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机器学习方法识别一种静息态功能连接模式,作为自闭症谱系障碍的内表型。

Machine learning approach to identify a resting-state functional connectivity pattern serving as an endophenotype of autism spectrum disorder.

机构信息

Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.

Medical Institute of Developmental Disabilities Research, Showa University, 6-11-11 Kitakarasuyama, Tokyo, 157-8577, Japan.

出版信息

Brain Imaging Behav. 2019 Dec;13(6):1689-1698. doi: 10.1007/s11682-018-9973-2.

DOI:10.1007/s11682-018-9973-2
PMID:30280304
Abstract

Endophenotype refers to a measurable and heritable component between genetics and diagnosis, and the same endophenotype is present in both individuals with a diagnosis and their unaffected siblings. Determination of the neural correlates of an endophenotype and diagnosis is important in autism spectrum disorder (ASD). However, prior studies enrolling individuals with ASD and their unaffected siblings have generally included only one group of typically developing (TD) subjects; they have not accounted for differences between TD siblings. Thus, they could not differentiate the neural correlates for endophenotype from the clinical diagnosis. In this context, we enrolled pairs of siblings with an ASD endophenotype (individuals with ASD and their unaffected siblings) and pairs of siblings without this endophenotype (pairs of TD siblings). Using resting-state functional MRI, we first aimed to identify an endophenotype pattern consisting of multiple functional connections (FCs) then examined the neural correlates of FCs for ASD diagnosis, controlling for differences between TD siblings. Sparse logistic regression successfully classified subjects as to the endophenotype (area under the curve = 0.78, classification accuracy = 75%). Then, a bootstrapping approach controlling for differences between TD siblings revealed that an FC between the right middle temporal gyrus and right anterior cingulate cortex was substantially different between individuals with ASD and their unaffected siblings, suggesting that this FC may be a neural correlate for the diagnosis, while the other FCs represent the endophenotype. The current findings suggest that an ASD endophenotype pattern exists in FCs, and a neural correlate for ASD diagnosis is dissociable from this endophenotype. (250 words).

摘要

表型是指遗传与诊断之间可测量和可遗传的成分,并且相同的表型存在于具有诊断的个体及其未受影响的兄弟姐妹中。确定自闭症谱系障碍 (ASD) 中表型和诊断的神经相关性很重要。然而,以前招募 ASD 个体及其未受影响的兄弟姐妹的研究通常只包括一组典型发育 (TD) 受试者;他们没有考虑 TD 兄弟姐妹之间的差异。因此,他们无法将表型的神经相关性与临床诊断区分开来。在这种情况下,我们招募了具有 ASD 表型的兄弟姐妹对(患有 ASD 的个体及其未受影响的兄弟姐妹)和没有这种表型的兄弟姐妹对(TD 兄弟姐妹对)。使用静息态功能磁共振成像,我们首先旨在确定由多个功能连接 (FC) 组成的表型模式,然后检查 FC 对 ASD 诊断的神经相关性,同时控制 TD 兄弟姐妹之间的差异。稀疏逻辑回归成功地根据表型对受试者进行分类(曲线下面积 = 0.78,分类准确性 = 75%)。然后,一种控制 TD 兄弟姐妹之间差异的引导方法显示,右侧颞中回和右侧前扣带皮层之间的 FC 在 ASD 个体及其未受影响的兄弟姐妹之间存在显著差异,这表明该 FC 可能是诊断的神经相关性,而其他 FC 则代表表型。目前的研究结果表明,ASD 表型模式存在于 FC 中,ASD 诊断的神经相关性与该表型可分离。(250 个单词)。

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