论文标题
大脑结构连接组的运动不变性自动编码
Motion-Invariant Variational Auto-Encoding of Brain Structural Connectomes
论文作者
论文摘要
通过扩散MRI对人脑结构连接的映射提供了一个独特的机会，可以理解大脑结构性连通性并将其与各种人类特征（例如认知）相关联。但是，图像采集过程中的头部位移会损害连接组重建的准确性和随后的推理结果。我们开发了一个生成模型，以学习与运动引起的伪影不变的结构连接组的低维表示，以便我们可以更准确地连接大脑网络和人类特征，并产生运动调整后的连接组。我们将提出的模型应用于青少年脑认知发展（ABCD）研究和人类连接组项目（HCP）的数据，以研究我们的运动不变连接符如何促进对大脑网络及其与认知的关系的理解。经验结果表明，所提出的运动不变的变异自动编码器（Inv-VAE）在各个方面都优于其竞争对手。特别是，与没有运动调整的其他方法相比，与运动调整的结构连接组相比，与其他认知相关的特征更加密切相关。
Mapping of human brain structural connectomes via diffusion MRI offers a unique opportunity to understand brain structural connectivity and relate it to various human traits, such as cognition. However, head displacement during image acquisition can compromise the accuracy of connectome reconstructions and subsequent inference results. We develop a generative model to learn low-dimensional representations of structural connectomes invariant to motion-induced artifacts, so that we can link brain networks and human traits more accurately, and generate motion-adjusted connectomes. We apply the proposed model to data from the Adolescent Brain Cognitive Development (ABCD) study and the Human Connectome Project (HCP) to investigate how our motion-invariant connectomes facilitate understanding of the brain network and its relationship with cognition. Empirical results demonstrate that the proposed motion-invariant variational auto-encoder (inv-VAE) outperforms its competitors in various aspects. In particular, motion-adjusted structural connectomes are more strongly associated with a wide array of cognition-related traits than other approaches without motion adjustment.
