Spatially-enhanced clusterwise inference for testing and localizing intermodal correspondence

• Published in: NeuroImage (Orlando, Fla.) Vol. 264; p. 119712
• Main Authors: Weinstein, Sarah M., Vandekar, Simon N., Baller, Erica B., Tu, Danni, Adebimpe, Azeez, Tapera, Tinashe M., Gur, Ruben C., Gur, Raquel E., Detre, John A., Raznahan, Armin, Alexander-Bloch, Aaron F., Satterthwaite, Theodore D., Shinohara, Russell T., Park, Jun Young
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.12.2022; Elsevier Limited; Elsevier
• Subjects: Adolescent; Brain; Brain - diagnostic imaging; Brain mapping; Brain Mapping - methods; Child; Clusterwise inference; Correspondence; Data integration; Functional anatomy; Functional magnetic resonance imaging; Humans; Hypotheses; Hypothesis testing; Intermodal correspondence; Localization; Magnetic Resonance Imaging - methods; Medical imaging; Methods; Neuroimaging; Neuroimaging - methods; Permutation; Spatial autocorrelation; Statistical power; Statistics; Structure-function relationships; Intermodal correspondence; Spatial autocorrelation; Permutation; Clusterwise inference; Data integration
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2022.119712

•We propose a new method CLEAN-R to test and localize intermodal correspondence.•Modeling modality-specific spatial autocorrelations dramatically improves power.•We further improve reproducibility by using clusterwise inference.•Type I error is controlled using null maps generated by permuting subjects.•A R package supports the fast implementation of CLEAN-R. 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.