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 (ρexpressive/ρreceptive = 0.609/0.528), yielding significant differentiation of LD from controls using new imaging predictors uexpressive (F = 15.024, p = .0003) and ureceptive (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.

中文翻译：

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语言障碍儿童语言功能白质连接效率的改变

为了表征与语言障碍 (LD) 儿童语言功能相关的结构性白质基质，采用典型相关分析 (CCA) 研究了心理测量驱动的扩散束成像网络，该网络可以从节点效率可靠地预测表达性和接受性语言分数。 NE) 获得的网络。CCA 发现左下额叶盖、左岛叶、左角回、左上颞回、右海马和右小脑小叶 6 个区域的 NE 值与语言分数高度相关（ρexpressive/ρreceptive = 0.609/0.528)，使用新的成像预测因子 uexpressive (F = 15.024, p = .0003) 和 ureceptive (F = 7.421, p = .009) 可显着区分 LD 与对照组。这项研究证明了内在语言网络分析在区分语言缺陷的类型和严重程度方面的效用，并可能对语言缺陷的类型和严重程度进行分类，特别是在患有 LD 的非常年幼的儿童（≤3 岁）中。使用结构成像来识别患有持续性语言障碍的儿童可能有助于了解语言障碍的病因。