期刊
NATURE COMMUNICATIONS
卷 7, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/ncomms11254
关键词
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资金
- Japan Agency for Medical Research and Development (AMED)
- Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan
- Japan Society for the Promotion of Science (JSPS) KAKENHI [25461752]
- NIH Research Project Grant Program [R01EY015980, R01MH091801]
- Grants-in-Aid for Scientific Research [25461752] Funding Source: KAKEN
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.
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