Inter-regional BOLD signal variability is an organizational feature of functional brain networks

Neuronal variability patterns promote the formation and organization of neural circuits. Macroscale similarities in regional variability patterns may therefore be linked to the strength and topography of inter-regional functional connections. To assess this relationship, we used multi-echo resting-s...

Full description

Bibliographic Details
Main Authors: Giulia Baracchini, Bratislav Mišić, Roni Setton, Laetitia Mwilambwe-Tshilobo, Manesh Girn, Jason S. Nomi, Lucina Q. Uddin, Gary R. Turner, R. Nathan Spreng
Format: Article
Language:English
Published: Elsevier 2021-08-01
Series:NeuroImage
Online Access:http://www.sciencedirect.com/science/article/pii/S1053811921004262
Description
Summary:Neuronal variability patterns promote the formation and organization of neural circuits. Macroscale similarities in regional variability patterns may therefore be linked to the strength and topography of inter-regional functional connections. To assess this relationship, we used multi-echo resting-state fMRI and investigated macroscale connectivity-variability associations in 154 adult humans (86 women; mean age = 22yrs). We computed inter-regional measures of moment-to-moment BOLD signal variability and related them to inter-regional functional connectivity. Region pairs that showed stronger functional connectivity also showed similar BOLD signal variability patterns, independent of inter-regional distance and structural similarity. Connectivity-variability associations were predominant within all networks and followed a hierarchical spatial organization that separated sensory, motor and attention systems from limbic, default and frontoparietal control association networks. Results were replicated in a second held-out fMRI run. These findings suggest that macroscale BOLD signal variability is an organizational feature of large-scale functional networks, and shared inter-regional BOLD signal variability may underlie macroscale brain network dynamics.
ISSN:1095-9572