Department of Child and Adolescent Psychiatry, Hassenfeld Children's Hospital at NYU Langone, New York, NY, 10016, USA; Center of Alcohol and Substance Use Studies, Department of Applied Psychology, Rutgers University, Piscataway, NJ 08854, USA.
MATTER Lab, Child Mind Institute, New York, NY, 10022, USA.
Neuroimage. 2019 Nov 15;202:116149. doi: 10.1016/j.neuroimage.2019.116149. Epub 2019 Aug 30.
Cortical development is characterized by distinct spatial and temporal patterns of maturational changes across various cortical shape measures. There is a growing interest in summarizing complex developmental patterns into a single index, which can be used to characterize an individual's brain age. We conducted this study with two primary aims. First, we sought to quantify covariation patterns for a variety of cortical shape measures, including cortical thickness, gray matter volume, surface area, mean curvature, and travel depth, as well as white matter volume, and subcortical gray matter volume. We examined these measures in a sample of 869 participants aged 5-18 from the Healthy Brain Network (HBN) neurodevelopmental cohort using the Joint and Individual Variation Explained (Lock et al., 2013) method. We validated our results in an independent dataset from the Nathan Kline Institute - Rockland Sample (NKI-RS; N = 210) and found remarkable consistency for some covariation patterns. Second, we assessed whether covariation patterns in the brain can be used to accurately predict a person's chronological age. Using ridge regression, we showed that covariation patterns can predict chronological age with high accuracy, reflected by our ability to cross-validate our model in an independent sample with a correlation coefficient of 0.84 between chronologic and predicted age. These covariation patterns also predicted sex with high accuracy (AUC = 0.85), and explained a substantial portion of variation in full scale intelligence quotient (R = 0.10). In summary, we found significant covariation across different cortical shape measures and subcortical gray matter volumes. In addition, each shape measure exhibited distinct covariations that could not be accounted for by other shape measures. These covariation patterns accurately predicted chronological age, sex and general cognitive ability. In a subset of NKI-RS, test-retest (<1 month apart, N = 120) and longitudinal scans (1.22 ± 0.29 years apart, N = 77) were available, allowing us to demonstrate high reliability for the prediction models obtained and the ability to detect subtle differences in the longitudinal scan interval among participants (median and median absolute deviation of absolute differences between predicted age difference and real age difference = 0.53 ± 0.47 years, r = 0.24, p-value = 0.04).
皮质发育的特点是在各种皮质形状测量中存在独特的时空成熟变化模式。人们越来越感兴趣的是将复杂的发育模式总结为一个单一的指标，这个指标可以用来描述个体的大脑年龄。我们进行这项研究有两个主要目的。首先，我们试图量化多种皮质形状测量值的协变模式，包括皮质厚度、灰质体积、表面积、平均曲率和行程深度，以及白质体积和皮质下灰质体积。我们使用 Joint and Individual Variation Explained（Lock 等人，2013）方法，在健康大脑网络（HBN）神经发育队列的 869 名 5-18 岁的参与者样本中检查了这些测量值。我们在来自 Nathan Kline Institute - Rockland Sample（NKI-RS；N=210）的独立数据集上验证了我们的结果，并发现一些协变模式非常一致。其次，我们评估了大脑中的协变模式是否可以准确预测一个人的实际年龄。使用脊回归，我们表明，协变模式可以非常准确地预测实际年龄，我们在独立样本中的交叉验证模型的相关系数为 0.84，实际年龄和预测年龄之间的相关性很高。这些协变模式也可以非常准确地预测性别（AUC=0.85），并解释了全量表智商的大部分变化（R=0.10）。总之，我们发现不同皮质形状测量值和皮质下灰质体积之间存在显著的协变。此外，每种形状测量值都表现出独特的协变，无法用其他形状测量值来解释。这些协变模式可以准确地预测实际年龄、性别和一般认知能力。在 NKI-RS 的一个子集中，测试-重测（相隔不到 1 个月，N=120）和纵向扫描（相隔 1.22±0.29 年，N=77）可用，这使我们能够证明获得的预测模型的高可靠性，以及检测参与者之间的纵向扫描间隔细微差异的能力（预测年龄差异与实际年龄差异之间的绝对差异的中位数和中位数绝对偏差=0.53±0.47 年，r=0.24，p 值=0.04）。
