Through the application of machine learning algorithms to neuroimaging data the brain age methodology was shown to provide a useful individual-level biological age prediction and identify key brain regions responsible for the prediction. In this study, we present the methodology of constructing a rhesus macaque brain age model using a machine learning algorithm and discuss the key predictive brain regions in comparison to the human brain, to shed light on cross-species primate similarities and differences. Structural information of the brain (e.g., parcellated volumes) from brain magnetic resonance imaging of 43 rhesus macaques were used to develop brain atlas-based features to build a brain age model that predicts biological age. The best-performing model used 22 selected features and achieved an R of 0.72. We also identified interpretable predictive brain features including Right Fronto-orbital Cortex, Right Frontal Pole, Right Inferior Lateral Parietal Cortex, and Bilateral Posterior Central Operculum. Our findings provide converging evidence of the parallel and comparable brain regions responsible for both non-human primates and human biological age prediction.

中文翻译：

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恒河猴一生中的大脑年龄

通过将机器学习算法应用于神经影像数据，大脑年龄方法被证明可以提供有用的个体层面的生物年龄预测，并识别负责预测的关键大脑区域。在这项研究中，我们提出了使用机器学习算法构建恒河猴大脑年龄模型的方法，并讨论了与人类大脑相比的关键预测大脑区域，以揭示跨物种灵长类动物的相似性和差异。来自 43 只恒河猴脑磁共振成像的大脑结构信息（例如，分区体积）被用来开发基于脑图谱的特征，以建立预测生物年龄的脑年龄模型。性能最佳的模型使用了 22 个选定的特征，R 值为 0.72。我们还确定了可解释的预测性大脑特征，包括右额眶皮层、右额极、右下外侧顶叶皮层和双侧后中央盖。我们的研究结果提供了负责非人类灵长类动物和人类生物年龄预测的平行且可比较的大脑区域的汇聚证据。