@article{0bfb969b376249b4830f2edabeb7e84f,
title = "Overlapping but Asymmetrical Relationships Between Schizophrenia and Autism Revealed by Brain Connectivity",
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.",
keywords = "Autism, Classifier, FMRI, Machine learning, Resting state, Schizophrenia, fMRI, resting state, classifier, schizophrenia, machine learning, autism",
author = "Yujiro Yoshihara and Giuseppe Lisi and Noriaki Yahata and Junya Fujino and Yukiko Matsumoto and Jun Miyata and Gen-Ichi Sugihara and Shin-Ichi Urayama and Manabu Kubota and Masahiro Yamashita and Ryuichiro Hashimoto and Naho Ichikawa and Weipke Cahn and {van Haren}, {Neeltje E M} and Susumu Mori and Yasumasa Okamoto and Kiyoto Kasai and Nobumasa Kato and Hiroshi Imamizu and Kahn, {Ren{\'e} S} and Akira Sawa and Mitsuo Kawato and Toshiya Murai and Jun Morimoto and Hidehiko Takahashi",
note = "Funding Information: This work was supported by the ?Application of DecNef for development of diagnostic and cure system for mental disorders and construction of clinical application bases? of the Strategic Research Program for Brain Sciences from the Japan Agency for Medical Research and Development, AMED under Grant Number 17dm0107044h0005. The UMCU-TOPFIT study was supported by funding from the Dutch Diabetes Research Foundation (2007.00.040); Lilly Pharmaceuticals, Houten, the Netherlands (Ho01-TOPFIT); Janssen Pharmaceuticals, Tilburg, the Netherlands; and the Dutch Psychomotor Therapy Foundation, Utrecht, the Netherlands. The JHU FES study was supported by the National Institute of Mental Health (NIMH) grant numbers (MH-094268), (MH-092443), (MH-105660); and the Silvio O. Conte Center funded by NIMH; the National Institutes of Health grant numbers (P41EB015909) and (R01NS084957); grants from Stanley, S-R/RUSK, and NARSAD; and part of the participant recruitment was supported by Mitsubishi Tanabe Pharm. Co. Ltd. (USA). The authors declare no competing financial interests and no conflicts of interest. Data supporting the findings of this study are available on request from the corresponding author. Publisher Copyright: {\textcopyright} The Author(s) 2020. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2020",
month = sep,
doi = "10.1093/schbul/sbaa021",
language = "English",
volume = "46",
pages = "1210--1218",
journal = "Schizophrenia Bulletin",
issn = "0586-7614",
publisher = "Oxford University Press",
number = "5",
}