A small number of abnormal brain connections predicts adult autism spectrum disorder
抄録
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.
収録刊行物
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- Nature Communications
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Nature Communications 7 2016-04-14
Nature Publishing Group
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詳細情報
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- CRID
- 1050001335835602304
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- NII論文ID
- 120005770754
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- ISSN
- 20411723
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- HANDLE
- 2433/215366
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- 本文言語コード
- en
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- 資料種別
- journal article
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- データソース種別
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- IRDB
- CiNii Articles