Aston Institute of Health and Neurodevelopment, College of Health and Life Sciences, Aston University, Birmingham, B4 7ET, UK.
School of Psychology, Faculty of Health, Melbourne Burwood Campus, Deakin University, Geelong, VIC, Australia.
Sci Rep. 2023 Sep 20;13(1):15591. doi: 10.1038/s41598-023-42414-5.
Brain development is regularly studied using structural MRI. Recently, studies have used a combination of statistical learning and large-scale imaging databases of healthy children to predict an individual's age from structural MRI. This data-driven, predicted 'Brainage' typically differs from the subjects chronological age, with this difference a potential measure of individual difference. Few studies have leveraged higher-order or connectomic representations of structural MRI data for this Brainage approach. We leveraged morphometric similarity as a network-level approach to structural MRI to generate predictive models of age. We benchmarked these novel Brainage approaches using morphometric similarity against more typical, single feature (i.e., cortical thickness) approaches. We showed that these novel methods did not outperform cortical thickness or cortical volume measures. All models were significantly biased by age, but robust to motion confounds. The main results show that, whilst morphometric similarity mapping may be a novel way to leverage additional information from a T1-weighted structural MRI beyond individual features, in the context of a Brainage framework, morphometric similarity does not provide more accurate predictions of age. Morphometric similarity as a network-level approach to structural MRI may be poorly positioned to study individual differences in brain development in healthy participants in this way.
大脑发育通常使用结构磁共振成像（MRI）进行研究。最近，研究人员结合统计学习和大规模健康儿童的成像数据库，从结构 MRI 预测个体的年龄。这种数据驱动的、预测的“脑龄”通常与受测者的实际年龄不同，这种差异可能是个体差异的一个潜在衡量标准。很少有研究利用结构 MRI 数据的高阶或连接组学表示来进行这种脑龄方法。我们利用形态相似性作为结构 MRI 的网络水平方法来生成年龄的预测模型。我们使用形态相似性对这些新的脑龄方法进行了基准测试，与更典型的单一特征（即皮质厚度）方法进行了比较。我们发现这些新方法并没有优于皮质厚度或皮质体积测量。所有模型都受到年龄的显著影响，但对运动混杂因素具有稳健性。主要结果表明，尽管形态相似性映射可能是一种从 T1 加权结构 MRI 中获取除个体特征之外的额外信息的新方法，但在脑龄框架中，形态相似性并不能更准确地预测年龄。形态相似性作为结构 MRI 的网络水平方法，可能无法以这种方式很好地研究健康参与者大脑发育的个体差异。
