标题： 基于协方差神经网络的神经退行性病变中可解释的大脑年龄差距预测 显示英文标题

标题： Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks

摘要： 脑龄是从神经影像数据集中通过机器学习算法估算出的生物年龄。 增加的\textit{脑年龄差}以相对于实际年龄的脑龄升高为特征，可以反映神经退行性和认知下降的易感性增加。 因此，脑龄差距是一个有前景的监测大脑健康的生物标志物。 然而，用于脑龄差距预测的黑箱机器学习方法实用性有限。 关于协方差神经网络（VNN）的最新研究提出了一种相对透明的深度学习流程用于神经影像数据分析，该流程具有两个关键特征：(i) 所得生物标志物的内在\textit{解剖学可解释性}；以及 (ii) 基于\textit{特征向量的解剖协方差矩阵关联}的方法学可解释视角。 在本文中，我们将基于 VNN 的方法应用于使用皮层厚度特征研究各种常见神经退行性疾病的脑龄差距。 我们的结果揭示了阿尔茨海默病、额颞叶痴呆和不典型帕金森综合征中脑龄差距的不同解剖模式。 此外，我们证明了脑龄差距的不同解剖模式与 VNN 如何利用解剖协方差矩阵的本征谱有关，从而为所报告的结果提供了可解释性。 显示英文摘要

摘要： Brain age is the estimate of biological age derived from neuroimaging datasets using machine learning algorithms. Increasing \textit{brain age gap} characterized by an elevated brain age relative to the chronological age can reflect increased vulnerability to neurodegeneration and cognitive decline. Hence, brain age gap is a promising biomarker for monitoring brain health. However, black-box machine learning approaches to brain age gap prediction have limited practical utility. Recent studies on coVariance neural networks (VNN) have proposed a relatively transparent deep learning pipeline for neuroimaging data analyses, which possesses two key features: (i) inherent \textit{anatomically interpretablity} of derived biomarkers; and (ii) a methodologically interpretable perspective based on \textit{linkage with eigenvectors of anatomic covariance matrix}. In this paper, we apply the VNN-based approach to study brain age gap using cortical thickness features for various prevalent neurodegenerative conditions. Our results reveal distinct anatomic patterns for brain age gap in Alzheimer's disease, frontotemporal dementia, and atypical Parkinsonian disorders. Furthermore, we demonstrate that the distinct anatomic patterns of brain age gap are linked with the differences in how VNN leverages the eigenspectrum of the anatomic covariance matrix, thus lending explainability to the reported results.