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Use of Temporal Attributes in Detection of Functional Areas in Basal Ganglia

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Book cover Brain Informatics (BI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11309))

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Abstract

Basal ganglia are a target for deep brain stimulation (DBS) in various neurological disorders like Parkinson’s Disease (PD) or Dystonia. Due to the complex excitatory and inhibitory interactions between various components of basal ganglia, it is often as much important to stimulate certain regions as it is not to stimulate others. Such is the case in DBS surgery for PD where the goal is the stimulation of the Subthalamic Nucleus (STN) while the Substantia Nigra Pars reticulata (SNr) should not be stimulated. In this paper it is shown that use of temporal attributes extracted from microrecordings acquired during DBS procedure not only allows for better detection of the STN itself but also helps to prevent false positive identification of the SNr recordings as the STN ones.

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Authors and Affiliations

  1. Research and Academic Computer Network, Warsaw, Poland

    Konrad A. Ciecierski

Authors
  1. Konrad A. Ciecierski
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Correspondence to Konrad A. Ciecierski .

Editor information

Editors and Affiliations

  1. University of Texas at Arlington, Arlington, TX, USA

    Shouyi Wang

  2. University of Southern California, West Hollywood, USA

    Vicky Yamamoto

  3. Department of Mathematics, University of Texas at Arlington, Arlington, TX, USA

    Jianzhong Su

  4. Maebashi Institute of Technology, Gunma, Japan

    Yang Yang

  5. The University of Texas at Arlington, Arlington, USA

    Erick Jones

  6. Louisiana Tech University, Arlington, TX, USA

    Leon Iasemidis

  7. Carnegie Mellon University, Pittsburgh, PA, USA

    Tom Mitchell

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Ciecierski, K.A. (2018). Use of Temporal Attributes in Detection of Functional Areas in Basal Ganglia. In: Wang, S., et al. Brain Informatics. BI 2018. Lecture Notes in Computer Science(), vol 11309. Springer, Cham. https://doi.org/10.1007/978-3-030-05587-5_17

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  • DOI: https://doi.org/10.1007/978-3-030-05587-5_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05586-8

  • Online ISBN: 978-3-030-05587-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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