Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China (Y.S., X.Z., X.Z., C.W., Z.Y., Z.F., X.Z.); Henan International Joint Laboratory of Neuroimaging, Zhengzhou, 450052, China (Y.S., X.Z., X.Z., C.W., Z.Y., Z.F., X.Z.).
MR Research China, GE Healthcare, Beijing, 100000, PR China (K.W.).
Acad Radiol. 2024 May;31(5):2074-2084. doi: 10.1016/j.acra.2023.12.023. Epub 2024 Jan 6.
This study employed tract-based spatial statistics (TBSS) to investigate abnormalities in the white matter microstructure among children with autism spectrum disorder (ASD). Additionally, an eXtreme Gradient Boosting (XGBoost) model was developed to effectively classify individuals with ASD and typical developing children (TDC).
Multi-shell diffusion weighted images were acquired from 62 children with ASD and 44 TDC. Using the Pydesigner procedure, diffusion tensor (DT), diffusion kurtosis (DK), and white matter tract integrity (WMTI) metrics were computed. Subsequently, TBSS analysis was applied to discern differences in these diffusion parameters between ASD and TDC groups. The XGBoost model was then trained using metrics showing significant differences, and Shapley Additive explanations (SHAP) values were computed to assess the feature importance in the model's predictions.
TBSS analysis revealed a significant reduction in axonal diffusivity (AD) in the left posterior corona radiata and the right superior corona radiata. Among the DK indicators, mean kurtosis, axial kurtosis, and kurtosis fractional anisotropy were notably increased in children with ASD, with no significant difference in radial kurtosis. WMTI metrics such as axonal water fraction, axonal diffusivity of the extra-axonal space (EAS_AD), tortuosity of the extra-axonal space (EAS_TORT), and diffusivity of intra-axonal space (IAS_Da) were significantly increased, primarily in the corpus callosum and fornix. Notably, there was no significant difference in radial diffusivity of the extra-axial space (EAS_RD). The XGBoost model demonstrated excellent classification ability, and the SHAP analysis identified EAS_TORT as the feature with the highest importance in the model's predictions.
This study utilized TBSS analyses with multi-shell diffusion data to examine white matter abnormalities in pediatric autism. Additionally, the developed XGBoost model showed outstanding performance in classifying ASD and TDC. The ranking of SHAP values based on the XGBoost model underscored the significance of features in influencing model predictions.
本研究采用基于体素的空间统计学（TBSS）分析，探讨自闭症谱系障碍（ASD）患儿的白质微观结构异常。此外，还构建了极端梯度提升（XGBoost）模型，以有效区分 ASD 患儿和典型发育儿童（TDC）。
共纳入 62 例 ASD 患儿和 44 例 TDC 的多壳层扩散加权成像数据。采用 Pydesigner 程序计算扩散张量（DT）、扩散峰度（DK）和白质束完整性（WMTI）指标。然后，采用 TBSS 分析比较 ASD 组和 TDC 组间各扩散参数的差异。采用具有显著差异的指标训练 XGBoost 模型，并计算 Shapley Additive explanations（SHAP）值以评估模型预测中特征的重要性。
TBSS 分析显示，左侧后放射冠和右侧上放射冠的轴突弥散度（AD）显著降低。DK 指标中，平均峰度、轴向峰度和峰度各向异性分数在 ASD 患儿中显著增加，而放射峰度无显著差异。WMTI 指标中，轴突水分数、细胞外空间轴突弥散度（EAS_AD）、细胞外空间曲折度（EAS_TORT）和轴内空间弥散度（IAS_Da）显著增加，主要位于胼胝体和穹窿。值得注意的是，细胞外空间放射峰度弥散度（EAS_RD）无显著差异。XGBoost 模型具有良好的分类能力，SHAP 分析表明 EAS_TORT 是模型预测中最重要的特征。
本研究采用多壳层扩散数据的 TBSS 分析，探讨了儿科自闭症的白质异常。此外，构建的 XGBoost 模型在 ASD 和 TDC 的分类中表现出色。基于 XGBoost 模型的 SHAP 值排序强调了特征在影响模型预测方面的重要性。
