Disambiguating the role of blood flow and global signal with Partial Information Decomposition

Abstract

In resting state functional magnetic resonance imaging (rs-fMRI) a common strategy to reduce the impact of physiological noise and other artifacts on the data is to regress out the global signal using global signal regression (GSR). Yet, GSR is one of the most controversial preprocessing techniques for rs-fMRI. It effectively removes non-neuronal artifacts, but at the same time it alters correlational patterns in unpredicted ways. Furthermore the global signal includes neural BOLD signal by construction, and is consequently related to neural and behavioral function. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proved to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improve denoising methods. Using GSR but not correcting for blood flow might selectively introduce physiological artifacts across intrinsic connectivity networks that distort the functional connectivity estimates.

Predicting the fMRI signal fluctuation with echo-state neural networks trained on vascular network dynamics

“…The global rs-fMRI signal is a critical confound of correlation analysis with many contributing factors from both physiological and non-physiological sources. In particular, whether the global mean fMRI signal should be removed before the analysis, which can create spurious correlation features, has been debated [36][37][38][39][40][41][42]44 . Also, the global rs-fMRI signal can over-shadow specific intrinsic RSN features, e.g.…”

“…Because of the high variability in different dynamic states, physiological and non-physiological confounding factors also contribute to the rs-fMRI lowfrequency oscillation [33][34][35] . In particular, global fMRI signal fluctuations are one of the most controversial oscillatory features to be linked to dynamic brain signals [36][37][38][39][40][41][42][43][44] . For example, the rs-fMRI signal from the white-matter tract has been used as a nuisance regressor to remove the global noise contribution 45,46 .…”

Intersectional synergies: untangling irreducible effects of intersecting identities via information decomposition

“…Crucially, information theory is well-equipped to handle the problem of decomposing multivariate relationships in data into synergistic (intersectional) and redundant components using a framework known as partial information decomposition (PID) [24,25] (see Section 2.2 for details), and has been applied in a variety of fields, including interpretable machine learning [26], medical imaging [27], biological neural networks [28,29], ecology [30], evolution [31], as well as to philosophical questions such as the nature of "emergence" [32,33] and consciousness [34]. This interdisciplinary group of results suggests that synergistic relationships "greater than the sum of their parts" are ubiquitous in both natural and human-made systems, so it is natural to hypothesize that they may also exist in social systems.…”