Overlapping but asymmetrical relationships between schizophrenia and autism revealed by brain connectivity

Yujiro Yoshihara, Giuseppe Lisi, Noriaki Yahata, Junya Fujino, Yukiko Matsumoto, Jun Miyata, Gen Ichi Sugihara, Shin Ichi Urayama, Manabu Kubota, Masahiro Yamashita, Ryuichiro Hashimoto, Naho Ichikawa, Weipke Cahn, Neeltje E.M. van Haren, Susumu Mori, Yasumasa Okamoto, Kiyoto Kasai, Nobumasa Kato, Hiroshi Imamizu, René S. KahnAkira Sawa, Mitsuo Kawato, Toshiya Murai, Jun Morimoto, Hidehiko Takahashi

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Although the relationship between schizophrenia spectrum disorder (SSD) and autism spectrum disorder (ASD) has long been debated, it has not yet been fully elucidated. The authors quantified and visualized the relationship between ASD and SSD using dual classifiers that discriminate patients from healthy controls (HCs) based on resting-state functional connectivity magnetic resonance imaging. To develop a reliable SSD classifier, sophisticated machine-learning algorithms that automatically selected SSD-specific functional connections were applied to Japanese datasets from Kyoto University Hospital (N = 170) including patients with chronic-stage SSD. The generalizability of the SSD classifier was tested by 2 independent validation cohorts, and 1 cohort including first-episode schizophrenia. The specificity of the SSD classifier was tested by 2 Japanese cohorts of ASD and major depressive disorder. The weighted linear summation of the classifier's functional connections constituted the biological dimensions representing neural classification certainty for the disorders. Our previously developed ASD classifier was used as ASD dimension. Distributions of individuals with SSD, ASD, and HCs s were examined on the SSD and ASD biological dimensions. We found that the SSD and ASD populations exhibited overlapping but asymmetrical patterns in the 2 biological dimensions. That is, the SSD population showed increased classification certainty for the ASD dimension but not vice versa. Furthermore, the 2 dimensions were correlated within the ASD population but not the SSD population. In conclusion, using the 2 biological dimensions based on resting-state functional connectivity enabled us to discover the quantified relationships between SSD and ASD.

Original languageEnglish (US)
Pages (from-to)1210-1218
Number of pages9
JournalSchizophrenia bulletin
Volume46
Issue number5
DOIs
StatePublished - Sep 1 2020

Keywords

  • Autism
  • Classifier
  • FMRI
  • Machine learning
  • Resting state
  • Schizophrenia

ASJC Scopus subject areas

  • Psychiatry and Mental health

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