同步非高斯成分分析（SING）在神经影像数据整合中的应用
Simultaneous Non-Gaussian Component Analysis (SING) for Data Integration in Neuroimaging
摘要：
随着技术的进步，可以获得补充信息，科学研究越来越普遍地收集多个数据集。大规模神经影像学研究通常包括多种模式（例如，任务功能MRI、静息状态fMRI、弥散MRI和/或结构MRI），目的是了解数据集之间的关系。在这项研究中，我们试图了解工作记忆任务中激活的大脑区域是否与休息状态相关性有关。在神经成像中，一种流行的方法在具有联合独立成分分析的典型相关分析之前使用主成分分析进行降维，但这可能会丢弃具有低方差的生物特征和/或虚假地将数据集特有的结构与联合结构相关联。我们引入同步非高斯成分分析（SING），其中降维和特征提取同时实现，并通过主题得分捕获共享信息。我们将我们的方法应用于人类连接组项目的工作记忆任务和静息状态相关性。我们发现联合结构，从联合得分，其负载突出休息状态的相关性，涉及与工作记忆相关的区域。此外，一些科目的分数与流体智力有关。
英文摘要：
As advances in technology allow the acquisition of complementary information, it is increasingly common for scientific studies to collect multiple datasets. Large-scale neuroimaging studies often include multiple modalities (e.g., task functional MRI, resting-state fMRI, diffusion MRI, and/or structural MRI), with the aim to understand the relationships between datasets. In this study, we seek to understand whether regions of the brain activated in a working memory task relate to resting-state correlations. In neuroimaging, a popular approach uses principal component analysis for dimension reduction prior to canonical correlation analysis with joint independent component analysis, but this may discard biological features with low variance and/or spuriously associate structure unique to a dataset with joint structure. We introduce Simultaneous Non-Gaussian component analysis (SING) in which dimension reduction and feature extraction are achieved simultaneously, and shared information is captured via subject scores. We apply our method to a working memory task and resting-state correlations from the Human Connectome Project. We find joint structure as evident from joint scores whose loadings highlight resting-state correlations involving regions associated with working memory. Moreover, some of the subject scores are related to fluid intelligence.
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