IEEE Trans Neural Syst Rehabil Eng. 2022;30:2146-2156. doi: 10.1109/TNSRE.2022.3190467. Epub 2022 Aug 9.
Precise prediction on brain age is urgently needed by many biomedical areas including mental rehabilitation prognosis as well as various medicine or treatment trials. People began to realize that contrasting physical (real) age and predicted brain age can help to highlight brain issues and evaluate if patients' brains are healthy or not. Such age prediction is often challenging for single model-based prediction, while the conditions of brains vary drastically over age. In this work, we present an age-adaptive ensemble model that is based on the combination of four different machine learning algorithms, including a support vector machine (SVR), a convolutional neural network (CNN) model, and the popular GoogLeNet and ResNet deep networks. The ensemble model proposed here is nonlinearly adaptive, where age is taken as a key factor in the nonlinear combination of various single-algorithm-based independent models. In our age-adaptive ensemble method, the weights of each model are learned automatically as nonlinear functions over age instead of fixed values, while brain age estimation is based on such an age-adaptive integration of various single models. The quality of the model is quantified by the mean absolute errors (MAE) and spearman correlation between the predicted age and the actual age, with the least MAE and the highest Spearman correlation representing the highest accuracy in age prediction. By testing on the Predictive Analysis Challenge 2019 (PAC 2019) dataset, our novel ensemble model has achieved a MAE down to 3.19, which is a significantly increased accuracy in this brain age competition. If deployed in the real world, our novel ensemble model having an improved accuracy could potentially help doctors to identify the risk of brain diseases more accurately and quickly, thus helping pharmaceutical companies develop drugs or treatments precisely, and potential offer a new powerful tool for researchers in the field of brain science.
精确预测大脑年龄是许多生物医学领域所急需的，包括精神康复预后以及各种医学或治疗试验。人们开始意识到，对比生理（实际）年龄和预测的大脑年龄可以帮助突出大脑问题，并评估患者的大脑是否健康。这种年龄预测对于基于单一模型的预测通常具有挑战性，而大脑的状况在年龄上差异很大。在这项工作中，我们提出了一种基于四个不同机器学习算法（包括支持向量机（SVR）、卷积神经网络（CNN）模型以及流行的 GoogLeNet 和 ResNet 深度网络）组合的年龄自适应集成模型。这里提出的集成模型是非线性自适应的，其中年龄被视为各种基于单一算法的独立模型的非线性组合中的关键因素。在我们的年龄自适应集成方法中，每个模型的权重被自动学习为非线性函数，而不是固定值，而大脑年龄估计则基于这种对各种单一模型的年龄自适应集成。模型的质量通过预测年龄和实际年龄之间的平均绝对误差（MAE）和斯皮尔曼相关性来量化，其中 MAE 最小和斯皮尔曼相关性最高表示年龄预测的最高准确性。通过在预测分析挑战赛 2019 年（PAC 2019）数据集上进行测试，我们的新型集成模型取得了 3.19 的 MAE，这在大脑年龄竞赛中显著提高了准确性。如果在现实世界中部署，我们的新型集成模型具有更高的准确性，可能有助于医生更准确和快速地识别大脑疾病的风险，从而帮助制药公司精确开发药物或治疗方法，并为大脑科学领域的研究人员提供一种新的强大工具。
