Dynamic Properties of Functional Connectivity in the Rodent

Abstract

Functional connectivity mapping with resting-state magnetic resonance imaging (MRI) has become an immensely powerful technique that provides insight into both normal cognitive function and disruptions linked to neurological disorders. Traditionally, connectivity is mapped using data from an entire scan (minutes), but it is well known that cognitive processes occur on much shorter time scales (seconds). Recent studies have demonstrated that the correlation between the blood oxygenation level-dependent (BOLD) MRI signal from different areas varies over time, motivating a further exploration of these fluctuations in apparent connectivity. However, it has also been shown that similar changes in correlation can arise when the timing relationships between voxels are randomized (Handwerker et al., 2012). In this work, we show that functional connectivity in the anesthetized rat exhibits dynamic properties that are similar to those previously observed in awake humans (Chang and Glover, 2010) and anesthetized monkeys (Hutchison et al., 2012). Sliding window correlation between BOLD time courses obtained from bilateral cortical and subcortical regions of interest results in periods of variable positive and negative correlation for most pairs of areas except homologous areas in opposite hemispheres, which exhibit a primarily positive correlation. A comparison with sliding window correlation of randomly matched time courses suggests that with the exception of homologous areas and sensorimotor connections, the dynamics cannot be distinguished from random fluctuations in correlation over time, supporting the idea that some of these dynamic patterns may be due to inherent properties of the signal rather than variations in neural coherence. Within the pairs of areas where the dynamics are most different from those of randomly matched time courses, ten common patterns of connectivity are identified, and their occurrence as a function of time is plotted for all animals. The observation of time-varying correlation in the rodent model will facilitate the future multimodal experiments needed to determine whether the changes in apparent connectivity are linked to underlying neural variability.

Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns

“…This dynamic behavior has been observed in awake and sleeping humans (5)(6)(7)(8), as well as in anesthetized animals (9,10). Several studies involving simultaneous fMRI and electrophysiological recordings have suggested that FC dynamics may be driven by neurophysiological sources rather than noise (6,11,12).…”

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

“…Since improved imaging and analysis methods reduced contributions from physiological noise and increased sensitivity, interest began to grow in examining variability in the networks over time, creating a need for new twists on the classic analysis techniques (Hutchison et al, 2013a). Most of these methods involve using sliding windows or temporal segmentation to create network maps that vary over time (Allen et al, 2014;Chang and Glover, 2010;Hutchison et al, 2013b;Keilholz et al, 2013;Kiviniemi et al, 2011). Others identify spatiotemporal patterns of activity that repeat over time (Handwerker et al, 2012;Liu and Duyn, 2013;Majeed et al, 2011) or activity related to single events Petridou et al, 2013).…”

“…These dynamic analysis methods are sensitive to changes that occur in psychiatric or neurologic disorders (Leonardi et al, 2013;Li et al, 2014b;Sakoglu et al, 2010) and are also related to variations in performance on individual trials in healthy subjects (Thompson et al, 2012;Yang et al, 2014). 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 (Handwerker et al, 2012;Keilholz et al, 2013). A few labs have begun utilizing simultaneous imaging and electrophysiological recording to elucidate the neural basis of the networks and their variability in animals (Magri et al, 2012;Pan et al, 2011Pan et al, , 2013Scholvinck et al, 2010;Shmuel and Leopold, 2008;Thompson et al, 2013aThompson et al, , 2013b and in humans (Chang et al, 2013;Tagliazucchi et al, 2012;Wu et al, 2010).…”

Different dynamic resting state fMRI patterns are linked to different frequencies of neural activity

“…While numerous methods of quantifying dynamic rsfMRI patterns exist (Chang and Glover 2010;Grigg and Grady 2010;Hutchison et al 2013;Keilholz et al 2013;Kiviniemi et al 2011;Liu and Duyn 2013;Magri et al 2012;Majeed et al 2009;Petridou et al 2013), QPP and SWC were chosen for this study as they are at opposite ends of the spectrum of methods, with SWC (as generated here) providing spatially-localized and temporally-windowed information, whereas QPP identifies spatially-extended and quasi-periodic to periodic processes.…”

“…As the resting state has (by definition) no stimulus for comparison, it is not trivial to extract meaningful dynamic information from it. Techniques proposed have included finding characteristic spatiotemporal dynamics (Majeed et al 2009), wavelet analysis (Chang and Glover 2010), sliding-window correlation (Hutchison et al 2013;Keilholz et al 2013), independent component analysis (Kiviniemi et al 2011), and averaging the rsfMRI signal near known events Magri et al 2012;Petridou et al 2013). While much work remains, evidence is emerging that such dynamics do indeed reflect the underlying neural activity (Keilholz 2014;Thompson et al 2013bThompson et al , 2013c.…”