Spatial and Spectral Components of the BOLD Global Signal in Rat Resting‐State Functional MRI

Abstract

PurposeIn resting‐state fMRI (rs‐fMRI), the global signal average captures widespread fluctuations related to unwanted sources of variance such as motion and respiration, as well as widespread neural activity; however, relative contributions of neural and non‐neural sources to the global signal remain poorly understood. This study sought to tackle this problem through the comparison of the BOLD global signal to an adjacent non‐brain tissue signal, where neural activity was absent, from the same rs‐fMRI scan obtained from anesthetized rats. In this dataset, motion was minimal and ventilation was phase‐locked to image acquisition to minimize respiratory fluctuations. Data were acquired using three different anesthetics: isoflurane, dexmedetomidine, and a combination of dexmedetomidine and light isoflurane.MethodsA power spectral density estimate, a voxel‐wise spatial correlation via Pearson's correlation, and a co‐activation pattern analysis were performed using the global signal and the non‐brain tissue signal. Functional connectivity was calculated using Pearson's linear correlation on default mode network (DMN) regions.ResultsWe report differences in the spectral composition of the two signals and show spatial selectivity within DMN structures that show an increased correlation to the global signal and decreased intra‐network connectivity after global signal regression. All of the observed differences between the global signal and the non‐brain tissue signal were maintained across anesthetics.ConclusionThese results show that the global signal is distinct from the noise contained in the tissue signal, as support for a neural contribution. This study provides a unique perspective to the contents of the global signal and their origins.

Variation in the Distribution of Large-scale Spatiotemporal Patterns of Activity Across Brain States

“…Prior work in rodents applied global signal regression and therefore only examined QPP2. As expected, QPP1 strongly resembles the pattern of global signal obtained from the same rats 14 , consistent with the observation that QPP1 captured the global signal in humans 8 . QPP2 and QPP3 both capture aspects of lateral-medial propagation from QPPs previously observed in rats after global signal regression 10,11 .…”

“…We first obtained rs-fMRI data from a small group of animals during free breathing or mechanical ventilation phase-locked to MRI acquisition to evaluate respiratory noise and motion, which can both contribute to large scale patterns of activity such as the global signal 16 . We then examined rs-fMRI data from mechanically-ventilated rats under three anesthetic conditions (reported previously in 14 ) to examine the spatiotemporal organization of intrinsic brain activity under conditions of minimal noise.…”

“…All rs-fMRI scans for the mechanically-ventilated rats were phase-locked, meaning that the time between subsequent images was set to a multiple of the animal’s respiratory rate so that each image was acquired during the same phase of the respiratory cycle 14 ( Fig. 1 ).…”

“…The animals were then scanned for a final time for 49 minutes on average. A table with the exact timing of anesthetic introduction and image acquisition for each rat is included in the supporting information of 14 .…”

“…These patterns, sometimes called quasiperiodic patterns or QPPs [10][11][12][13] , capture cyclical activation and deactivation of brain areas and propagation of activity as areas transition between activated and deactivated phases. In humans, the first QPP describes the spatiotemporal evolution of the global signal, which contains contributions from both neural activity and widespread noise [14][15][16][17][18] . QPP2 captures the alternating activation and deactivation of the default mode network (DMN) and task positive network (TPN), which has been implicated in variability in task performance [19][20][21] .…”