Nemmi Federico, Cignetti Fabien, Vaugoyeau Marianne, Assaiante Christine, Chaix Yves, Péran Patrice
Toulouse NeuroImaging Center (ToNIC - UMR1214), Inserm/Université Paul Sabatier, Toulouse, France.
Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, TIMC, Grenoble, France.
Cortex. 2023 Mar;160:43-54. doi: 10.1016/j.cortex.2022.10.016. Epub 2022 Dec 17.
Developmental dyslexia (DD) and developmental coordination disorder (DCD) are two common neurodevelopmental disorders with a high co-occurrence rate. This led several authors to postulate that the two disorders share, at least partially, similar neural underpinning. However, even though several studies examined brain differences between typically developing (TD) children and children with either DD or DCD, no previous study directly compared DD, DCD and children with both disorders (COM) using neuroimaging. We acquired structural and resting-state functional MRI images of 136 children (TD = 42, DD = 45, DCD = 20, COM = 29). Difference between TD children and the other groups was assessed using univariate analysis of structural indexes including grey and white matter volumes and functional indexes quantifying activity (fraction of the amplitude of the low frequency fluctuations), local and global connectivity. Regional differences in structural and functional brain indexes were then used to train machine learning models to discriminate among DD, DCD and COM and to find the most discriminant regions. While no imaging index alone discriminated between the three groups, grouping grey and white matter volumes (structural model) or activity, local and global connectivity (functional model) made possible to discriminate among the DD, DCD and COM groups. The most important discrimination was obtained using the functional model, with regions in the cerebellum and the temporal lobe being the most discriminant for DCD and DD children, respectively. Results further showed that children with both DD and DCD have subtle but identifiable brain differences that can only be captured using several imaging indexes pertaining to both brain structure and function.
发育性阅读障碍(DD)和发育性协调障碍(DCD)是两种常见的神经发育障碍,共现率很高。这使得一些作者推测,这两种障碍至少部分共享相似的神经基础。然而,尽管有几项研究考察了发育正常(TD)儿童与患有DD或DCD的儿童之间的大脑差异,但此前尚无研究使用神经影像学直接比较DD、DCD以及同时患有这两种障碍的儿童(COM)。我们获取了136名儿童(TD = 42名,DD = 45名,DCD = 20名,COM = 29名)的结构和静息态功能磁共振成像图像。使用单变量分析评估TD儿童与其他组之间的差异,分析指标包括灰质和白质体积等结构指标以及量化活动(低频波动幅度分数)、局部和全局连通性的功能指标。然后,利用大脑结构和功能指标的区域差异来训练机器学习模型,以区分DD、DCD和COM,并找出最具区分性的区域。虽然没有单一的成像指标能够区分这三组,但将灰质和白质体积(结构模型)或活动、局部和全局连通性(功能模型)进行分组,使得区分DD、DCD和COM组成为可能。使用功能模型获得了最重要的区分效果,小脑区域和颞叶区域分别对DCD儿童和DD儿童最具区分性。结果还表明,同时患有DD和DCD的儿童存在细微但可识别的大脑差异,只有使用与大脑结构和功能相关的多个成像指标才能捕捉到这些差异。