A simple permutation‐based test of intermodal correspondence

• Published in: Human brain mapping Vol. 42; no. 16; pp. 5175 - 5187
• Main Authors: Weinstein, Sarah M., Vandekar, Simon N., Adebimpe, Azeez, Tapera, Tinashe M., Robert‐Fitzgerald, Timothy, Gur, Ruben C., Gur, Raquel E., Raznahan, Armin, Satterthwaite, Theodore D., Alexander‐Bloch, Aaron F., Shinohara, Russell T.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.11.2021
• Subjects: Brain; Brain mapping; Brain Mapping - methods; Brain Mapping - standards; Cerebral Cortex - anatomy & histology; Cerebral Cortex - diagnostic imaging; Cerebral Cortex - physiology; Correspondence; covariance stationarity; Humans; Hypotheses; hypothesis; Image Processing, Computer-Assisted - methods; Image Processing, Computer-Assisted - standards; Intermodal; intermodal correspondence; Medical imaging; Memory tasks; Mental task performance; Methods; Models, Statistical; Nerve Net - anatomy & histology; Nerve Net - diagnostic imaging; Nerve Net - physiology; Neuroimaging; Neuroimaging - methods; Neuroimaging - standards; permutation testing; Permutations; Random variables; Short term memory; Similarity; Statistical analysis; Statistical inference; Statistical methods; Statistics; testing; Thickness; permutation testing; covariance stationarity; intermodal correspondence; hypothesis; testing
• DOI: 10.1002/hbm.25577
• ISSN: 1065-9471; 1097-0193

Many key findings in neuroimaging studies involve similarities between brain maps, but statistical methods used to measure these findings have varied. Current state‐of‐the‐art methods involve comparing observed group‐level brain maps (after averaging intensities at each image location across multiple subjects) against spatial null models of these group‐level maps. However, these methods typically make strong and potentially unrealistic statistical assumptions, such as covariance stationarity. To address these issues, in this article we propose using subject‐level data and a classical permutation testing framework to test and assess similarities between brain maps. Our method is comparable to traditional permutation tests in that it involves randomly permuting subjects to generate a null distribution of intermodal correspondence statistics, which we compare to an observed statistic to estimate a p‐value. We apply and compare our method in simulated and real neuroimaging data from the Philadelphia Neurodevelopmental Cohort. We show that our method performs well for detecting relationships between modalities known to be strongly related (cortical thickness and sulcal depth), and it is conservative when an association would not be expected (cortical thickness and activation on the n‐back working memory task). Notably, our method is the most flexible and reliable for localizing intermodal relationships within subregions of the brain and allows for generalizable statistical inference. We propose using a classical permutation testing framework to study intermodal correspondence using subject‐level data while requiring minimal statistical assumptions. We compare our method to previous approaches involving spatial null modeling of group‐level brain maps and illustrate and discuss the flexibility of our method for localizing intermodal relationships within subregions of the brain.