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

Abstract

Time-resolved 'dynamic' over whole-period 'static' analysis of low frequency (LF) blood-oxygen level dependent (BOLD) fluctuations provides many additional insights into the macroscale organization and dynamics of neural activity. Although there has been considerable advancement in the development of mouse resting state fMRI (rsfMRI), very little remains known about its dynamic repertoire. Here, we report for the first time the detection of a set of recurring spatiotemporal Quasi-Periodic Patterns (QPPs) in mice, which show spatial similarity with known resting state networks. Furthermore, we establish a close relationship between several of these patterns and the global signal. We acquired high temporal rsfMRI scans under conditions of low (LA) and high (HA) medetomidine-isoflurane anesthesia. We then employed the algorithm developed by Majeed et al. (2011), previously applied in rats and humans, which detects and averages recurring spatiotemporal patterns in the LF BOLD signal. One type of observed patterns in mice was highly similar to those originally observed in rats, displaying propagation from lateral to medial cortical regions, which suggestively pertain to a mouse Task-Positive like network (TPN) and Default Mode like network (DMN). Other QPPs showed more widespread or striatal involvement and were no longer detected after global signal regression (GSR). This was further supported by diminished detection of subcortical dynamics after GSR, with cortical dynamics predominating. Observed QPPs were both qualitatively and quantitatively determined to be consistent across both anesthesia conditions, with GSR producing the same outcome. Under LA, QPPs were consistently detected at both group and single subject level. Under HA, consistency and pattern occurrence rate decreased, whilst cortical contribution to the patterns diminished. These findings confirm the robustness of QPPs across species and demonstrate a new approach to study mouse LF BOLD spatiotemporal dynamics and mechanisms underlying functional connectivity. The observed impact of GSR on QPPs might help better comprehend its controversial role in conventional resting state studies. Finally, consistent detection of QPPs at single subject level under LA promises a step forward towards more reliable mouse rsfMRI and further confirms the importance of selecting an optimal anesthesia regime.

Quasi-periodic patterns contribute to functional connectivity in the brain

“…Our study reproduces previous reports of the presence of reliably recurring quasi-periodic patterns in the brain (Belloy et al 2018;Majeed et al 2011;Thompson et al 2014;Yousefi et al 2018). This is the first time QPPs have been examined in whole-brain data from task-performing individuals.…”

“…Task performance drastically alters the functional architecture of the brain, shifting focus towards task-positive regions (Elton et al 2015;Goparaju et al 2014; Thompson et al 2013). So far, a detailed analysis of QPPs has only been conducted in anesthetized animals and resting-state humans (Belloy et al 2018;Majeed et al 2009;Majeed et al 2011;Thompson et al 2014b;Yousefi et al 2018). Task performance tends to increase anti-correlation between the DMN and TPN ), suggesting that QPP strength or frequency may be increased, altering measured functional connectivity as a result.…”

Network structure of the mouse brain connectome with voxel resolution

“…We however noted that the anatomical organization of the unimodal-polymodal gradient was anatomically consistent with dominant recurring patterns of fMRI co-activation (CAPs) recently described to govern spontaneous fMRI network dynamics in this species 26,43 . These take the form of infraslow oscillatory transitions in latero-cortical and midline poly-modal regions reminiscent of the topographical organization of gradient A.…”

Brain states govern the spatio-temporal dynamics of resting state functional connectivity

“…Several studies have demonstrated that the functional state of the brain is dynamic over time, and this is reflected in functional connectivity changes 72,73 . These dynamic states are quantifiable, depend on the ongoing infraslow activity and seem to show atypical dynamics in some neurological disorders as autism 74 .…”