Objective

Neurocognitive disabilities in Duchenne muscular dystrophy (DMD) children beginning in early childhood and distal DMD gene deletions involving disruption of Dp140 isoform are more likely to manifest significant neurocognitive impairments. MRI data analysis techniques like brain-network metrics can provide information on microstructural integrity and underlying pathophysiology.

Methods

A prospective study on 95 participants [DMD = 57, and healthy controls (HC) = 38]. The muscular dystrophy functional rating scale (MDFRS) scores, neuropsychology batteries, and multiplex ligand-dependent probe amplification (MLPA) testing were used for clinical assessment, IQ estimation, and genotypic classification. Diffusion MRI and network-based statistics were used to analyze structural connectomes at various levels and correlate with clinical markers.

Results

Motor and executive sub-networks were extracted and analyzed. Out of 57 DMD children, 23 belong to Dp140 + and 34 to Dp140- subgroup. Motor disabilities are pronounced in Dp140- subgroup as reflected by lower MDFRS scores. IQ parameters are significantly low in all-DMD cases; however, the Dp140- has specifically lowest scores. Significant differences were observed in global efficiency, transitivity, and characteristic path length between HC and DMD. Subgroup analysis demonstrates that the significance is mainly driven by participants with Dp140- than Dp140 + isoform. Finally, a random forest classifier model illustrated an accuracy of 79% between HC and DMD and 90% between DMD- subgroups.

Conclusions

Current findings demonstrate structural network-based characterization of abnormalities in DMD, especially prominent in Dp140-. Our observations suggest that participants with Dp140 + have relatively intact connectivity while Dp140- show widespread connectivity alterations at global, nodal, and edge levels. This study provides valuable insights supporting the genotype–phenotype correlation of brain-behavior involvement in DMD children.

中文翻译：

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Duchenne 肌营养不良症中结构连接组和神经认知功能的破坏：基于 Dp140 肌营养不良蛋白亚型的分类和亚型

客观的

Duchenne 肌营养不良症 (DMD) 儿童的神经认知障碍从儿童早期开始，以及涉及 Dp140 异构体破坏的远端 DMD 基因缺失更有可能表现出明显的神经认知障碍。像脑网络指标这样的 MRI 数据分析技术可以提供有关微结构完整性和潜在病理生理学的信息。

方法

一项针对 95 名参与者的前瞻性研究 [DMD = 57，健康对照组 (HC) = 38]。肌营养不良症功能评定量表 (MDFRS) 评分、神经心理学电池和多重配体依赖性探针扩增 (MLPA) 测试用于临床评估、智商估计和基因型分类。扩散 MRI 和基于网络的统计数据用于分析不同水平的结构连接组并与临床标志物相关联。

结果

提取和分析运动和执行子网络。在 57 名 DMD 儿童中，23 名属于 Dp140 +，34 名属于 Dp140- 亚组。运动障碍在 Dp140 亚组中很明显，这反映在较低的 MDFRS 分数上。在所有 DMD 病例中，IQ 参数显着降低；但是，Dp140- 得分最低。在 HC 和 DMD 之间的全局效率、传递性和特征路径长度方面观察到显着差异。亚组分析表明，显着性主要由 Dp140- 的参与者驱动，而不是 Dp140 + 异构体。最后，随机森林分类器模型显示 HC 和 DMD 之间的准确度为 79%，DMD 亚组之间的准确度为 90%。

结论

目前的研究结果证明了基于结构网络的 DMD 异常特征，尤其是在 Dp140-中。我们的观察表明，具有 Dp140 + 的参与者具有相对完整的连通性，而 Dp140- 在全局、节点和边缘级别显示出广泛的连通性变化。这项研究提供了有价值的见解，支持 DMD 儿童大脑行为参与的基因型-表型相关性。