Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans

By Waqas Majeed, Matthew Evan Magnuson, Wendy Hasenkamp, Hillary D. Schwarb, Eric H. Schumacher, Lawrence W. Barsalou, Shella Dawn Keilholz. NeuroImage, 2010

294 citations recorded by the source corpus.

Most studies involving spontaneous fluctuations in the BOLD signal extract connectivity patterns that show relationships between brain areas that are maintained over the length of the scanning session. In this study, however, we examine the spatiotemporal dynamics of the BOLD fluctuations to identify common patterns of propagation within a scan. A novel pattern finding algorithm was developed for detecting repeated spatiotemporal patterns in BOLD fMRI data. The algorithm was applied to high temporal resolution T2*-weighted multislice images obtained from rats and humans in the absence of any task or stimulation. In rats, the primary pattern consisted of waves of high signal intensity, propagating in a lateral to medial direction across the cortex, replicating our previous findings (Majeed et al., 2009a). These waves were observed primarily in sensorimotor cortex, but also extended to visual and parietal association areas. A secondary pattern, confined to subcortical regions consisted of an initial increase and subsequent decrease in signal intensity in the caudate-putamen. In humans, the most common spatiotemporal pattern consisted of an alteration between activation of areas comprising the “default-mode” (e.g., posterior cingulate and anterior medial prefrontal cortices) and the “task-positive” (e.g., superior parietal and premotor cortices) networks. Signal propagation from focal starting points was also observed. The pattern finding algorithm was shown to be reasonably insensitive to the variation in user-defined parameters, and the results were consistent within and between subjects. This novel approach for probing the spontaneous network activity of the brain has implications for the interpretation of conventional functional connectivity studies, and may increase the amount of information that can be obtained from neuroimaging data.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a287b164d794e31945158bc3f56fe1feba325b9d

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Citations in the corpus

Functional Magnetic Resonance Imaging Phase Synchronization as a Measure of Dynamic Functional Connectivity (2012, unknown): “…have been explored with computational models (Cabral et al., 2011; Honey et al., 2007) and with real data using sliding time window correlation (Majeed et al., 2011), sliding time window ICA (Kiviniemi et al., 2011), wavelet coherence (Chang and Glover, 2010), partial least squares (Grigg and…”
Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest (2013, result): “Several recent studies have since confirmed the non-stationarity of FC (Handwerker et al., 2012; Hutchison et al., in press; Kang et al., 2011; Kiviniemi et al., 2011; Li et al., in press;Majeed et al., 2011).”
Time-varying functional network information extracted from brief instances of spontaneous brain activity (2013, methodology): “Recent approaches toward this goal have relied mainly on examining temporal variability in the fMRI signal correlations (or coherences) using the time–frequency analysis or sliding window method (22, 24, 25, 27).”
Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? (2016, background): “Such a viewpoint is taken, for example, in Majeed et al. (2011) and Liu and Duyn (2013) (see Hutchison et al., 2013a for a review).”
On spurious and real fluctuations of dynamic functional connectivity during rest (2015, unknown): “, 2012), but also at rest (Allen et al., 2014; Chang and Glover, 2010; Hutchison et al., 2013b; Kang et al., 2011; Leonardi et al., 2013; Majeed et al., 2011; Smith et al., 2012).”
Dynamic functional connectivity of the default mode network tracks daydreaming (2014, background): “Such dynamic FC fluctuations occur in anesthetized animals (Hutchison et al., 2013b; Majeed et al., 2011) and therefore cannot be explained purely by mind-wandering.”
The dynamic functional connectome: State-of-the-art and perspectives (2017, unknown): “In details, Majeed et al. (2011) developed an innovative approach in which the recorded fMRI data is probed to extract a temporal sequence of volumes, referred to as the template, which recurs over the RS acquisition.”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, methodology): “, 2010), a property that is not exclusive to humans (Hutchison et al., in press; Keilholz et al., 2013; Majeed et al., 2011).”
Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states (2016, methodology): “QPeriodicSN was formed based on the identification of quasi periodic patterns of FC networks in rats and humans (Majeed et al., 2011).”