Cross-paradigm connectivity: reliability, stability, and utility

• Type of Material: Article
• Authors: Stephan Hamann, Emory University; Elaine Walker, Emory University; Hengyi Cao, Yale University; Oliver Y Chen, University of Oxford; Sarah C McEwen, University of California Los Angeles; Jennifer K Forsyth, University of California Los Angeles; Dylan G Gee, Yale University; Carrie E Bearden, University of California Los Angeles; Jean Addington, Hotchkiss Brain Institute; Bradley Goodyear, University of Calgary; Kristin S Cadenhead, University of California San Diego; Heline Mirzakhanian, University of California San Diego; Barbara A Cornblatt, Zucker Hillside Hospital; Ricardo E Carrión, Zucker Hillside Hospital; Daniel H Mathalon, University of California, San Francisco; Thomas H McGlashan, Yale University; Diana O Perkins, The University of North Carolina at Chapel Hill; Aysenil Belger, The University of North Carolina at Chapel Hill; Heidi Thermenos, Harvard Medical School; Ming T Tsuang, University of California San Diego; Theo GM van Erp, University of California, Irvine; Alan Anticevic, Yale University; Scott W Woods, Yale University; Tyrone D Cannon, Yale University
• Language: English
• Date: 2021-04-01
• Publisher: Springer Nature
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8378905/bin/NIHMS1722913-supplement-supplemental_materials.pdf
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1007/s11682-020-00272-z
• Title of Journal or Parent Work: Brain Imaging and Behavior
• Volume: 15
• Issue: 2
• Start Page: 614
• End Page: 629
• Grant/Funding Information: This work was supported by the Brain and Behavior Research Foundation NARSAD Young Investigator Grant (No. 27068) to Dr. Cao, by gifts from the Staglin Music Festival for Mental Health and International Mental Health Research Organization to Dr. Cannon, and by National Institute of Health (NIH) grants U01 MH081902 to Dr. Cannon, P50 MH066286 and the Miller Family Endowed Term Chair to Dr. Bearden, U01 MH081857 to Dr. Cornblatt, U01 MH82022 to Dr. Woods, U01 MH066134 to Dr. Addington, U01 MH081944 to Dr. Cadenhead, R01 U01 MH066069 to Dr. Perkins, R01 MH076989 to Dr. Mathalon, and U01 MH081988 to Dr. Walker.
• Keywords: Humans; Connectome; Rest; Nerve Net; Brain; Reliability; Stability; Reproducibility of Results; Magnetic Resonance Imaging; Individual identifiability; Functional connectome; Cross-paradigm connectivity
• Research Categories: Health Sciences, Mental Health

Abstract

While functional neuroimaging studies typically focus on a particular paradigm to investigate network connectivity, the human brain appears to possess an intrinsic “trait” architecture that is independent of any given paradigm. We have previously proposed the use of “cross-paradigm connectivity (CPC)” to quantify shared connectivity patterns across multiple paradigms and have demonstrated the utility of such measures in clinical studies. Here, using generalizability theory and connectome fingerprinting, we examined the reliability, stability, and individual identifiability of CPC in a group of highly-sampled healthy traveling subjects who received fMRI scans with a battery of five paradigms across multiple sites and days. Compared with single-paradigm connectivity matrices, the CPC matrices showed higher reliability in connectivity diversity, lower reliability in connectivity strength, higher stability, and higher individual identification accuracy. All of these assessments increased as a function of number of paradigms included in the CPC analysis. In comparisons involving different paradigm combinations and different brain atlases, we observed significantly higher reliability, stability, and identifiability for CPC matrices constructed from task-only data (versus those from both task and rest data), and higher identifiability but lower stability for CPC matrices constructed from the Power atlas (versus those from the AAL atlas). Moreover, we showed that multi-paradigm CPC matrices likely reflect the brain’s “trait” structure that cannot be fully achieved from single-paradigm data, even with multiple runs. The present results provide evidence for the feasibility and utility of CPC in the study of functional “trait” networks and offer some methodological implications for future CPC studies.