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Disambiguating the role of blood flow and global signal with partial information decomposition

(2020) NEUROIMAGE. 213.
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Abstract
Global signal (GS) is an ubiquitous construct in resting state functional magnetic resonance imaging (rs-fMRI), associated to nuisance, but containing by definition most of the neuronal signal. Global signal regression (GSR) effectively removes the impact of physiological noise and other artifacts, but at the same time it alters correlational patterns in unpredicted ways. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proven 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, while using hemodynamic and calcium mouse recordings we were able to confirm the presence of vascular effects, as calcium recordings lack hemodynamic information. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improving de-noising methods. Additionally, and beyond the mere issue of data denoising, we quantify the diverse and complementary effect of global and vessel BOLD signals on the dynamics of cortical areas.
Keywords
Cognitive Neuroscience, Neurology, RESTING-STATE FMRI, FUNCTIONAL CONNECTIVITY MRI, MOTION ARTIFACT, BRAIN ACTIVITY, ANTICORRELATIONS, NETWORKS, PATTERNS, NOISE, TIME, ICA

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MLA
Colenbier, Nigel, et al. “Disambiguating the Role of Blood Flow and Global Signal with Partial Information Decomposition.” NEUROIMAGE, vol. 213, 2020.
APA
Colenbier, N., Van de Steen, F., Uddin, L. Q., Poldrack, R. A., Calhoun, V. D., & Marinazzo, D. (2020). Disambiguating the role of blood flow and global signal with partial information decomposition. NEUROIMAGE, 213.
Chicago author-date
Colenbier, Nigel, Frederik Van de Steen, Lucina Q. Uddin, Russell A. Poldrack, Vince D. Calhoun, and Daniele Marinazzo. 2020. “Disambiguating the Role of Blood Flow and Global Signal with Partial Information Decomposition.” NEUROIMAGE 213.
Chicago author-date (all authors)
Colenbier, Nigel, Frederik Van de Steen, Lucina Q. Uddin, Russell A. Poldrack, Vince D. Calhoun, and Daniele Marinazzo. 2020. “Disambiguating the Role of Blood Flow and Global Signal with Partial Information Decomposition.” NEUROIMAGE 213.
Vancouver
1.
Colenbier N, Van de Steen F, Uddin LQ, Poldrack RA, Calhoun VD, Marinazzo D. Disambiguating the role of blood flow and global signal with partial information decomposition. NEUROIMAGE. 2020;213.
IEEE
[1]
N. Colenbier, F. Van de Steen, L. Q. Uddin, R. A. Poldrack, V. D. Calhoun, and D. Marinazzo, “Disambiguating the role of blood flow and global signal with partial information decomposition,” NEUROIMAGE, vol. 213, 2020.
@article{8653842,
  abstract     = {Global signal (GS) is an ubiquitous construct in resting state functional magnetic resonance imaging (rs-fMRI), associated to nuisance, but containing by definition most of the neuronal signal. Global signal regression (GSR) effectively removes the impact of physiological noise and other artifacts, but at the same time it alters correlational patterns in unpredicted ways. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proven 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, while using hemodynamic and calcium mouse recordings we were able to confirm the presence of vascular effects, as calcium recordings lack hemodynamic information. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improving de-noising methods. Additionally, and beyond the mere issue of data denoising, we quantify the diverse and complementary effect of global and vessel BOLD signals on the dynamics of cortical areas.},
  articleno    = {116699},
  author       = {Colenbier, Nigel and Van de Steen, Frederik and Uddin, Lucina Q. and Poldrack, Russell A. and Calhoun, Vince D. and Marinazzo, Daniele},
  issn         = {1053-8119},
  journal      = {NEUROIMAGE},
  keywords     = {Cognitive Neuroscience,Neurology,RESTING-STATE FMRI,FUNCTIONAL CONNECTIVITY MRI,MOTION ARTIFACT,BRAIN ACTIVITY,ANTICORRELATIONS,NETWORKS,PATTERNS,NOISE,TIME,ICA},
  language     = {eng},
  pages        = {18},
  title        = {Disambiguating the role of blood flow and global signal with partial information decomposition},
  url          = {http://dx.doi.org/10.1016/j.neuroimage.2020.116699},
  volume       = {213},
  year         = {2020},
}

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