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TDM: a temporal decomposition method for removing venous effects from task-based fMRI

View ORCID ProfileKendrick Kay, View ORCID ProfileKeith W. Jamison, Ruyuan Zhang, Kamil Ugurbil
doi: https://doi.org/10.1101/868455
Kendrick Kay
Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota
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  • For correspondence: kay@umn.edu
Keith W. Jamison
Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota
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Ruyuan Zhang
Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota
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Kamil Ugurbil
Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota
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Abstract

Most functional magnetic resonance imaging (fMRI) is conducted with gradient-echo pulse sequences. Although this yields high sensitivity to blood oxygenation level dependent (BOLD) signals, gradient-echo acquisitions are heavily influenced by venous effects which limit the ultimate spatial resolution and spatial accuracy of fMRI. While alternative acquisition methods such as spin-echo can be used to mitigate venous effects, these methods lead to serious reductions in signal-to-noise ratio and spatial coverage, and are difficult to implement without leakage of undesirable non-spin-echo effects into the data. Moreover, analysis heuristics such as masking veins or sampling inner cortical depths using high-resolution fMRI may be helpful, but sacrifice information from many parts of the brain. Here, we describe a new analysis method that is compatible with conventional gradient-echo acquisition and provides venous-free response estimates throughout the entire imaged volume. The method involves fitting a low-dimensional manifold characterizing variation in response timecourses observed in a given dataset, and then using identified early and late timecourses as basis functions for decomposing responses into components related to the microvasculature (capillaries and small venules) and the macrovasculature (veins), respectively. We show that this Temporal Decomposition through Manifold Fitting (TDM) method is robust, consistently deriving meaningful timecourses in individual fMRI scan sessions. Moreover, we show that by removing late components, TDM substantially reduces the superficial cortical depth bias present in gradient-echo BOLD responses and eliminates artifacts in cortical activity maps. TDM is general: it can be applied to any task-based fMRI experiment, can be used with standard- or high-resolution fMRI acquisitions, and can even be used to remove residual venous effects from specialized acquisition methods like spin-echo. We suggest that TDM is a powerful method that improves the spatial accuracy of fMRI and provides insight into the origins of the BOLD signal.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted December 08, 2019.
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TDM: a temporal decomposition method for removing venous effects from task-based fMRI
Kendrick Kay, Keith W. Jamison, Ruyuan Zhang, Kamil Ugurbil
bioRxiv 868455; doi: https://doi.org/10.1101/868455
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TDM: a temporal decomposition method for removing venous effects from task-based fMRI
Kendrick Kay, Keith W. Jamison, Ruyuan Zhang, Kamil Ugurbil
bioRxiv 868455; doi: https://doi.org/10.1101/868455

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