| Literature DB >> 32300809 |
Yujiro Yoshihara1, Giuseppe Lisi2, Noriaki Yahata3,4,5, Junya Fujino6, Yukiko Matsumoto1, Jun Miyata1, Gen-Ichi Sugihara7, Shin-Ichi Urayama8, Manabu Kubota1,6,9, Masahiro Yamashita10, Ryuichiro Hashimoto3,6,11, Naho Ichikawa12, Weipke Cahn13, Neeltje E M van Haren13, Susumu Mori14, Yasumasa Okamoto12, Kiyoto Kasai15, Nobumasa Kato6, Hiroshi Imamizu10,16, René S Kahn13, Akira Sawa17, Mitsuo Kawato3, Toshiya Murai1, Jun Morimoto2, Hidehiko Takahashi7.
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.Entities:
Keywords: autism; classifier; fMRI; machine learning; resting state; schizophrenia
Year: 2020 PMID: 32300809 PMCID: PMC7505174 DOI: 10.1093/schbul/sbaa021
Source DB: PubMed Journal: Schizophr Bull ISSN: 0586-7614 Impact factor: 9.306