Modeling Dynamic Cerebrovascular Reactivity in the Postpartum Period Using BOLD-fMRI
Poster No:
2103
Submission Type:
Abstract Submission
Authors:
Natali van Zijl1, Joana Pinto1, Stephen Payne2, Daniel Bulte1
Institutions:
1University of Oxford, Oxford, Oxfordshire, 2Institute of Applied Mechanics, National Taiwan University, Taipei, Taiwan
First Author:
Co-Author(s):
Introduction:
Women's health is an important topic that has only recently started attracting more interest and funding. Specifically, pregnancy can lead to brain anatomical changes lasting up to two years postpartum (Pritschet et al., 2024) but associated neurovascular changes have yet to be investigated. Dynamic cerebrovascular reactivity (dCVR), the temporal aspect of the cerebral vasculature's ability to respond to vasoactive stimuli such as arterial CO2, has also gained interest recently, with differences reported between young and older controls (West et al., 2019), and between subjects with mild-cognitive deficit and Alzheimer's disease (Holmes et al., 2020). This study used a hybrid approach, combining data-based impulse response (IR) estimation from BOLD-fMRI data and a physiological model for mechanistic insights, to compare the magnitude and timing of dCVR in two age-matched groups: postpartum women and controls.
Methods:
Data was collected from 13 postpartum women (33.6 ± 4.3 years, postpartum period: 14.2 ± 6.2 months after giving birth) and 10 women with no pregnancy history (31.0 ± 6.7 years) on a 3T Siemens Prisma Scanner (32 channel head coil). A BOLD-fMRI sequence (GE-EPI, 2.4mm isotropic, MB=6, TR/TE=800/30ms, 450 volumes) was acquired during a 5% CO2 challenge protocol (Suri et al., 2021) while sampling expired CO2 values. BOLD-fMRI data were processed using a standard pipeline (motion correction, spatial smoothing 4 mm, temporal filtering 100s) in FSL (Jenkinson et al., 2012) and the first five volumes were deleted to allow for signal stabilization. End-tidal CO2 (PetCO2) values were extracted from the expired CO2 trace, interpolated with a cubic spline, and resampled at the BOLD-fMRI sampling frequency of 1.25 Hz. Subject-specific PetCO2 time shifts were obtained by cross correlation with the average whole-brain BOLD-fMRI signals.
Data analysis was performed using a physiological model, extended from (Payne, 2006), that describes dCVR as a second order response of the vasculature to changes in arterial CO2 partial pressure. Model parameters, a gain (Ga) and two time constants (τa1, τa2), were optimized by fitting the model to CO2-flow IRs estimated from the PetCO2 and BOLD-fMRI data for nine brain regions (MNI atlas) per subject. IRs were derived by estimating the first order Volterra kernel using a basis expansion method with spherical Laguerre bases. Hyperparameters, model order and exponential decay, were selected per brain region as the average values of grid searches minimizing BIC for each subject. Differences between groups were investigated using linear mixed effects models, with group and brain region as fixed effects and subject as a random effect.
Data analysis was performed using a physiological model, extended from (Payne, 2006), that describes dCVR as a second order response of the vasculature to changes in arterial CO2 partial pressure. Model parameters, a gain (Ga) and two time constants (τa1, τa2), were optimized by fitting the model to CO2-flow IRs estimated from the PetCO2 and BOLD-fMRI data for nine brain regions (MNI atlas) per subject. IRs were derived by estimating the first order Volterra kernel using a basis expansion method with spherical Laguerre bases. Hyperparameters, model order and exponential decay, were selected per brain region as the average values of grid searches minimizing BIC for each subject. Differences between groups were investigated using linear mixed effects models, with group and brain region as fixed effects and subject as a random effect.
Results:
Figure 1 shows the model fit to a data-based CO2-flow IR. Most estimated IRs showed the expected shape of an initial increase to peak, followed by a potential undershoot and settling to baseline. 12 IRs (6%) of specific subjects/regions (caudate, parietal, thalamus) were excluded from further analysis as they showed significant deviations in the expected shape (e.g. initial decrease), possibly due to low signal-to-noise ratio of PetCO2 traces and/or BOLD-fMRI data. Table 1 provides the distributions of the three optimized model parameters. No significant differences in model parameters were found between controls and postpartum women. Significant differences between brain regions were found in all model parameters, highlighting the spatial variability of dCVR. Although this is in line with previous reports (Prokopiou et al., 2019), these differences may be related to differences in hyperparameter values and numbers of voxels averaged.
Conclusions:
Our exploratory analysis found no significant differences between controls and postpartum women in the brain regions investigated. Future research should expand on this work by including a larger cohort and/or targeting specific regions known to be altered by pregnancy such as the Default Mode Network.
Modeling and Analysis Methods:
Methods Development 2
Physiology, Metabolism and Neurotransmission:
Cerebral Metabolism and Hemodynamics 1
Keywords:
Cerebral Blood Flow
Data analysis
FUNCTIONAL MRI
Modeling
MRI
Other - Pregnancy; Dynamic Cerebrovascular Reactivity
1|2Indicates the priority used for review
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Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Jenkinson, M. et al. (2012). FSL. NeuroImage, 62(2), 782–790.
Payne, S. J. (2006). A model of the interaction between autoregulation and neural activation in the brain. Mathematical Biosciences, 204(2), 260–281.
Pritschet, L. et al. (2024). Neuroanatomical changes observed over the course of a human pregnancy. Nature Neuroscience, 27(11), 2253–2260.
Prokopiou, P. C. et al. (2019). Modeling of dynamic cerebrovascular reactivity to spontaneous and externally induced CO2 fluctuations in the human brain using BOLD-fMRI. NeuroImage, 186, 533–548.
Suri, S. et al. (2021). Study Protocol: The Heart and Brain Study. Frontiers in Physiology, 12, 643725.
West, K. L. et al. (2019). BOLD hemodynamic response function changes significantly with healthy aging. NeuroImage, 188, 198–207.
Acknowledgements:
Work funded by grants from EPSRC (EP/S021507/1), UKRI (EP/S021507/1) and pilot grants from Wellcome Centre for Integrative Neuroimaging (WIN, University of Oxford) and Alzheimer’s Research UK Thames Valley, and supported by a Yushan Fellowship from the Ministry of Education, Taiwan (111V1004-2) and a scholarship from the Rhodes Trust.