Moving beyond the 'CAP' of the Iceberg: Intrinsic connectivity networks in fMRI are continuously engaging and overlapping

• Type of Material: Article
• Authors: A Iraji, Georgia State University; A Faghiri, Georgia State University; Z Fu, Georgia State University; P Kochunov, University of Maryland, Baltimore; BM Adhikari, University of Maryland, Baltimore; A Belger, University of North Carolina; JM Ford, University of California San Francisco; S McEwen, University of California Los Angeles; DH Mathalon, University of California San Francisco; GD Pearlson, Yale University; SG Potkin, University of California Irvine; A Preda, University of California Irvine; JA Turner, Georgia State University; TGM Van Erp, University of California Irvine; C Chang, Vanderbilt University; Vince Calhoun, Emory University
• Language: English
• Date: 2022-05-01
• Publisher: ACADEMIC PRESS INC ELSEVIER SCIENCE
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: Published by Elsevier Inc.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.neuroimage.2022.119013
• Title of Journal or Parent Work: NEUROIMAGE
• Volume: 251
• Start Page: 119013
• End Page: 119013
• Grant/Funding Information: This work was supported by grants from the National Institutes of Health grant numbers 1U24RR021992, 1U24RR025736, R01EB020407, and R01MH118695, and National Science Foundation grant 2112455 to Dr. Vince D. Calhoun and by grants from the VA Merit I01CX000497 program and VA Senior Research Career Award to Dr. Judith M Ford.
• Keywords: RESTING-STATE NETWORKS; CORTEX; Co-activation patterns (CAPs); Life Sciences & Biomedicine; Event absent time points (EATs); Activation spatial map (ASM); Radiology, Nuclear Medicine & Medical Imaging; Event present time points (EPTs); Neurosciences & Neurology; DYNAMICS; Functional connectivity (FC); Independent component analysis (ICA); MARKERS; FUNCTIONAL CONNECTIVITY; Science & Technology; Neuroimaging; SCHIZOPHRENIA; Intrinsic connectivity networks (ICNs); Neurosciences; PATTERNS

Abstract

Resting-state functional magnetic resonance imaging is currently the mainstay of functional neuroimaging and has allowed researchers to identify intrinsic connectivity networks (aka functional networks) at different spatial scales. However, little is known about the temporal profiles of these networks and whether it is best to model them as continuous phenomena in both space and time or, rather, as a set of temporally discrete events. Both categories have been supported by series of studies with promising findings. However, a critical question is whether focusing only on time points presumed to contain isolated neural events and disregarding the rest of the data is missing important information, potentially leading to misleading conclusions. In this work, we argue that brain networks identified within the spontaneous blood oxygenation level-dependent (BOLD) signal are not limited to temporally sparse burst moments and that these event present time points (EPTs) contain valuable but incomplete information about the underlying functional patterns. We focus on the default mode and show evidence that is consistent with its continuous presence in the BOLD signal, including during the event absent time points (EATs), i.e., time points that exhibit minimum activity and are the least likely to contain an event. Moreover, our findings suggest that EPTs may not contain all the available information about their corresponding networks. We observe distinct default mode connectivity patterns obtained from all time points (AllTPs), EPTs, and EATs. We show evidence of robust relationships with schizophrenia symptoms that are both common and unique to each of the sets of time points (AllTPs, EPTs, EATs), likely related to transient patterns of connectivity. Together, these findings indicate the importance of leveraging the full temporal data in functional studies, including those using event-detection approaches.