协方差特征空间为脑年龄差距中的纵向效应提供潜在归因
Covariance Eigenspace Provides Latent Attribution of Longitudinal Effects in Brain Age Gap
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中文总结 AI 辅助
本研究利用协方差神经网络和积分梯度归因，发现协方差特征谱比单个脑区更能解释MCI队列中脑年龄差距的纵向效应，前导特征向量归因达83.2%。
中文摘要 AI 辅助
脑年龄差距是一种由机器学习（ML）驱动的、源自神经影像数据的加速生物衰老的有前景的生物标志物。最近关于用于脑年龄差距预测的协方差神经网络（VNN）的研究，为脑年龄差距预测提供了一个可解释且解剖学上可验证的框架。值得注意的是，VNN的结果是通过沿协方差特征向量对输入进行滤波而获得的，这提供了一种根据潜在（正交）向量来解释其决策的机制。在本文中，我们使用VNN驱动的脑年龄差距预测框架，研究了轻度认知障碍（MCI）队列中的纵向脑年龄差距效应。具体而言，使用积分梯度（IG）作为归因机制，我们的结果表明，解剖协方差矩阵的特征谱比单个脑区域更能显著解释在淀粉样蛋白阳性与阴性亚队列中观察到的脑年龄差距的不同纵向效应（前导特征向量为83.2%，而任何脑区域的最大值为7.7%）。因此，基于协方差的潜在解释识别了一个关键的协调输入模式，该模式承载了观察到的脑年龄差距纵向效应的大部分。
英文摘要
Brain age gap is a promising machine learning (ML)-driven bio\-marker of accelerated biological aging and is derived from neuroimaging data. Recent works on coVariance neural networks (VNN) for brain age gap prediction have provided an explainable and anatomically verifiable framework for brain age gap prediction. Notably, VNN outcomes are derived by filtering inputs along covariance eigenvectors, providing a mechanism for explaining their decisions in terms of latent (orthogonal) vectors. In this paper, we investigate the longitudinal brain age gap effects in a Mild Cognitive Impairment (MCI) cohort using a VNN-driven brain age gap prediction framework. Specifically, using Integrated Gradients (IG) as the attribution mechanism, our results demonstrate that the eigenspectrum of the anatomical covariance matrix explains the different longitudinal effects observed in brain age gap in amyloid positive vs amyloid negative subcohorts more prominently than individual brain regions ($83.2\%$ with the leading eigenvector versus a maximal value of $7.7\%$ for any brain region). Therefore, the covariance-based latent explanation identifies a key coordinated input pattern that carries most of the observed longitudinal effect in brain age gap.
