Disrupted Topological Organization of White Matter Network in Angelman Syndrome

• Published in: Journal of magnetic resonance imaging Vol. 57; no. 4; pp. 1212 - 1221
• Main Authors: Wei, Lei, Du, Xiaonan, Yang, Zidong, Ding, Ming, Yang, Baofeng, Wang, Ji, Long, Shasha, Qiao, Zhongwei, Jiang, Yonghui, Wang, Yi, Wang, He
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.04.2023; Wiley Subscription Services, Inc
• Subjects: Angelman syndrome; Angelman Syndrome - pathology; Angelman's syndrome; Brain; Brain - pathology; Child; Child, Preschool; Diffusion; diffusion MRI; Diffusion Tensor Imaging - methods; Efficiency; Female; Females; Field strength; Genetic disorders; Humans; Magnetic resonance imaging; Male; Mathematical models; Medical imaging; Model testing; network analysis; Neural networks; Neurodevelopmental disorders; Neuroimaging; Pathogenesis; Permutations; Prospective Studies; Regression models; Statistical analysis; Statistical tests; Substantia alba; Tensors; Topology; White Matter; diffusion MRI; Angelman syndrome; network analysis
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.28360

Background 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. Purpose To investigate both local and global alterations of white matter in patients with AS. Study Type Prospective. Subjects 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. Assessment Network metrics including global efficiency (Eg), local efficiency (Eloc), 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. Statistical Tests Linear regression model, permutation test. P values estimated from the regression model for each brain region were adjusted by false discovery rate (FDR) correction. Results AS patients showed significantly lower Eg and higher swc compared to HC. Φn 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. Data Conclusion 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. Evidence Level 2 Technical Efficacy Stage 3