Disambiguating the role of blood flow and global signal with Partial Information Decomposition
Year: 2019
Type: article
Abstract: Event Abstract Back to Event Disambiguating the role of blood flow and global signal with Partial Information Decomposition Nigel Colenbier1*, Frederik Van De Steen1, Lucina Q. Uddin2, Russell A. Poldrack3, Vince Calhoun4, 5 and Daniele Marinazzo1 1 Ghent University, Belgium 2 Department of Psychology, Miami University, United States 3 Stanford University, United States 4 Mind Research Network (MRN), United States 5 Department of Electrical and Computer Engineering, University of New Mexico, United States 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 (Saad et al., 2012). 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 (Erdoğan et al., 2016). 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 (Faes et al., 2017) 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. Figure 1 Figure 2 References Saad, Z. S., Gotts, S. J., Murphy, K., Chen, G., Jo, H. J., Martin, A., & Cox, R. W. (2012). Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression. Brain Connectivity, 2(1), 25–32. https://doi.org/10.1089/brain.2012.0080 Faes, L., Marinazzo, D., & Stramaglia, S. (2017). Multiscale information decomposition: Exact computation for multivariate Gaussian processes. Entropy, 19(8), 408. https://doi.org/10.3390/e19080408 Erdoğan, S. B., Tong, Y., Hocke, L. M., Lindsey, K. P., & deB Frederick, B. (2016). Correcting for Blood Arrival Time in Global Mean Regression Enhances Functional Connectivity Analysis of Resting State fMRI-BOLD Signals. Frontiers in Human Neuroscience, 10, 311. https://doi.org/10.3389/fnhum.2016.00311 Keywords: global signal regression, Partial information decomposition, physiological artifacts, resting state fMRI, network connectivity, blood arrival time Conference: 13th National Congress of the Belgian Society for Neuroscience , Brussels, Belgium, 24 May - 24 May, 2019. Presentation Type: Poster presentation Topic: Behavioral/Systems Neuroscience Citation: Colenbier N, Van De Steen F, Uddin LQ, Poldrack RA, Calhoun V and Marinazzo D (2019). Disambiguating the role of blood flow and global signal with Partial Information Decomposition. Front. Neurosci. Conference Abstract: 13th National Congress of the Belgian Society for Neuroscience . doi: 10.3389/conf.fnins.2019.96.00045 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers' terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 24 Apr 2019; Published Online: 27 Sep 2019. * Correspondence: Mx. Nigel Colenbier, Ghent University, Ghent, Belgium, [email protected] Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Nigel Colenbier Frederik Van De Steen Lucina Q Uddin Russell A Poldrack Vince Calhoun Daniele Marinazzo Google Nigel Colenbier Frederik Van De Steen Lucina Q Uddin Russell A Poldrack Vince Calhoun Daniele Marinazzo Google Scholar Nigel Colenbier Frederik Van De Steen Lucina Q Uddin Russell A Poldrack Vince Calhoun Daniele Marinazzo PubMed Nigel Colenbier Frederik Van De Steen Lucina Q Uddin Russell A Poldrack Vince Calhoun Daniele Marinazzo Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.
Source: Frontiers in Neuroscience
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,950
OpenAlex ID: W2976371733