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通过NBS-Predict从患有自闭症谱系障碍的学龄前儿童的脑结构磁共振成像预测语用语言能力。

Predicting pragmatic language abilities from brain structural MRI in preschool children with ASD by NBS-Predict.

作者信息

Qian Lu, Ding Ning, Fang Hui, Xiao Ting, Sun Bei, Gao HuiYun, Ke XiaoYan

机构信息

Child Mental Health Research Center, Nanjing Brain Hospital Affiliated of Nanjing Medical University, Nanjing Guangzhou Road 264#, Nanjing, 210029, China.

Affiliated Mental Health Center of Jiangnan University, QianRong Road156#, Wuxi, 214151, China.

出版信息

Eur Child Adolesc Psychiatry. 2025 Jun 11. doi: 10.1007/s00787-025-02775-w.

Abstract

Pragmatics plays a crucial role in effectively conveying messages across various social communication contexts. This aspect is frequently highlighted in the challenges experienced by children diagnosed with autism spectrum disorder (ASD). Notably, there remains a paucity of research investigating how the structural connectome (SC) predicts pragmatic language abilities within this population. Using diffusion tensor imaging (DTI) and deterministic tractography, we constructed the whole-brain white matter structural network (WMSN) in a cohort comprising 92 children with ASD and 52 typically developing (TD) preschoolers, matched for age and gender. We employed network-based statistic (NBS)-Predict, a novel methodology that integrates machine learning (ML) with NBS, to identify dysconnected subnetworks associated with ASD, and then to predict pragmatic language abilities based on the SC derived from the whole-brain WMSN in the ASD group. Initially, NBS-Predict identified a subnetwork characterized by 42 reduced connections across 37 brain regions (p = 0.01), achieving a highest classification accuracy of 79.4% (95% CI: 0.791 ~ 0.796). The dysconnected regions were predominantly localized within the brain's frontotemporal and subcortical areas, with the right superior medial frontal gyrus (SFGmed.R) emerging as the region exhibiting the most extensive disconnection. Moreover, NBS-Predict demonstrated that the optimal correlation coefficient between the predicted pragmatic language scores and the actual measured scores was 0.220 (95% CI: 0.174 ~ 0.265). This analysis revealed a significant association between the pragmatic language abilities of the ASD cohort and the white matter connections linking the SFGmed.R with the bilateral anterior cingulate gyrus (ACG). In summary, our findings suggest that the subnetworks displaying the most significant abnormal connections were concentrated in the frontotemporal and subcortical regions among the ASD group. Furthermore, the observed abnormalities in the white matter connection pathways between the SFGmed.R and ACG may underlie the neurobiological basis for pragmatic language deficits in preschool children with ASD.

摘要

语用学在跨各种社会交流情境有效传达信息方面起着至关重要的作用。这一方面在被诊断为自闭症谱系障碍(ASD)的儿童所经历的挑战中经常被强调。值得注意的是,关于结构连接组(SC)如何预测该人群的语用语言能力的研究仍然匮乏。我们使用扩散张量成像(DTI)和确定性纤维束成像技术,在一个由92名患有ASD的儿童和52名年龄及性别匹配的典型发育(TD)学龄前儿童组成的队列中构建了全脑白质结构网络(WMSN)。我们采用基于网络的统计(NBS)-Predict,这是一种将机器学习(ML)与NBS相结合的新方法,来识别与ASD相关的断开连接的子网络,然后基于从ASD组全脑WMSN得出的SC来预测语用语言能力。最初,NBS-Predict识别出一个子网络,其特征是在37个脑区中有42个连接减少(p = 0.01),最高分类准确率达到79.4%(95% CI:0.791 ~ 0.796)。断开连接的区域主要位于大脑的额颞叶和皮质下区域,右侧额上内侧回(SFGmed.R)是显示出最广泛断开连接的区域。此外,NBS-Predict表明预测的语用语言分数与实际测量分数之间的最佳相关系数为0.220(95% CI:0.174 ~ 0.265)。该分析揭示了ASD队列的语用语言能力与连接SFGmed.R和双侧前扣带回(ACG)的白质连接之间存在显著关联。总之,我们的研究结果表明,显示出最显著异常连接的子网络集中在ASD组的额颞叶和皮质下区域。此外,观察到的SFGmed.R和ACG之间白质连接通路的异常可能是患有ASD的学龄前儿童语用语言缺陷的神经生物学基础。

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