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Modeling the carbon-dioxide response function in fMRI under task and resting-state conditions

Seyedmohammad Shams, Prokopis Prokopiou, Azin Esmaelbeigi, Georgios D. Mitsis, J. Jean Chen
doi: https://doi.org/10.1101/2022.07.04.498727
Seyedmohammad Shams
1Rotman Research Institute, Baycrest Health Sciences; Hospital, Harvard Medical School, Boston, MA, USA
2Department of Neurology, Henry Ford Health; Hospital, Harvard Medical School, Boston, MA, USA
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Prokopis Prokopiou
3Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Azin Esmaelbeigi
1Rotman Research Institute, Baycrest Health Sciences; Hospital, Harvard Medical School, Boston, MA, USA
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Georgios D. Mitsis
4Department of Bioengineering, McGill University
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J. Jean Chen
1Rotman Research Institute, Baycrest Health Sciences; Hospital, Harvard Medical School, Boston, MA, USA
4Department of Bioengineering, McGill University
5Department of Medical Biophysics, University of Toronto
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  • For correspondence: jchen@research.baycrest.org
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Abstract

Conventionally, cerebrovascular reactivity (CVR) is estimated as the amplitude of the hemodynamic response to vascular stimuli. While the CVR amplitude has established clinical utility, the temporal characteristics of CVR have been increasingly explored and may yield even more pathology-sensitive parameters. This work is motivated by the current need to evaluate the feasibility of dCVR modeling in various noise conditions. In this work, we present a comparison of several recently published model-based deconvolution approaches for estimating h(t), including maximum a posterior likelihood (MAP), inverse logit (IL), canonical correlation analysis (CCA), and basis expansion (using Gamma and Laguerre basis sets). To aid the comparison, we devised a novel simulation framework that allowed us to target a wide range of SNRs, ranging from 10 to −7 dB, representative of both task and resting-state CO2 changes. In addition, we built ground-truth h(t) into our simulation framework, overcoming the practical limitation that the true h(t) is unknown in methodological evaluations. Moreover, to best represent realistic noise found in fMRI scans, we extracted it from in-vivo resting-state scans. Furthermore, we introduce a simple optimization of the CCA method (CCAopt) and compare its performance to these existing methods. Our findings suggest that model-based methods can reasonably estimate dCVR even amidst high noise, and in a manner that is largely independent of the underlying model assumptions for each method. We also provide a quantitative basis for making methodological choices, based on the desired dCVR parameters, the estimation accuracy and computation time. The BEL method provided the highest accuracy and robustness, followed by the CCAopt and IL methods. Of the three, the CCAopt method required the lowest computational time. These findings lay the foundation for wider adoption of dCVR estimation in CVR mapping.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted July 04, 2022.
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Modeling the carbon-dioxide response function in fMRI under task and resting-state conditions
Seyedmohammad Shams, Prokopis Prokopiou, Azin Esmaelbeigi, Georgios D. Mitsis, J. Jean Chen
bioRxiv 2022.07.04.498727; doi: https://doi.org/10.1101/2022.07.04.498727
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Modeling the carbon-dioxide response function in fMRI under task and resting-state conditions
Seyedmohammad Shams, Prokopis Prokopiou, Azin Esmaelbeigi, Georgios D. Mitsis, J. Jean Chen
bioRxiv 2022.07.04.498727; doi: https://doi.org/10.1101/2022.07.04.498727

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