Quasi-Periodic Patterns of Intrinsic Brain Activity in Individuals and their Relationship to Global Signal

Abstract

Quasiperiodic patterns (QPPs) as reported by Majeed et al., 2011 are prominent features of the brain’s intrinsic activity that involve important large-scale networks (default mode, DMN; task positive, TPN) and are likely to be major contributors to widely used measures of functional connectivity. We examined the variability of these patterns in 470 individuals from the Human Connectome Project resting state functional MRI dataset. The QPPs from individuals can be coarsely categorized into two types: one where strong anti-correlation between the DMN and TPN is present, and another where most areas are strongly correlated. QPP type could be predicted by an individual’s global signal, with lower global signal corresponding to QPPs with strong anti-correlation. After regression of global signal, all QPPs showed strong anti-correlation between DMN and TPN. QPP occurrence and type was similar between a subgroup of individuals with extremely low motion (or even high motion) and the rest of the sample, which shows that motion is not a major contributor to the QPPs. After regression of estimates of slow respiratory and cardiac induced signal fluctuations, more QPPs showed strong anti-correlation between DMN and TPN, an indication that while physiological noise influences the QPP type, it is not the primary source of the QPP itself. QPPs were more similar for the same subjects scanned on different days than for different subjects. These results provide the first assessment of the variability in individual QPPs and their relationship to physiological parameters.

Dynamic resting state fMRI analysis in mice reveals a set of Quasi-Periodic Patterns and illustrates their relationship with the global signal

“…We further speculate that the lateral cortical network might represent a mouse TPN-like network. These networks have been conjectured to be present in mice (Liska et al, 2015), and match a similar DMN-TPN anti-correlation (Fox et al, 2005) and quasi-periodicity in humans (Majeed et al, 2011; Yousefi et al, 2017). Although the exact subcortical patterns shown in the current study were not reported in rats, Majeed et al (2011) did indicate the presence of a pattern including Cpu.…”

“…However, while normal low temporal rsfMRI could be used to relate QPPs to large-scale brain networks, QPP propagation across the rostro-caudal axis could not be investigated. In humans, QPPs propagate across the entire brain, involving mainly DMN and TPN areas (Majeed et al, 2011; Yousefi et al, 2017). Similarly, different whole-brain CAPs appeared to display some form of temporal sequence, suggestive of QPP-like behavior (Chen et al, 2015; Liu and Duyn, 2013).…”

Aberrant static and dynamic functional patterns of frontoparietal control network in antipsychotic‐naïve first‐episode psychosis subjects

“…A spatiotemporal pattern‐finding, correlation‐based iterative algorithm, described by Yousefi et al () was used to search for repeating patterns in the functional scans. The pattern‐finding algorithm begins by conducting a sliding correlation between a random starting segment within a functional time series and the functional time series itself.…”

“…QPPs in humans are approximately 20 s long (Majeed et al, ); for this study, the window length, or template duration, was set to 15 time points (= 23.2 s). Scans were concatenated for each subject and the QPPs inspected at every time point (Yousefi et al, ). Hence, for the template resulting from each time point, values of its sliding correlation at local maxima that were above the threshold of 0.3 at the final iteration were summed and, the template with the highest sum was designated as the most representative QPP for its respective group.…”

“…On the other hand, quasiperiodic patterns (QPPs) consist of infraslow (<0.1 Hz) spatiotemporal patterns embedded in the BOLD signal that repeats over time, exhibiting alternation of high and low activity. QPPs are reproducible patterns of spatial changes (Majeed et al, ), distinct from physiological noise and global signal (Yousefi, Shin, Schumacher, & Keilholz, ), and ubiquitous across species (mice, Belloy et al, ; rats, Majeed et al, ; as well as in resting‐state and task‐performing humans, Abbas, Belloy, et al, ; Majeed et al, ). Because infraslow activity is one of the best candidates for explaining the coordination between large brain networks (Thompson, Pan, Magnuson, Jaeger, & Keilholz, ), QPPs could offer a window into the relationship between functional networks.…”