occur after the DBS treatment. Parameters had been obtained from 3DSlicer (Harvard Medical School, Boston, MA), which allowed us to track connections between the stimulated part of STN and the cortex based on the DTI (diffusion tensor imaging).

Keywords

Subthalamic nucleus UPDRS RSES MRI DTI DBS Parkinson’s disease Data mining 

References

  1. 1.
    Benabid, A.L., et al.: Deep brain stimulation of the subthalamic nucleus for the treatment of Parkinson’s disease. Lancet Neurol. 8(1), 67–81 (2009)CrossRefGoogle Scholar
  2. 2.
    Nambu, A., Tokuno, H., Takada, M.: Functional significance of the cortico–subthalamo–pallidal ‘hyperdirect’ pathway. Neurosci. Res. 43(2), 111–117 (2002)CrossRefGoogle Scholar
  3. 3.
    Szymański, A., Przybyszewski, A.W.: Rough set rules help to optimize parameters of deep brain stimulation in Parkinson’s patients. In: Ślȩzak, D., Tan, A.-H., Peters, J.F., Schwabe, L. (eds.) BIH 2014. LNCS (LNAI), vol. 8609, pp. 345–356. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-09891-3_32CrossRefGoogle Scholar
  4. 4.
    Szymański, A., Kubis, A., Przybyszewski, A.W.: Data mining and neural network simulations can help to improve deep brain stimulation effects in Parkinson’s disease. Comput. Sci. 16(2), 199 (2015)CrossRefGoogle Scholar
  5. 5.
    Pawlak, Z.: Rough set theory and its applications. J. Telecommun. Inf. Technol., 7–10 (2002)Google Scholar
  6. 6.
    McKinney, W.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445 (2010)Google Scholar
  7. 7.
    Przybyszewski, A.W., et al.: Multi-parametric analysis assists in STN localization in Parkinson’s patients. J. Neurol. Sci. 366, 37–43 (2016)CrossRefGoogle Scholar
  8. 8.
    Benazzouz, A., et al.: Intraoperative microrecordings of the subthalamic nucleus in Parkinson’s disease. Mov. Disord. Official J. Mov. Disord. Soc. 17(S3), S145–S149 (2002)CrossRefGoogle Scholar
  9. 9.
    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. 18(7), 738–750 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Polish-Japanese Academy of Information TechnologyWarsawPoland
  2. 2.Department of NeurologyUniversity of Massachusetts Medical SchoolWorcesterUSA

Personalised recommendations