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Neurodevelopmental Disorders

開催日 2014/9/12
時間 11:00 - 12:00
会場 Poster / Exhibition(Event Hall B)

A machine-learning-based investigation on the neural substrates of autism spectrum disorder using resting-state fMRI

  • P2-357
  • 八幡 憲明 / Noriaki Yahata:1 森本 淳 / Jun Morimoto:2 橋本 龍一郎 / Ryuichiro Hashimoto:3 柴田 和久 / Kazuhisa Shibata:2,4 今水 寛 / Hiroshi Imamizu:2 福田 めぐみ / Megumi Fukuda:2 川久保 友紀 / Yuki Kawakubo:5 桑原 斉 / Hitoshi Kuwabara:5 黒田 美保 / Miho Kuroda:5 山田 貴志 / Takashi Yamada:3 加藤 進昌 / Nobumasa Kato:3 佐々木 由香 / Yuka Sasaki:2,4 渡邊 武郎 / Takeo Watanabe:2,4 笠井 清登 / Kiyoto Kasai:6 川人 光男 / Mitsuo Kawato:2 
  • 1:東京大院・医・ユースメンタルヘルス / Dept Youth Mental Health, Univ of Tokyo, Tokyo, Japan 2:ATR脳情報通信総合研究所 / ATR Brain Info Comm Res Lab Group, Kyoto, Japan 3:昭和大学発達障害医療研究所 / Medical Institute of Developmental Disorder, Showa University, Tokyo, Japan 4:ブラウン大学・認知言語心理科学部 / Dept Cogn Ling & Psychol Sci, Brown Univ, RI, USA 5:東京大院・医・こころの発達医学 / Dept Child Neuropsych, Univ of Tokyo, Tokyo, Japan 6:東京大院・医・精神医学 / Dept Neuropsych, Univ of Tokyo, Tokyo, Japan 

It has been assumed that abnormal functional connections (FCs) critically underlie the neural mechanism of autism spectrum disorder (ASD). Currently, its diagnosis is dependent on medial interviews using standard clinical scales, designed to identify ASD-specific impairments in socio-communication and behavioral flexibility. There has been no effective neurally-defined biomarker that could be used in clinical settings for reliable, quantitative assessment on the behavioral symptoms of ASD. Here, we adopted a data-driven, hypothesis-free approach and investigated the whole-brain patterns of FCs using a resting-state functional connectivity magnetic resonance imaging (rs-fcMRI) data of 74 adults with ASD and 114 demographically-matched typically-developed individuals acquired at multiple sites in Japan. By employing novel machine learning algorithms, we established an rs-fcMRI-based classifier that incorporated a small fraction (0.2%) of all the FCs considered in the whole brain. This classifier allowed accurate prediction on the diagnoses of the individuals in the data set (84% correct) and this reliability was generalized to the second cohort comprising of ethnically different populations in the US (74% correct). Furthermore, the same set of FCs in the classifier accurately predicted the sub-scores of standard diagnostic instruments (ADI-R and ADOS) that described the behavioral characteristics of individuals with ASD in childhood and at present, respectively. Collectively, we have established a reliable rs-fcMRI-based biomarker of ASD that elucidates a direct link between the underlying neural mechanisms and the behavioral characteristics of ASD.

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