Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA.
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
Neuroimage. 2022 Dec 1;264:119712. doi: 10.1016/j.neuroimage.2022.119712. Epub 2022 Oct 26.
With the increasing availability of neuroimaging data from multiple modalities-each providing a different lens through which to study brain structure or function-new techniques for comparing, integrating, and interpreting information within and across modalities have emerged. Recent developments include hypothesis tests of associations between neuroimaging modalities, which can be used to determine the statistical significance of intermodal associations either throughout the entire brain or within anatomical subregions or functional networks. While these methods provide a crucial foundation for inference on intermodal relationships, they cannot be used to answer questions about where in the brain these associations are most pronounced. In this paper, we introduce a new method, called CLEAN-R, that can be used both to test intermodal correspondence throughout the brain and also to localize this correspondence. Our method involves first adjusting for the underlying spatial autocorrelation structure within each modality before aggregating information within small clusters to construct a map of enhanced test statistics. Using structural and functional magnetic resonance imaging data from a subsample of children and adolescents from the Philadelphia Neurodevelopmental Cohort, we conduct simulations and data analyses where we illustrate the high statistical power and nominal type I error levels of our method. By constructing an interpretable map of group-level correspondence using spatially-enhanced test statistics, our method offers insights beyond those provided by earlier methods.
随着来自多种模态的神经影像学数据的日益普及——每种模态都提供了一个不同的视角来研究大脑结构或功能——新的技术已经出现，用于比较、整合和解释模态内和模态间的信息。最近的发展包括神经影像学模态之间关联的假设检验，这些检验可用于确定跨脑或解剖子区域或功能网络内模态间关联的统计显著性。虽然这些方法为模态间关系的推断提供了重要基础，但它们不能用于回答这些关联在大脑中的哪些部位最为明显的问题。在本文中，我们介绍了一种新的方法，称为 CLEAN-R，它既可以用于测试整个大脑中的模态对应关系，也可以用于定位这种对应关系。我们的方法首先涉及在每个模态中调整潜在的空间自相关结构，然后在小簇内聚合信息以构建增强测试统计量的地图。使用来自费城神经发育队列的儿童和青少年子样本的结构和功能磁共振成像数据，我们进行了模拟和数据分析，展示了我们方法的高统计功效和名义第一类错误水平。通过使用空间增强的测试统计量构建群组水平对应关系的可解释地图，我们的方法提供了比早期方法更深入的见解。
