Translational Imaging Laboratory, Children's Hospital of Michigan, Detroit, MI, USA; Carman and Ann Adams Department of Pediatrics, Wayne State University School of Medicine, Detroit, MI, USA.
Translational Imaging Laboratory, Children's Hospital of Michigan, Detroit, MI, USA; Translational Neuroscience Program, Wayne State University School of Medicine, Detroit, MI, USA.
Brain Lang. 2020 Apr;203:104743. doi: 10.1016/j.bandl.2020.104743. Epub 2020 Jan 28.
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 (ρ/ρ = 0.609/0.528), yielding significant differentiation of LD from controls using new imaging predictors u (F = 15.024, p = .0003) and u (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.
为了描述语言障碍（LD）儿童语言功能相关的结构性白质基质，我们采用典型相关分析（CCA）研究了一种基于心理测量的弥散轨迹网络，该方法可以从获得的网络的节点效率（NE）可靠地预测表达性和接受性语言分数。CCA 发现，六个区域的 NE 值：左侧额下回-前运动区、左侧脑岛、左侧角回、左侧颞上回、右侧海马体和右侧小脑小叶，与语言分数高度相关（ρ/ρ=0.609/0.528），使用新的成像预测因子 u（F=15.024，p=0.0003）和 u（F=7.421，p=0.009）可以显著区分 LD 和对照组。本研究证明了内在语言网络分析在区分和潜在分类语言缺陷的类型和严重程度方面的作用，尤其是在≤3 岁的 LD 幼儿中。使用结构成像来识别持续存在语言障碍的儿童可能有助于了解语言障碍的病因。
