Hierarchical supervised/unsupervised approach for subtype and redefine psychiatric disorders using resting state functional magnetic resonance imaging

DOI
  • Yamashita Ayumu
    Advanced telecommunications research institute international（ATR） Graduate School of Information Science and Technology the University of Tokyo

Bibliographic Information

Other Title
  • 精神疾患のサブタイプや再定義をめざした階層的教師有・教師無機械学習法〜調和された多施設多疾患安静時fMRIデータを用いた研究〜

Abstract

Information technologies such as deep learning and machine learning have made remarkable progress, and their effectiveness has been demonstrated in brain imaging research related to psychiatric disorders. For example, the application of supervised learning to resting‐state functional magnetic resonance imaging (fMRI) data has been used to identify psychiatric disorders based on their biological basis, and unsupervised learning has been used for subtyping of psychiatric disorders. However, there have been problems such as small effect sizes on the relationship between resting brain activity and cognitive functions, inter‐imaging‐site differences in brain imaging data, and development of the technologies based on DSM diagnoses. In this paper, I discuss these problems, the extent to which brain imaging studies have revealed psychiatric disorders, and what needs to be done now. I introduce our efforts to overcome these problems, constructing a large multi‐imaging‐site, multi‐disorder dataset, developing a novel harmonization technique to mitigate inter‐site differences in brain imaging data, and investigating subtypes of psychiatric disorders based on the brain circuit.

Journal

Keywords

Details 詳細情報について

  • CRID
    1390295568860811904
  • DOI
    10.11249/jsbpjjpp.34.1_24
  • ISSN
    21866465
    21866619
  • Text Lang
    ja
  • Data Source
    • JaLC
  • Abstract License Flag
    Disallowed

Report a problem

Back to top