Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, Brooklyn, NY, USA.
Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA.
Neuroradiol J. 2023 Dec;36(6):693-701. doi: 10.1177/19714009231177396. Epub 2023 May 22.
Repeated head impacts (RHI) without concussion may cause long-term sequelae. A growing array of diffusion MRI metrics exist, both empiric and modeled and it is hard to know which are potentially important biomarkers. Common conventional statistical methods fail to consider interactions between metrics and rely on group-level comparisons. This study uses a classification pipeline as a means towards identifying important diffusion metrics associated with subconcussive RHI.
36 collegiate contact sport athletes and 45 non-contact sport controls from FITBIR CARE were included. Regional/whole brain WM statistics were computed from 7 diffusion metrics. Wrapper-based feature selection was applied to 5 classifiers representing a range of learning capacities. Best 2 classifiers were interpreted to identify the most RHI-related diffusion metrics.
Mean diffusivity (MD) and mean kurtosis (MK) are found to be the most important metrics for discriminating between athletes with and without RHI exposure history. Regional features outperformed global statistics. Linear approaches outperformed non-linear approaches with good generalizability (test AUC 0.80-0.81).
Feature selection and classification identifies diffusion metrics that characterize subconcussive RHI. Linear classifiers yield the best performance and mean diffusion, tissue microstructure complexity, and radial extra-axonal compartment diffusion (MD, MK, D) are found to be the most influential metrics. This work provides proof of concept that applying such approach to small, multidimensional dataset can be successful given attention to optimizing learning capacity without overfitting and serves an example of methods that lead to better understanding of the myriad of diffusion metrics as they relate to injury and disease.
无脑震荡的重复头部冲击（RHI）可能会导致长期后遗症。目前存在大量扩散 MRI 指标，包括经验性和模型化指标，很难知道哪些是潜在的重要生物标志物。常见的常规统计方法未能考虑指标之间的相互作用，并且依赖于组水平比较。本研究使用分类管道作为一种识别与亚脑震荡性 RHI 相关的重要扩散指标的方法。
FITBIR CARE 纳入了 36 名大学生接触性运动运动员和 45 名非接触性运动对照者。从 7 种扩散指标计算了区域性/全脑 WM 统计数据。基于包装器的特征选择应用于代表一系列学习能力的 5 个分类器。最佳的 2 个分类器用于识别与 RHI 相关的最扩散指标。
发现平均弥散度（MD）和平均峰度（MK）是区分有和无 RHI 暴露史运动员的最重要指标。区域特征优于全局统计。线性方法优于非线性方法，具有良好的泛化能力（测试 AUC 为 0.80-0.81）。
特征选择和分类确定了用于描述亚脑震荡性 RHI 的扩散指标。线性分类器的性能最佳，平均扩散、组织微观结构复杂性和径向细胞外轴突扩散（MD、MK、D）是最具影响力的指标。这项工作提供了一个概念验证，即在注意优化学习能力而不产生过拟合的情况下，将这种方法应用于小的、多维数据集可以取得成功，并为理解与损伤和疾病相关的众多扩散指标的方法提供了一个范例。
