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Altered efficiency of white matter connections for language function in children with language disorder

机译:语言障碍儿童语言功能的白质连接效率改变

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摘要

To characterize structural white matter substrates associated with language functions in children with language disorders (LD), a psychometry-driven diffusion tractography network was investigated with canonical correlation analysis (CCA), which can reliably predict expressive and receptive language scores from the nodal efficiency (NE) of the obtained network. The CCA found that the NE values of six regions: left inferior-frontal-opercular, left insular, left angular gyrus, left superior-temporal-gyrus, right hippocampus, and right cerebellar-lobule were highly correlated with language scores (rho(expressive)/rho(receptive) = 0.609/0.528), yielding significant differentiation of LD from controls using new imaging predictors u(expressive) (F = 15.024, p = .0003) and u(receptive) (F = 7.421, p = .009). This study demonstrates the utility of intrinsic language network analyses in distinguishing and potentially subtyping the type and severity of language deficit, especially in very young children (<= 3 years) with LD. The use of structural imaging to identify children with persisting language disorder could prove useful in understanding the etiology of language disorder.
机译:为了表征与语言障碍儿童(LD)的语言功能相关的结构白质衬底,用规范相关分析(CCA)研究了心理测量驱动的扩散牵引网络,这可以可靠地预测从节点效率(所获得的网络的NE)。 CCA发现六个区域的网元值:左下方 - 术,左侧较大,左角度,左侧颞 - 转象,右海马和右侧小叶片与语言评分(rho(表现力)高度相关)/ rho(接受)= 0.609 / 0.528),使用新的成像预测器U(F = 15.024,P = .0003)和U(接受)(F = 7.421,P =,从对照产生LD的显着分化009)。本研究展示了内在语言网络分析在区分和潜在群的语言赤字的类型和严重程度,特别是在非常幼儿(<= 3年)与LD。使用结构成像来识别具有持续性语言障碍的儿童可以证明在理解语言障碍的病因中是有用的。

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