Discovering neural signatures common to developmental stages with autism

Summary

Takashi Itabashi, a lecturer at Showa University, and Ayumu Yamashita, a researcher at ATR, and colleagues have developed a classifier to predict the diagnosis of autism spectrum disorder (ASD) using machine learning methods and functional magnetic resonance imaging (fMRI) data from adults collected from multiple institutions in Japan. The developed classifier was shown to be applicable to data from different developmental stages of children and adolescents as well as adults used to train the machine learning. This achievement is expected not only to deepen our understanding of the brain circuit mechanisms of ASD, but also to serve as a tool to improve the accuracy of ASD diagnosis. Further future research is also expected to identify new biological subtypes of ASD as well as the potential for practical use as a diagnostic aid for ASD.

【Information on the outcome】
https://www.showa-u.ac.jp/news/nid00006967.html　（昭和大学）
https://www.u-presscenter.jp/article/post-54663.html   (大学プレスセンター）
https://www.amed.go.jp/news/seika/2024_seika_index.html   (AMED）

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