TY - GEN
T1 - Voxelwise optimization of hemodynamic lags to improve regional CVR estimates in breath-hold fMRI
AU - Moia, Stefano
AU - Stickland, Rachael C.
AU - Ayyagari, Apoorva
AU - Termenon, Maite
AU - Caballero-Gaudes, Cesar
AU - Bright, Molly G.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - Cerebrovascular Reactivity (CVR), the responsiveness of blood vessels to a vasodilatory stimulus, is an important indicator of cerebrovascular health. Assessing CVR with fMRI, we can measure the change in the Blood Oxygen Level Dependent (BOLD) response induced by a change in CO2 pressure (%BOLD/mmHg). However, there exists a temporal offset between the recorded CO2 pressure and the local BOLD response, due to both measurement and physiological delays. If this offset is not corrected for, voxel-wise CVR values will not be accurate. In this paper, we propose a framework for mapping hemodynamic lag in breath-hold fMRI data. As breath-hold tasks drive task-correlated head motion artifacts in BOLD fMRI data, our framework for lag estimation fits a model that includes polynomial terms and head motion parameters, as well as a shifted variant of the CO2 regressor (±9 s in 0.3 s increments), and the hemodynamic lag at each voxel is the shift producing the maximum total model R2 within physiological constraints. This approach is evaluated in 8 subjects with multi-echo fMRI data, resulting in robust maps of hemodynamic delay that show consistent regional variation across subjects, and improved contrast-to-noise compared to methods where motion regression is ignored or performed earlier in preprocessing.Clinical Relevance - We map hemodynamic lag using breathhold fMRI, providing insight into vascular transit times and improving the regional accuracy of cerebrovascular reactivity measurements.
AB - Cerebrovascular Reactivity (CVR), the responsiveness of blood vessels to a vasodilatory stimulus, is an important indicator of cerebrovascular health. Assessing CVR with fMRI, we can measure the change in the Blood Oxygen Level Dependent (BOLD) response induced by a change in CO2 pressure (%BOLD/mmHg). However, there exists a temporal offset between the recorded CO2 pressure and the local BOLD response, due to both measurement and physiological delays. If this offset is not corrected for, voxel-wise CVR values will not be accurate. In this paper, we propose a framework for mapping hemodynamic lag in breath-hold fMRI data. As breath-hold tasks drive task-correlated head motion artifacts in BOLD fMRI data, our framework for lag estimation fits a model that includes polynomial terms and head motion parameters, as well as a shifted variant of the CO2 regressor (±9 s in 0.3 s increments), and the hemodynamic lag at each voxel is the shift producing the maximum total model R2 within physiological constraints. This approach is evaluated in 8 subjects with multi-echo fMRI data, resulting in robust maps of hemodynamic delay that show consistent regional variation across subjects, and improved contrast-to-noise compared to methods where motion regression is ignored or performed earlier in preprocessing.Clinical Relevance - We map hemodynamic lag using breathhold fMRI, providing insight into vascular transit times and improving the regional accuracy of cerebrovascular reactivity measurements.
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U2 - 10.1109/EMBC44109.2020.9176225
DO - 10.1109/EMBC44109.2020.9176225
M3 - Conference contribution
C2 - 33018273
AN - SCOPUS:85091003010
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1489
EP - 1492
BT - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Y2 - 20 July 2020 through 24 July 2020
ER -