Aberrant white matter microstructure detected by automatic fiber quantification in pediatric myelin oligodendrocyte glycoprotein antibody-associated disease

作者全名："Ding, Shuang; Shi, Zhuowei; Huang, Kaiping; Fan, Xiao; Li, Xiujuan; Zheng, Helin; Wang, Longlun; Yan, Zichun; Cai, Jinhua"

作者地址："[Ding, Shuang; Huang, Kaiping; Fan, Xiao; Zheng, Helin; Wang, Longlun; Cai, Jinhua] Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Minist Educ,Key Lab Child Dev & Disorders, Dept Radiol,Childrens Hosp,Chongqing Key Lab Child, Chongqing 400014, Peoples R China; [Shi, Zhuowei; Yan, Zichun] Chongqing Med Univ, Dept Radiol, Affiliated Hosp 1, Chongqing 400000, Peoples R China; [Li, Xiujuan] Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Dept Neonatol,Minist Educ,Key Lab Child Dev & Diso, Childrens Hosp,Chongqing Key Lab Child Neurodev &, Chongqing 400014, Peoples R China"

通信作者："Cai, JH (通讯作者)，Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Minist Educ,Key Lab Child Dev & Disorders, Dept Radiol,Childrens Hosp,Chongqing Key Lab Child, Chongqing 400014, Peoples R China."

来源：MULTIPLE SCLEROSIS AND RELATED DISORDERS

ESI学科分类：NEUROSCIENCE & BEHAVIOR

WOS号：WOS:001186322400001

JCR分区：Q2

影响因子：4

年份：2024

卷号：84

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：Automated fiber quantification; Diffusion tensor imaging; Machine learning; Myelin oligodendrocyte glycoprotein antibody associated disease; Pediatrics

摘要："Background and objectives: Myelin oligodendrocyte glycoprotein antibody-associated diseases (MOGAD) is an idiopathic inflammatory demyelinating disorder in children, for which the precise damage patterns of the white matter (WM) fibers remain unclear. Herein, we utilized diffusion tensor imaging (DTI)-based automated fiber quantification (AFQ) to identify patterns of fiber damage and to investigate the clinical significance of MOGAD-affected fiber tracts. Methods: A total of 28 children with MOGAD and 31 healthy controls were included in this study. The AFQ approach was employed to track WM fiber with 100 equidistant nodes defined along each tract for statistical analysis of DTI metrics in both the entire and nodal manner. The feature selection method was used to further screen significantly aberrant DTI metrics of the affected fiber tracts or segments for eight common machine learning (ML) to evaluate their potential in identifying MOGAD. These metrics were then correlated with clinical scales to assess their potential as imaging biomarkers. Results: In the entire manner, significantly reduced fractional anisotropy (FA) was shown in the left anterior thalamic radiation, arcuate fasciculus, and the posterior and anterior forceps of corpus callosum in MOGAD (all p < 0.05). In the nodal manner, significant DTI metrics alterations were widely observed across 37 segments in 10 fiber tracts (all p < 0.05), mainly characterized by decreased FA and increased radial diffusivity (RD). Among them, 14 DTI metrics in seven fiber tracts were selected as important features to establish ML models, and satisfactory discrimination of MOGAD was obtained in all models (all AUC > 0.85), with the best performance in the logistic regression model (AUC = 0.952). For those features, the FA of left cingulum cingulate and the RD of right inferior frontal-occipital fasciculus were negatively and positively correlated with the expanded disability status scale (r = -0.54, p = 0.014; r = 0.43, p = 0.03), respectively. Conclusion: Pediatric MOGAD exhibits extensive WM fiber tract aberration detected by AFQ. Certain fiber tracts exhibit specific patterns of DTI metrics that hold promising potential as biomarkers."

基金机构：Postgraduate Innovation Research Project of Chongqing [CYB22212]

基金资助正文：Funding This study received funding from the Postgraduate Innovation Research Project of Chongqing (No. CYB22212) .