We are investigators from different disciplines, who use fMRI as a means to quantify different aspects of cognition and behavior. During the last year, we noticed that our independent studies using fMRI converged on a common topic, the Global Signal (GS). Indicatively, Prof. Uddin shows that the GS topography can be informative across the life span (Li, et al 2019; Nomi et al., 2023), Prof. Van De Ville indicates that GS can mathematically influence dynamic connectivity analyses (Van de Ville et al, 2019), Dr Mortaheb shows that the GS amplitude completes the interpretation of ongoing mental state reports (Mortaheb et al, 2022; 2023), and Prof. Liu’s work on the GS aptly contextualizes this work (Liu et al, 2018). Coming together as a panel, we believe that we can provide an updated comprehensive discussion on where we stand methodologically and theoretically on the ongoing debate about the GS as a source of noise or of rich information.
• To educate on the fMRI global signal methodological debate
• To update on the cognitive and behavioral properties accounted by the fMRI global signal
• To invite on a comprehensive consideration when dealing with the global signal, instead of a polarized view as noise or information.
Our Symposium is relevant for researchers working with fMRI data from various backgrounds, including bioengineers, medical professionals, and computational neuroscientists.
The global signal is widely used as a regressor or normalization factor for removing the effects of global variations in the analysis of functional magnetic resonance imaging (fMRI) experiments. However, there continues to be considerable controversy regarding its use and interpretation. In this talk, I will review the basic properties of the global signal and describe the various ways that it has been used for the analysis of both task-related and resting-state fMRI data. I will also discuss the sources of information that are embedded in the global signal and touch upon emerging views of its role in the analysis of fMRI studies.
, UC San Diego San Diego, CA
Global signal regression (GSR) is often considered as a preprocessing step for resting-state fMRI data. The many different analysis pipelines for dynamic functional connectivity (dFC) treat the global signal implicitly through centering, normalization, clustering, and other operations. Therefore, in some cases, the global signal can be separated by the analysis method without the need for GSR as a preprocessing step. In addition, dFC allows to extract properties of the spatiotemporal organization of the global signal that can provide insights into its role and function.
Spatiotemporal patterns resembling global signal (GS) topography were shown to explain > 20% of the variance in intrinsic BOLD timeseries (Bolt et al., 2022). Such GS topography was also mediated by individual differences in positive/ negative life outcomes and psychological function (Li et al., 2019). More recently, we found systematic age-associations, where subcortical vs. cortical contributions to the GS topography differed across the lifespan (Nomi et al., 2023). These results suggest that the GS contains rich information related to trait-level cognition, highlighting the need to carefully consider whether or not to remove the GS during preprocessing.
, University of California Los Angeles
Department of Psychiatry and Biobehavioral Sciences
Culver City, CA
The fMRI global signal (GS) as the average BOLD signal across the brain, is a controversial aspect of the brain’s functional data analysis. While some argue that the GS reflects non-neural physiological noise, others contend it contains valuable information about the brain’s functional organization. We recently showed that the GS amplitude varies across different mental states, from mind blanking (Mortaheb et al 2022; Boulakis et al, 2023) to psychedelic experiences (Mortaheb et al, 2023). Here, I will present how the GS amplitude complements the dynamic functional connectivity analysis to better interpret the neural substrate of different mental states.
, Cyclotron Research Center, GIGA Institute, University of Liège Liège, -- SELECT --