A Small Number of Abnormal Brain Connections Predicts Adult Autism Spectrum Disorder
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 (aut): Yahata, Noriaki
- Author (aut): Morimoto, Jun
- Author (aut): Hashimoto, Ryuichiro
- Author (aut): Lisi, Giuseppe
- Author (aut): Shibata, Kazuhisa
- Author (aut): Kawakubo, Yuki
- Author (aut): Kuwabara, Hitoshi
- Author (aut): Kuroda, Miho
- Author (aut): Yamada, Takashi
- Author (aut): Megumi, Fukuda
- Author (aut): Imamizu, Hiroshi
- Author (aut): Nanez, Jose
- Author (aut): Takahashi, Hidehiko
- Author (aut): Okamoto, Yasumasa
- Author (aut): Kasai, Kiyoto
- Author (aut): Kato, Nobumasa
- Author (aut): Sasaki, Yuka
- Author (aut): Watanabe, Takeo
- Author (aut): Kawato, Mitsuo
- Contributor (ctb): New College of Interdisciplinary Arts and Sciences