Hubs of Anticorrelation in High-Resolution Resting-State Functional Connectivity Network Architecture

Abstract

A major focus of brain research recently has been to map the resting-state functional connectivity (rsFC) network architecture of the normal brain and pathology through functional magnetic resonance imaging. However, the phenomenon of anticorrelations in resting-state signals between different brain regions has not been adequately examined. The preponderance of studies on resting-state fMRI (rsFMRI) have either ignored anticorrelations in rsFC networks or adopted methods in data analysis, which have rendered anticorrelations in rsFC networks uninterpretable. The few studies that have examined anticorrelations in rsFC networks using conventional methods have found anticorrelations to be weak in strength and not very reproducible across subjects. Anticorrelations in rsFC network architecture could reflect mechanisms that subserve a number of important brain processes. In this preliminary study, we examined the properties of anticorrelated rsFC networks by systematically focusing on negative cross-correlation coefficients (CCs) among rsFMRI voxel time series across the brain with graph theory-based network analysis. A number of methods were implemented to enhance the neuronal specificity of resting-state functional connections that yield negative CCs, although at the cost of decreased sensitivity. Hubs of anticorrelation were seen in a number of cortical and subcortical brain regions. Examination of the anticorrelation maps of these hubs indicated that negative CCs in rsFC network architecture highlight a number of regulatory interactions between brain networks and regions, including reciprocal modulations, suppression, inhibition, and neurofeedback.

Exploring Anti-correlated Resting State BOLD Signals Through Dynamic Functional Connectivity and Whole-brain Computational Modeling

“…This observation is consistent with previous findings on the role of subcortical regions as a hub for anti-correlations (Gopinath et al 2015). The analyses also suggested higher anti-correlation power associated with lower EC output strength from sensory-motor regions and lower EC input strength to higher association regions.…”

Estimation of Global and Local Complexities of Brain Networks: A Random Walks Approach

“…Later studies identified positive correlations between regions that are now known to comprise the default mode network (DMN) (Buckner, Andrews-Hanna, & Schacter, 2008; Raichle et al, 2001). In addition to the reported correlated networks, anticorrelated networks have also been reported by several studies (Michael D. Fox, Zhang, Snyder, & Raichle, 2009; Gopinath, Krishnamurthy, Cabanban, & Crosson, 2015; Liang, King, & Zhang, 2012). Although anticorrelations have been attributed to the global signals removal recent studies suggest a physiological basis (Michael D. Fox et al, 2009; Kazeminejad & Sotero, 2019).…”

Maintenance and Representation of Mind Wandering during Resting-State fMRI

“…The functional segregation might reflect reciprocal modulation or inhibition/suppression through direct or indirect anatomical connections424344. It is to be noted that, while negative correlations have been associated with data preprocessing methods using global signal regression4546, a number of studies4247484950, including this current study, observed negative correlations even in the absence of global signal regression. Therefore, the negative connectivity observed in this study could not be an artifact introduced by a global signal regression procedure.…”