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

Yahata, Noriaki  ,  Morimoto, Jun  ,  Hashimoto, Ryuichiro  ,  Lisi, Giuseppe  ,  Shibata, Kazuhisa  ,  Kawakubo, Yuki  ,  Kuwabara, Hitoshi  ,  Kuroda, Miho  ,  Yamada, Takashi  ,  Megumi, Fukuda  ,  Imamizu, Hiroshi  ,  Náñez, José E.  ,  Takahashi, Hidehiko  ,  Okamoto, Yasumasa  ,  Kasai, Kiyoto  ,  Kato, Nobumasa  ,  Sasaki, Yuka  ,  Watanabe, Takeo  ,  Kawato, Mitsuo

72016-04-14 , Nature Publishing Group
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.

Number of accesses :  

Other information