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

• 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
• Journal Title: NEUROIMAGE
• Volume: Volume 251
• Publisher: ACADEMIC PRESS INC ELSEVIER SCIENCE | 2022-05-01, Pages 119013-119013
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1016/j.neuroimage.2022.119013
• Subjects: Health Sciences, Mental Health
• Keywords: Science & Technology; Life Sciences & Biomedicine; Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging; Neurosciences & Neurology; Functional connectivity (FC); Event present time points (EPTs); Event absent time points (EATs); Activation spatial map (ASM); Independent component analysis (ICA); Intrinsic connectivity networks (ICNs); Co-activation patterns (CAPs); RESTING-STATE NETWORKS; FUNCTIONAL CONNECTIVITY; SCHIZOPHRENIA; PATTERNS; CORTEX; DYNAMICS; MARKERS

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.