Chang Xuebin, Jia Xiaoyan, Eickhoff Simon B, Dong Debo, Zeng Wei
Department of Information Science, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China.
Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
Med Image Anal. 2025 Apr;101:103455. doi: 10.1016/j.media.2025.103455. Epub 2025 Jan 10.
Accurate prediction of brain age is crucial for identifying deviations between typical individual brain development trajectories and neuropsychiatric disease progression. Although current research has made progress, the effective application of brain age prediction models to multi-center datasets, particularly those with small-sample sizes, remains a significant challenge that is yet to be addressed. To this end, we propose a multi-center data correction method, which employs a domain adaptation correction strategy with Wasserstein distance of optimal transport, along with maximum mean discrepancy to improve the generalizability of brain-age prediction models on small-sample datasets. Additionally, most of the existing brain age models based on neuroimage identify the task of predicting brain age as a regression or classification problem, which may affect the accuracy of the prediction. Therefore, we propose a brain dual-modality fused convolutional neural network model (BrainDCN) for brain age prediction, and optimize this model by introducing a joint loss function of mean absolute error and cross-entropy, which identifies the prediction of brain age as both a regression and classification task. Furthermore, to highlight age-related features, we construct weighting matrices and vectors from a single-center training set and apply them to multi-center datasets to weight important features. We validate the BrainDCN model on the CamCAN dataset and achieve the lowest average absolute error compared to state-of-the-art models, demonstrating its superiority. Notably, the joint loss function and weighted features can further improve the prediction accuracy. More importantly, our proposed multi-center correction method is tested on four neuroimaging datasets and achieves the lowest average absolute error compared to widely used correction methods, highlighting the superior performance of the method in cross-center data integration and analysis. Furthermore, the application to multi-center schizophrenia data shows a mean accelerated aging compared to normal controls. Thus, this research establishes a pivotal methodological foundation for multi-center brain age prediction studies, exhibiting considerable applicability in clinical contexts, which are predominantly characterized by small-sample datasets.
准确预测脑龄对于识别典型个体脑发育轨迹与神经精神疾病进展之间的偏差至关重要。尽管当前研究已取得进展，但脑龄预测模型在多中心数据集，尤其是小样本量数据集上的有效应用，仍然是一个有待解决的重大挑战。为此，我们提出了一种多中心数据校正方法，该方法采用具有最优传输瓦瑟斯坦距离的域适应校正策略以及最大均值差异，以提高脑龄预测模型在小样本数据集上的泛化能力。此外，大多数现有的基于神经影像的脑龄模型将预测脑龄的任务视为回归或分类问题，这可能会影响预测的准确性。因此，我们提出了一种用于脑龄预测的脑双模态融合卷积神经网络模型（BrainDCN），并通过引入平均绝对误差和交叉熵的联合损失函数来优化该模型，该函数将脑龄预测视为回归和分类任务。此外，为了突出与年龄相关的特征，我们从单中心训练集中构建加权矩阵和向量，并将其应用于多中心数据集以加权重要特征。我们在CamCAN数据集上验证了BrainDCN模型，与现有最先进的模型相比，实现了最低的平均绝对误差，证明了其优越性。值得注意的是，联合损失函数和加权特征可以进一步提高预测准确性。更重要的是，我们提出的多中心校正方法在四个神经影像数据集上进行了测试，与广泛使用的校正方法相比，实现了最低的平均绝对误差，突出了该方法在跨中心数据整合和分析中的卓越性能。此外，将其应用于多中心精神分裂症数据显示，与正常对照组相比存在平均加速衰老。因此，本研究为多中心脑龄预测研究奠定了关键的方法学基础，在以小样本数据集为主的临床环境中具有相当大的适用性。
