Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada; Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, QC, Canada.
Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, QC, Canada; Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada.
Neuroimage. 2023 Mar;268:119885. doi: 10.1016/j.neuroimage.2023.119885. Epub 2023 Jan 16.
Brain maturation studies typically examine relationships linking a single morphometric feature with cognition, behavior, age, or other demographic characteristics. However, the coordinated spatiotemporal arrangement of morphological features across development and their associations with behavior are unclear. Here, we examine covariation across multiple cortical features (cortical thickness [CT], surface area [SA], local gyrification index [GI], and mean curvature [MC]) using magnetic resonance images from the NIMH developmental cohort (ages 5-25). Neuroanatomical covariance was examined using non-negative matrix factorization (NMF), which decomposes covariance resulting in a parts-based representation. Cross-sectionally, we identified six components of covariation which demonstrate differential contributions of CT, GI, and SA in hetero- vs. unimodal areas. Using this technique to examine covariance in rates of change to identify longitudinal sources of covariance highlighted preserved SA in unimodal areas and changes in CT and GI in heteromodal areas. Using behavioral partial least squares (PLS), we identified a single latent variable (LV) that recapitulated patterns of reduced CT, GI, and SA related to older age, with limited contributions of IQ and SES. Longitudinally, PLS revealed three LVs that demonstrated a nuanced developmental pattern that highlighted a higher rate of maturational change in SA and CT in higher IQ and SES females. Finally, we situated the components in the changing architecture of cortical gradients. This novel characterization of brain maturation provides an important understanding of the interdependencies between morphological measures, their coordinated development, and their relationship to biological sex, cognitive ability, and the resources of the local environment.
大脑成熟度研究通常研究将单一形态特征与认知、行为、年龄或其他人口统计学特征联系起来的关系。然而，形态特征在整个发育过程中的协调时空排列及其与行为的关联尚不清楚。在这里，我们使用来自 NIMH 发育队列（年龄 5-25 岁）的磁共振成像检查了多个皮质特征（皮质厚度[CT]、表面积[SA]、局部回旋指数[GI]和平均曲率[MC]）之间的协变。使用非负矩阵分解（NMF）检查神经解剖协方差，该方法分解协方差，从而产生基于部分的表示。横向分析中，我们确定了六个协变分量，这些分量展示了 CT、GI 和 SA 在异模态与单模态区域中的不同贡献。使用该技术检查变率的协方差以识别协变的纵向来源，突出了单模态区域中 SA 的保留和异模态区域中 CT 和 GI 的变化。使用行为偏最小二乘法（PLS），我们确定了一个单一的潜在变量（LV），该变量重现了与年龄较大相关的 CT、GI 和 SA 减少的模式，而 IQ 和 SES 的贡献有限。纵向来看，PLS 揭示了三个 LV，它们表现出微妙的发展模式，突出了高 IQ 和 SES 女性在 SA 和 CT 中更高的成熟变化率。最后，我们将这些成分置于皮质梯度不断变化的结构中。这种对大脑成熟的新特征描述提供了对形态测量之间的相互依存关系、它们的协调发展及其与生物性别、认知能力和局部环境资源的关系的重要理解。
