@article{oai:repo.qst.go.jp:00047916,
 author = {Yahata, Noriaki and Morimoto, Jun and Hashimoto, Ryuichiro and Lisi, Giuseppe and Shibata, Kazuhisa and Kawato, Mitsuo and et.al and 八幡 憲明},
 journal = {Nature Communications  (Online Only URL:http://www.nature.com/ncomms/index.html)},
 month = {Apr},
 note = {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.},
 pages = {11254-1--11254-12},
 title = {A small number of abnormal brain connections predicts adult autism spectrum disorder},
 volume = {7},
 year = {2016}
}