School of Psychology, University of Nottingham, University Park, Nottingham, NG7 2RD, UK.
Department of Psychology, Boston College, Chestnut Hill, MA, USA.
Eur J Neurosci. 2018 Mar;47(5):399-416. doi: 10.1111/ejn.13835. Epub 2018 Feb 12.
Despite interindividual differences in cortical structure, cross-sectional and longitudinal studies have demonstrated a large degree of population-level consistency in age-related differences in brain morphology. This study assessed how accurately an individual's age could be predicted by estimates of cortical morphology, comparing a variety of structural measures, including thickness, gyrification and fractal dimensionality. Structural measures were calculated across up to seven different parcellation approaches, ranging from one region to 1000 regions. The age prediction framework was trained using morphological measures obtained from T1-weighted MRI volumes collected from multiple sites, yielding a training dataset of 1056 healthy adults, aged 18-97. Age predictions were calculated using a machine-learning approach that incorporated nonlinear differences over the lifespan. In two independent, held-out test samples, age predictions had a median error of 6-7 years. Age predictions were best when using a combination of cortical metrics, both thickness and fractal dimensionality. Overall, the results reveal that age-related differences in brain structure are systematic enough to enable reliable age prediction based on metrics of cortical morphology.
尽管皮质结构存在个体间差异，但横断面和纵向研究表明，大脑形态与年龄相关的差异在人群水平上具有很大程度的一致性。本研究评估了通过皮质形态的估计值来预测个体年龄的准确性，比较了多种结构测量方法，包括厚度、脑回和分形维数。结构测量值是通过多达七种不同的分区方法计算的，范围从一个区域到 1000 个区域。年龄预测框架使用从多个站点采集的 T1 加权 MRI 体积获得的形态学测量值进行训练，产生了一个包含 1056 名年龄在 18-97 岁之间的健康成年人的训练数据集。使用机器学习方法计算年龄预测值，该方法结合了整个生命周期中的非线性差异。在两个独立的、保留的测试样本中，年龄预测的中位数误差为 6-7 岁。当使用皮质指标（厚度和分形维数）的组合时，年龄预测效果最佳。总体而言，研究结果表明，大脑结构与年龄相关的差异足够系统，能够基于皮质形态学的指标进行可靠的年龄预测。
