Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada; Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada.
Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada; Department of Accounting, Operations, and Information Systems, Alberta School of Business, University of Alberta, Edmonton, Alberta, Canada.
Neuroimage. 2020 Jun;213:116675. doi: 10.1016/j.neuroimage.2020.116675. Epub 2020 Feb 26.
Previous diffusion tensor imaging (DTI) studies confirmed the vulnerability of corpus callosum (CC) fibers to aging. However, most studies employed lower order regressions to study the relationship between age and white matter microstructure. The present study investigated whether higher order polynomial regression modelling can better describe the relationship between age and CC DTI metrics compared to lower order models in 140 healthy participants (ages 18-85). The CC was found to be non-uniformly affected by aging, with accelerated and earlier degradation occurring in anterior portion; callosal volume, fiber count, fiber length, mean fibers per voxel, and FA decreased with age while mean, axial, and radial diffusivities increased. Half of the parameters studied also displayed significant age-sex interaction or intracranial volume effects. Higher order models were chosen as the best fit, based on Bayesian Information Criterion minimization, in 16 out of 23 significant cases when describing the relationship between DTI measurements and age. Higher order model fits provided different estimations of aging trajectory peaks and decline onsets than lower order models; however, a likelihood ratio test found that higher order regressions generally did not fit the data significantly better than lower order polynomial or linear models. The results contrast the modelling approaches and highlight the importance of using higher order polynomial regression modelling when investigating associations between age and CC white matter microstructure.
先前的弥散张量成像（DTI）研究证实了胼胝体（CC）纤维对衰老的脆弱性。然而，大多数研究采用较低阶回归来研究年龄与白质微观结构之间的关系。本研究在 140 名健康参与者（年龄 18-85 岁）中，探讨了高阶多项式回归模型是否比低阶模型能更好地描述年龄与 CC DTI 指标之间的关系。研究发现，CC 受到衰老的非均匀影响，前部的降解速度更快且更早；胼胝体体积、纤维计数、纤维长度、体素内平均纤维数和 FA 随年龄下降，而平均弥散度、轴向弥散度和径向弥散度增加。在所研究的参数中，有一半还显示出显著的年龄-性别相互作用或颅内体积效应。在 23 个显著情况下，有 16 个情况基于贝叶斯信息准则最小化选择了高阶模型作为最佳拟合，以描述 DTI 测量值与年龄之间的关系。高阶模型的拟合提供了不同的衰老轨迹峰值和下降起始的估计值，而低于低阶模型；然而，似然比检验发现，高阶回归通常并不比低阶多项式或线性模型更能显著地拟合数据。研究结果对比了建模方法，并强调了在研究年龄与 CC 白质微观结构之间的关联时，使用高阶多项式回归建模的重要性。
