---
title: "Testing for Confounding in Multimodal Neuroimaging Coupling"
id: "biorxiv-14-a-test-for-confounding-in-coupling-of-multimodal-neuroimaging-data"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-14-a-test-for-confounding-in-coupling-of-multimodal-neuroimaging-data"
content_type: "clinical_feed_article"
specialty: "Neurology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.09.14.750774v1?rss=1"
published_at: "2026-09-21T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Testing for Confounding in Multimodal Neuroimaging Coupling
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-14-a-test-for-confounding-in-coupling-of-multimodal-neuroimaging-data
- **Specialty:** [Neurology](https://medichelpline.com/clinical-feed/neurology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.09.14.750774v1?rss=1)
- **Published At:** 2026-09-21T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- Multimodal neuroimaging studies compare spatial brain maps across modalities to quantify **intermodal coupling** and spatial correspondence. - The SPICE test (simple permutation-based inter-modal correspondence) assesses whether within-subject correspondence exceeds chance by permuting subject labels in one modality to build a null distribution. - Permutation-based inference requires that subjects be exchangeable under the null; this assumption can be violated when covariates (for example, age, sex, or disease status) systematically change the distribution of brain maps across subjects. - Violation of exchangeability can create misleading results similar to **Simpson's paradox**, where population-level correspondence is driven by between-group differences rather than true within-subject coupling. - The authors propose a formal **U-statistic**-based test to detect covariate effects that would confound coupling analyses; the test serves both as a diagnostic for assumption violations and as a tool for scientific discovery of covariate-driven effects. - Using synthetic and semi-synthetic neuroimaging data, the proposed method demonstrates controlled Type I error rates and substantial statistical power (specific numeric results were not reported in the source abstract). - The method was applied to examine potential confounding by **age** and **sex** using real imaging modality pairs from the Philadelphia Neurodevelopmental Cohort; specific outcomes of those analyses were not detailed in the source abstract. - The framework aims to increase rigor and interpretability of intermodal coupling analyses, particularly in heterogeneous populations and studies focused on development, aging, or disease. - The preprint authors declare no competing interests and acknowledge NIH funding and institutional support; this work is a preprint and has not undergone peer review.
## Clinical Analysis & Structured Key Points
A Test for Confounding in Coupling of Multimodal Neuroimaging Data | bioRxiv Skip to main content New Results A Test for Confounding in Coupling of Multimodal Neuroimaging Data Yiyan Hao , Simon Vandekar , Aaron Alexander-Bloch , Theodore Satterthwaite , Brian White , Russell Shinohara doi: https://doi.org/10.64898/2026.09.14.750774 Yiyan Hao 1 University of Pennsylvania; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Simon Vandekar 2 Vanderbilt University Medical Center; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aaron Alexander-Bloch 1 University of Pennsylvania; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Theodore Satterthwaite 1 University of Pennsylvania; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Brian White 3 Children's Hospital of Philadelphia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Russell Shinohara 1 University of Pennsylvania; Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: russell.shinohara{at}pennmedicine.upenn.edu Abstract Info/History Metrics Supplementary material Preview PDF Abstract Multimodal neuroimaging studies often involve comparisons between brain maps. Recently, statistical methods have been proposed to quantify and assess spatial correspondence between two modalities. The simple permutation-based inter-modal correspondence (SPICE) test evaluates whether within-subject correspondence exceeds chance, where the null distribution is constructed by permuting subject labels for one modality. Despite its easy implementation and minimal spatial assumptions, the critical assumption underlying permutation analysis, that subjects are exchangeable under the null, may be violated when covariates such as age, sex, or disease status systematically alter brain map distributions. This violation can produce results analogous to Simpson's paradox, where apparent population-level correspondence reflects between-group differences rather than genuine within-subject coupling. We propose a formal U-statistic-based test for such covariate effects, enabling both diagnostic evaluation of assumption violations and scientific discovery. Using synthetic and semi-synthetic neuroimaging data, we demonstrate well-controlled Type I error and high statistical power. We apply our method to test for confounding effects due to age and sex using real data from two pairs of imaging modalities in the Philadelphia Neurodevelopmental Cohort. Our framework increases the rigor and interpretability of intermodal coupling analyses, with broad implications for neuroimaging studies in heterogeneous populations, especially in developmental, aging, and disease-focused research. Competing Interest Statement The authors have declared no competing interest. Funder Information Declared National Institutes of Health , R01MH112847 , R01MH123550 The Research Institute of the Children's Hospital of Philadelphia Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Back to top Previous Next Posted September 21, 2026. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following A Test for Confounding in Coupling of Multimodal Neuroimaging Data Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share A Test for Confounding in Coupling of Multimodal Neuroimaging Data Yiyan Hao , Simon Vandekar , Aaron Alexander-Bloch , Theodore Satterthwaite , Brian White , Russell Shinohara bioRxiv 2026.09.14.750774; doi: https://doi.org/10.64898/2026.09.14.750774 Share This Article: Copy Citation Tools A Test for Confounding in Coupling of Multimodal Neuroimaging Data Yiyan Hao , Simon Vandekar , Aaron Alexander-Bloch , Theodore Satterthwaite , Brian White , Russell Shinohara bioRxiv 2026.09.14.750774; doi: https://doi.org/10.64898/2026.09.14.750774 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Areas All Articles Animal Behavior and Cognition (8015) Biochemistry (18744) Bioengineering (14890) Bioinformatics (44453) Biophysics (22610) Cancer Biology (19728) Cell Biology (26906) Clinical Trials (138) Developmental Biology (13970) Ecology (21008) Epidemiology (2067) Evolutionary Biology (25457) Genetics (16171) Genomics (23513) Immunology (18717) Microbiology (42520) Molecular Biology (18064) Neuroscience (93479) Paleontology (700) Pathology (2982) Pharmacology and Toxicology (5100) Physiology (8116) Plant Biology (16000) Scientific Communication and Education (2095) Synthetic Biology (4560) Systems Biology (10237) Zoology (2391)
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