@article{a587f9d3c3a147e5b9088fff23d05704,
title = "A small number of abnormal brain connections predicts adult autism spectrum disorder",
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.",
author = "Noriaki Yahata and Jun Morimoto and Ryuichiro Hashimoto and Giuseppe Lisi and Kazuhisa Shibata and Yuki Kawakubo and Hitoshi Kuwabara and Miho Kuroda and Takashi Yamada and Fukuda Megumi and Hiroshi Imamizu and Jose Nanez and Hidehiko Takahashi and Yasumasa Okamoto and Kiyoto Kasai and Nobumasa Kato and Yuka Sasaki and Takeo Watanabe and Mitsuo Kawato",
note = "Funding Information: This research was conducted under the 'Development of BMI Technologies for Clinical Application' of the Strategic Research Program for Brain Sciences supported by Japan Agency for Medical Research and Development (AMED). This research was also partially supported by the grants 'Development of Biomarker Candidates for Social Behavior' and 'Integrated Research on Neuropsychiatric Disorders' of the Strategic Research Program for Brain Sciences and Grant-in-Aid for Scientific Research on Innovative Areas (Comprehensive Brain Science Network) by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan, Japan Society for the Promotion of Science (JSPS) KAKENHI (25461752 to N.Y.), Grant-in-Aid for JSPS Fellows (to K.S.) and NIH Research Project Grant Program (R01EY015980 to T.W. and R01MH091801 to Y.S.). We thank Masa-aki Sato, Okito Yamashita, Yu Takagi, Masahiro Yamashita and Ayumu Yamashita for their insightful comments on an early draft. We also thank Saori C. Tanaka, Naho Ichikawa and Yujiro Yoshihara for their technical assistance in this study.",
year = "2016",
month = apr,
day = "14",
doi = "10.1038/ncomms11254",
language = "English (US)",
volume = "7",
journal = "Nature communications",
issn = "2041-1723",
publisher = "Nature Publishing Group",
}