Wei Lei, Du Xiaonan, Yang Zidong, Ding Ming, Yang Baofeng, Wang Ji, Long Shasha, Qiao Zhongwei, Jiang Yonghui, Wang Yi, Wang He
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Department of Neurology, Children's Hospital of Fudan University, Shanghai, China.
J Magn Reson Imaging. 2023 Apr;57(4):1212-1221. doi: 10.1002/jmri.28360. Epub 2022 Jul 20.
Angelman syndrome (AS) is a genetic disorder that affects neurodevelopment. The investigation of changes in the brain white matter network, which would contribute to a better understanding of the pathogenesis of AS brain, was lacking.
To investigate both local and global alterations of white matter in patients with AS.
Prospective.
A total of 29 AS patients (6.6 ± 1.4 years, 15 [52%] females) and 19 age-matched healthy controls (HC) (7.0 ± 1.5 years, 10 [53%] females).
FIELD STRENGTH/SEQUENCE: A 3-T, three-dimensional (3D) T1-weighted imaging by using gradient-echo-based sequence, single shell diffusion tensor imaging by using spin-echo-based echo-planar imaging.
Network metrics including global efficiency (E ), local efficiency (E ), small world coefficient (Swc), rich-club coefficient (Φ), and nodal degree (ND) were estimated from diffusion MR (dMR) data. Connections among highly connected (hub) regions and less connected (peripheral) regions were also assessed. Correlation between the topological parameters and age for each group was also calculated to assess the development of the brain.
Linear regression model, permutation test. P values estimated from the regression model for each brain region were adjusted by false discovery rate (FDR) correction.
AS patients showed significantly lower E and higher swc compared to HC. Φ significantly increased at higher k-levels in AS patients. In addition, the connections among hub regions and peripheral regions were significantly interrupted in AS patients.
The AS brain showed diminished connectivity, reflected by reduced network efficiency compared to HC. Compared to densely connected regions, less connected regions were more vulnerable in AS.
2 TECHNICAL EFFICACY: Stage 3.
天使综合征（AS）是一种影响神经发育的遗传性疾病。目前缺乏对脑白质网络变化的研究，而这有助于更好地理解AS脑部的发病机制。
研究AS患者脑白质的局部和整体改变。
前瞻性研究。
共纳入29例AS患者（6.6±1.4岁，15例[52%]为女性）和19例年龄匹配的健康对照（HC）（7.0±1.5岁，10例[53%]为女性）。
场强/序列：采用基于梯度回波序列的3-T三维（3D）T1加权成像，以及基于自旋回波的回波平面成像的单壳扩散张量成像。
从扩散磁共振（dMR）数据中估计包括全局效率（E）、局部效率（E）、小世界系数（Swc）、富俱乐部系数（Φ）和节点度（ND）在内的网络指标。还评估了高连接（枢纽）区域和低连接（周边）区域之间的连接。计算每组拓扑参数与年龄之间的相关性，以评估大脑的发育情况。
线性回归模型、置换检验。通过错误发现率（FDR）校正对每个脑区回归模型估计的P值进行调整。
与HC相比，AS患者的E显著降低，Swc显著升高。在较高的k水平上，AS患者的Φ显著增加。此外，AS患者中枢纽区域和周边区域之间的连接明显中断。
与HC相比，AS脑部的连接性降低，表现为网络效率降低。与密集连接区域相比，AS中连接较少的区域更易受损。
2 技术效能：3级
