A small number of abnormal brain connections predicts adult autism spectrum disorder

Noriaki Yahata, Jun Morimoto, Ryuichiro Hashimoto, Giuseppe Lisi, Kazuhisa Shibata, Yuki Kawakubo, Hitoshi Kuwabara, Miho Kuroda, Takashi Yamada, Fukuda Megumi, Hiroshi Imamizu, José E. Náñez, Hidehiko Takahashi, Yasumasa Okamoto, Kiyoto Kasai, Nobumasa Kato, Yuka Sasaki (), Takeo Watanabe and Mitsuo Kawato ()
Additional contact information
Noriaki Yahata: Graduate School of Medicine, The University of Tokyo
Jun Morimoto: ATR Brain Information Communication Research Laboratory Group
Ryuichiro Hashimoto: ATR Brain Information Communication Research Laboratory Group
Giuseppe Lisi: ATR Brain Information Communication Research Laboratory Group
Kazuhisa Shibata: ATR Brain Information Communication Research Laboratory Group
Yuki Kawakubo: Graduate School of Medicine, The University of Tokyo
Hitoshi Kuwabara: Disability Services Office, The University of Tokyo
Miho Kuroda: Graduate School of Medicine, The University of Tokyo
Takashi Yamada: ATR Brain Information Communication Research Laboratory Group
Fukuda Megumi: ATR Brain Information Communication Research Laboratory Group
Hiroshi Imamizu: ATR Brain Information Communication Research Laboratory Group
José E. Náñez: School of Social and Behavioral Sciences, Arizona State University
Hidehiko Takahashi: Kyoto University Graduate School of Medicine
Yasumasa Okamoto: Graduate School of Biomedical Sciences, Hiroshima University
Kiyoto Kasai: Graduate School of Medicine, The University of Tokyo
Nobumasa Kato: Medical Institute of Developmental Disabilities Research, Showa University Karasuyama Hospital
Yuka Sasaki: ATR Brain Information Communication Research Laboratory Group
Takeo Watanabe: ATR Brain Information Communication Research Laboratory Group
Mitsuo Kawato: ATR Brain Information Communication Research Laboratory Group

Nature Communications, 2016, vol. 7, issue 1, 1-12

Abstract: Abstract Although autism spectrum disorder (ASD) is a serious lifelong condition, its underlying neural mechanism remains unclear. Recently, neuroimaging-based classifiers for ASD and typically developed (TD) individuals were developed to identify the abnormality of functional connections (FCs). Due to over-fitting and interferential effects of varying measurement conditions and demographic distributions, no classifiers have been strictly validated for independent cohorts. Here we overcome these difficulties by developing a novel machine-learning algorithm that identifies a small number of FCs that separates ASD versus TD. The classifier achieves high accuracy for a Japanese discovery cohort and demonstrates a remarkable degree of generalization for two independent validation cohorts in the USA and Japan. The developed ASD classifier does not distinguish individuals with major depressive disorder and attention-deficit hyperactivity disorder from their controls but moderately distinguishes patients with schizophrenia from their controls. The results leave open the viable possibility of exploring neuroimaging-based dimensions quantifying the multiple-disorder spectrum.

Date: 2016
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DOI: 10.1038/ncomms11254

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