一种用于脑年龄建模的不确定性感知、可共享和透明的神经网络结构
An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling
摘要：
实际年龄和从神经影像学数据预测的年龄之间的偏差已被确定为交叉障碍大脑变化的敏感风险标志物，逐渐成为生物学年龄研究的基石。然而，作为该领域基础的机器学习模型不考虑不确定性，从而将结果与训练数据密度和可变性混淆。此外，现有的模型通常基于同构的训练集，通常没有独立验证，并且由于数据保护问题而无法共享。在这里，我们介绍了一个不确定性感知，可共享和透明的Monte-Carlo Dropout Composite-Quantile-Regression（MCCQR）神经网络，该网络在来自德国国家队列的N= 10，691个数据集上进行训练。MCCQR模型在高维神经影像学数据中提供了稳健的、无分布的不确定性量化，与10个招募中心和3个独立验证样本（N= 4，004）中的现有模型相比，其出错率更低。在两个例子中，我们证明了它可以防止虚假的关联，并增加检测加速大脑老化的能力。我们将预先训练好的模型公开。
英文摘要：
The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk-marker of cross-disorder brain changes, growing into a cornerstone of biological age-research. However, Machine Learning models underlying the field do not consider uncertainty, thereby confounding results with training data density and variability. Also, existing models are commonly based on homogeneous training sets, often not independently validated, and cannot be shared due to data protection issues. Here, we introduce an uncertainty-aware, shareable, and transparent Monte-Carlo Dropout Composite-Quantile-Regression (MCCQR) Neural Network trained on N=10,691 datasets from the German National Cohort. The MCCQR model provides robust, distribution-free uncertainty quantification in high-dimensional neuroimaging data, achieving lower error rates compared to existing models across ten recruitment centers and in three independent validation samples (N=4,004). In two examples, we demonstrate that it prevents spurious associations and increases power to detect accelerated brain-aging. We make the pre-trained model publicly available.
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