Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, South Bend, Indiana, USA.
Department of Statistics, Rutgers University, New Brunswick, New Jersey, USA.
Hum Brain Mapp. 2023 Jun 1;44(8):3168-3179. doi: 10.1002/hbm.26271. Epub 2023 Mar 10.
Brain growth in early childhood is reflected in the evolution of proportional cerebrospinal fluid volumes (pCSF), grey matter (pGM), and white matter (pWM). We study brain development as reflected in the relative fractions of these three tissues for a cohort of 388 children that were longitudinally followed between the ages of 18 and 96 months. We introduce statistical methodology (Riemannian Principal Analysis through Conditional Expectation, RPACE) that addresses major challenges that are of general interest for the analysis of longitudinal neuroimaging data, including the sparsity of the longitudinal observations over time and the compositional structure of the relative brain volumes. Applying the RPACE methodology, we find that longitudinal growth as reflected by tissue composition differs significantly for children of mothers with higher and lower maternal education levels.
儿童早期的大脑生长反映在比例性脑脊液体积（pCSF）、灰质（pGM）和白质（pWM）的演变上。我们对 388 名儿童的队列进行了纵向随访，研究了这些组织的相对分数所反映的大脑发育情况，这些儿童的年龄在 18 至 96 个月之间。我们引入了统计方法学（通过条件期望的黎曼主分析，RPACE），该方法学解决了对纵向神经影像学数据进行分析时普遍感兴趣的主要挑战，包括随时间推移的纵向观测的稀疏性以及相对脑容量的组成结构。应用 RPACE 方法学，我们发现，反映在组织组成上的纵向生长在母亲受教育程度较高和较低的儿童中存在显著差异。
