Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing 400014, China.
Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400000, China.
Mult Scler Relat Disord. 2024 Apr;84:105483. doi: 10.1016/j.msard.2024.105483. Epub 2024 Feb 9.
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.
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.
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.
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.
髓鞘少突胶质细胞糖蛋白抗体相关疾病(MOGAD)是一种儿童特发性炎症性脱髓鞘疾病,其脑白质(WM)纤维的确切损伤模式尚不清楚。本研究采用基于弥散张量成像(DTI)的自动纤维定量(AFQ)技术来识别纤维损伤模式,并探讨 MOGAD 相关纤维束的临床意义。
本研究共纳入 28 例 MOGAD 患儿和 31 名健康对照者。采用 AFQ 方法追踪 WM 纤维,在每个纤维束上定义 100 个等距节点,以进行整个纤维束和节点的 DTI 指标的统计学分析。采用特征选择方法进一步筛选 8 种常见机器学习(ML)中受影响纤维束或节段的显著异常 DTI 指标,以评估其识别 MOGAD 的能力。然后,这些指标与临床量表相关联,以评估其作为影像学生物标志物的潜力。
在整个纤维束方式中,MOGAD 患儿左侧丘脑前辐射、弓状束和胼胝体后、前部纤维束的分数各向异性(FA)明显降低(均 p<0.05)。在节点方式中,10 条纤维束的 37 个节段中广泛观察到显著的 DTI 指标改变(均 p<0.05),主要表现为 FA 降低和径向扩散系数(RD)增加。其中,7 条纤维束中的 14 个 DTI 指标被选为重要特征来建立 ML 模型,所有模型均能很好地区分 MOGAD(所有 AUC>0.85),其中逻辑回归模型的性能最佳(AUC=0.952)。对于这些特征,左侧扣带束扣带和右侧额枕下额束的 FA 值与扩展残疾状况量表呈负相关(r=-0.54,p=0.014)和正相关(r=0.43,p=0.03),RD 值与扩展残疾状况量表呈正相关。
儿科 MOGAD 表现为广泛的 WM 纤维束异常,AFQ 可检测到这种异常。某些纤维束具有特定的 DTI 指标模式,具有作为生物标志物的潜在前景。