Global Signal Governs the Spatiotemporal Structure of Local Networks and Coordinates Cognition
Poster No:
1471
Submission Type:
Abstract Submission
Authors:
Jing Yuan1, Yuejia Luo1, Jianfeng Zhang2
Institutions:
1Beijing Normal University, Beijing, Beijing, 2Shenzhen University, Shenzhen, Guangdong
First Author:
Co-Author(s):
Introduction:
The functional role of the global signal (GS) in functional magnetic resonance imaging (fMRI) remains debated. Previous studies have shown that the peak of the GS correlates with the trough of neural arousal and reflects dynamic, transient co-activation patterns (CAPs)(Liu et al., 2018; Zhang, Huang, Tumati, & Northoff, 2020). However, the functional significance of the full-phase GS information remains underexplored. We hypothesize that the complete phase of the global signal provides a temporal framework that organizes local networks and coordinates internal and external cognitive functions. In this study, we use a clustering approach to examine how brain dynamics coupled with the global signal differ across various cognitive states.
Methods:
We analyzed resting-state fMRI (rs-fMRI) data from 693 participants in the Human Connectome Project (HCP) S1200 release, along with seven task fMRI conditions and physiological recordings (Glasser et al., 2016). K-means clustering (k = 6) was applied to both rs-fMRI and task fMRI data, resulting in six distinct CAPs: the default mode network (DMN), salience network (SN), visual network (VIS), negative visual network (VIS-), control network (CON), and somatomotor network (SMN) (Fig. 1A). Each CAP time course was derived by correlating the fMRI frames to their respective centroid. The GS was calculated as the mean BOLD signal across the brain, filtered between 0.01 and 0.03 Hz, and decomposed into phase components. To investigate how GS coupled with CAPs during rest and task states, we analyzed the phase of GS at the time of CAP occurrence (Gutierrez-Barragan, Basson, Panzeri, & Gozzi, 2019).
Results:
We found that CAPs fluctuate in synchrony with global brain activity. Consistent with previous studies, both the power spectrum of CAP time series and GS exhibited peaks in the infra-slow frequency band (0.01–0.03 Hz) during both rest and task (Fig. 1B). We then examined the coherence between CAP time series and GS and found that the SMN and CON CAPs showed peak coherence with GS in the infra-slow band, while other four CAPs exhibited peak coherence on ~0.1 Hz (Fig. 1C). These findings suggest that CAPs fluctuated with GS in a frequency-dependent manner. To assess the cognitive function of the temporal structure provided by GS, we measured the GS phase at which CAPs occurred. We used two metrics to compare rest and task conditions: phase position and phase locking strength measured by mean vector length. First, we found that different CAPs were locked to distinct phases of GS (Fig. 1D) (Watson-Williams test; p < 0.005 for both rest and task) and were significant lock to GS phase (p < 0.005; permutation test), except for the SN CAP in the task condition (p = 0.26, permutation test). As for the difference between rest and task, all CAPs exhibited significant differences in phase occurrence (multi-sample test for equal median directions, p < 0.005 for all CAPs), with the phase positions of CON and SMN CAPs showing the greatest changes from rest to task. Additionally, the phase locking of VIS and VIS- CAPs to GS was stronger during task conditions compared to rest (Fig. 1E), indicating that visual-related CAPs occurred more consistently in specific GS phases during task. We also observed reduced variance in the GS phase lag between CAPs during the task (Fig. 1F), suggesting that the GS constrains CAP transitions more tightly during task conditions.
Conclusions:
Our findings demonstrate that the full-phase global signal temporally modulates the spatiotemporal structure of local networks, coordinating cognitive functions by governing CAP transitions from rest to task. These results provide new insights into the dynamic relationship between global brain activity and the organization of brain networks, highlighting the role of GS in coordinating cognitive states.
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 2
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Cognition
FUNCTIONAL MRI
Other - Global signal, Co-activation patterns
1|2Indicates the priority used for review
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Gutierrez-Barragan, D., Basson, M. A., Panzeri, S., & Gozzi, A. (2019). Infraslow State Fluctuations Govern Spontaneous fMRI Network Dynamics. Curr Biol, 29(14), 2295-2306 e2295. doi:10.1016/j.cub.2019.06.017
Liu, X., de Zwart, J. A., Schoelvinck, M. L., Chang, C., Ye, F. Q., Leopold, D. A., & Duyn, J. H. (2018). Subcortical evidence for a contribution of arousal to fMRI studies of brain activity. Nature Communications, 9. doi:10.1038/s41467-017-02815-3
Zhang, J., Huang, Z., Tumati, S., & Northoff, G. (2020). Rest-task modulation of fMRI-derived global signal topography is mediated by transient coactivation patterns. Plos Biology, 18(7). doi:10.1371/journal.pbio.3000733