Parkinson's disease: deep learning with a parameter-weighted structural connectome matrix for diagnosis and neural circuit disorder investigation.

Journal: Neuroradiology
Published Date: Jan 22, 2021

Abstract

PURPOSE: To investigate whether Parkinson's disease (PD) can be differentiated from healthy controls and to identify neural circuit disorders in PD by applying a deep learning technique to parameter-weighted and number of streamlines (NOS)-based structural connectome matrices calculated from diffusion-weighted MRI.

Authors

  • Koichiro Yasaka
    From the Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan 113-8655.
  • Koji Kamagata
  • Takashi Ogawa
    Department of Neurology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
  • Taku Hatano
    Department of Neurology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
  • Haruka Takeshige-Amano
    Department of Neurology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
  • Kotaro Ogaki
    Department of Neurology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
  • Christina Andica
    Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
  • Hiroyuki Akai
    From the Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan 113-8655.
  • Akira Kunimatsu
  • Wataru Uchida
    Department of Radiology, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan.
  • Nobutaka Hattori
    Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan.
  • Shigeki Aoki
  • Osamu Abe
    From the Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan 113-8655.

Keywords

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