The Neural Basis of Time-Varying Resting-State Functional Connectivity

Abstract

Dynamic network analysis based on resting-state magnetic resonance imaging (rsMRI) is a fairly new and potentially powerful tool for neuroscience and clinical research. Dynamic analysis can be sensitive to changes that occur in psychiatric or neurologic disorders and can detect variations related to performance on individual trials in healthy subjects. However, the appearance of time-varying connectivity can also arise in signals that share no temporal information, complicating the interpretation of dynamic functional connectivity studies. Researchers have begun utilizing simultaneous imaging and electrophysiological recording to elucidate the neural basis of the networks and their variability in animals and in humans. In this article, we review findings that link changes in electrically recorded brain states to changes in the networks obtained with rsMRI and discuss some of the challenges inherent in interpretation of these studies. The literature suggests that multiple brain processes may contribute to the dynamics observed, and we speculate that it may be possible to separate particular aspects of the rsMRI signal to enhance sensitivity to certain types of neural activity, providing new tools for basic neuroscience and clinical research.

Dynamic Connectivity Patterns in Conscious and Unconscious Brain

“…Separate dRSFC patterns were characteristic to different states of consciousness Similar sRSFC patterns across different consciousness states can hardly explain the dramatic behavioral change from wakefulness to AIU (Keilholz, 2014). This is likely because AIU causes dynamic changes in brain function, whereas sRSFC data analysis can only provide averaged RSFC over the data acquisition period, losing all dynamic information.…”

Unraveling the Miswired Connectome: A Developmental Perspective

“…In macaques, Leopold and colleagues have shown robust relationships between fluctuations in the amplitude of intracranially recorded neuronal fluctuations in both high (e.g., gamma) and low frequency bands and the intrinsic fMRI signal fluctuations that underlie the functional connectome (e.g., Shmuel and Leopold, 2008). In rodents, an elegant line of research (e.g., Keilholz, 2014) has investigated the relationship between both static and dynamic measures of functional connectivity and concurrently recorded electrophysiological measures of neuronal activity. These efforts demonstrate how simultaneous recording techniques can permit the differentiation between aspects of the functional connectome that reflect changes in infraslow neuronal activity and those that reflect changes in higher frequency (e.g., gamma) activity.…”

Dynamic resting state functional connectivity in awake and anesthetized rodents

“…In addition, with clustering analysis human brain networks displayed dynamic but quasistable connectivity patterns that diverged substantially from the averaged connectivity pattern (Allen et al, 2014). Importantly, simultaneous electrophysiological and fMRI recordings indicated that time-varying RSFC has neurophysiological origin (Chang et al, 2013; Keilholz, 2014; Magri et al, 2012; Pan et al, 2013; Tagliazucchi et al, 2012b; Thompson et al, 2013; Thompson et al, 2014). …”