A small number of abnormal brain connections predicts adult autism spectrum disorder.
A small number of abnormal brain connections predicts adult autism spectrum disorder.
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DOI：
10.1038/ncomms11254
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发表时间：
2016-04-14
影响因子：
16.6
通讯作者：
Kawato M
中科院分区：
文献类型：
--
作者：
Yahata N;Morimoto J;Hashimoto R;Lisi G;Shibata K;Kawakubo Y;Kuwabara H;Kuroda M;Yamada T;Megumi F;Imamizu H;Náñez JE Sr;Takahashi H;Okamoto Y;Kasai K;Kato N;Sasaki Y;Watanabe T;Kawato M
虽然自闭症谱系障碍（ASD）是一种严重的终身疾病，但其潜在的神经机制尚不清楚。最近，基于神经影像学的ASD和典型发育（TD）个体分类器被开发用于识别功能连接（FCs）异常。由于不同测量条件和人口分布的过度拟合和干扰效应，没有分类器对独立队列进行严格验证。在这里，我们通过开发一种新的机器学习算法来克服这些困难，该算法可以识别区分ASD和TD的少量fc。该分类器在日本发现队列中实现了很高的准确性，并在美国和日本的两个独立验证队列中展示了显著的泛化程度。已开发的ASD分类器不能区分重度抑郁症和注意缺陷多动障碍患者与对照组，但能适度区分精神分裂症患者与对照组。该结果为探索基于神经成像的维度量化多重障碍谱系留下了可行的可能性。自闭症谱系障碍（ASD）表现为大脑细微但显著的变化。在这里，Yahata和他的同事设计了一种新的机器学习算法，并基于大脑功能连接开发了一种可靠的ASD分类器，用它来定量测量ASD和其他精神障碍之间的神经影像学维度。
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. Autism spectrum disorder (ASD) is manifested by subtle but significant changes in the brain. Here, Yahata and colleagues devise a novel machine learning algorithm and develop a reliable ASD classifier based on brain functional connectivity, with which they quantitatively measure neuroimaging dimensions between ASD and other mental disorders.