Mapping Cerebral Blood Flow Pathways in Sickle Cell Disease Using BOLD fMRI
Poster No:
2119
Submission Type:
Abstract Submission
Authors:
Jessica Budde1, Brianna Kish1, Ying Wang2, Yunjie Tong1
Institutions:
1Purdue University, West Lafayette, IN, 2Indiana University School of Medicine, Indianapolis, IN
First Author:
Co-Author(s):
Introduction:
Sickle cell disease (SCD) is a genetic blood disorder characterized by the production of abnormal hemoglobin, reducing blood oxygen-carrying capacity (Arkuszewski et al., 2013; DeBaun et al., 2006). To compensate, the body increases cerebral blood flow, but overcompensation can result in cerebral vascular shunting; blood velocity is increased until oxygen off-loading efficiency is reduced (Juttukonda et al., 2017). Studies have shown that shunting is more common in SCD patients than in healthy individuals (Juttukonda et al., 2021). Systemic low-frequency oscillations (sLFOs) are a subset of low-frequency oscillations (0.01- 0.1 Hz) represented throughout the brain with high correlations and meaningful delay values in resting state BOLD-fMRI (Tong et al., 2017). A previous study from this group proposed a dual hemodynamic pathway for SCD patients to explain competing correlations and delays observed when averaging all fMRI signals across the brain (Kish et al., 2024). In this pathway, we argue that oxygen-rich blood from large arteries takes two complementary pathways to the venules and veins (Figure 1). Building on that framework, this study investigates the hemodynamic mechanisms underlying sLFOs in SCD patients using a voxel-wise approach with the aim of mapping both pathways.
Methods:
Data were collected from 22 participants with SCD and 11 age- and gender-matched non-SCD controls. Each subject underwent T1-weighted MRI and resting-state fMRI scans, with fMRI data preprocessed using FSL to correct motion artifacts (Jenkinson et al., 2012).
Building on prior research (Kish et al., 2024), we hypothesize that fast-moving, highly oxygenated shunting blood reaches the superior sagittal sinus (SSS) earlier than deoxygenated blood via the regular pathway. As illustrated in Figure 1, three scenarios emerge: (1) healthy subjects with regular flow and no shunting, (2) SCD group 1 with minimal shunting, and (3) SCD group 2 with increased shunting. SCD group 2, associated with severe blood measures, shows highly oxygenated blood predominantly in the SSS, leading to a negative maximum correlation coefficient with the global mean fMRI signal.
To classify patients, we divided them into groups 1 and 2 based on the maximum cross-correlation coefficient (MCCC) between the global mean signal and the SSS. Voxel-wise cross-correlation of SSS with brain signals was performed to map two pathways, classifying voxels as "shunting flow" or "regular flow" based on polarity and delay. A novel integrative approach was implemented to stabilize the resulting maps, which were then thresholded, standardized, and averaged for group interpretation.
Building on prior research (Kish et al., 2024), we hypothesize that fast-moving, highly oxygenated shunting blood reaches the superior sagittal sinus (SSS) earlier than deoxygenated blood via the regular pathway. As illustrated in Figure 1, three scenarios emerge: (1) healthy subjects with regular flow and no shunting, (2) SCD group 1 with minimal shunting, and (3) SCD group 2 with increased shunting. SCD group 2, associated with severe blood measures, shows highly oxygenated blood predominantly in the SSS, leading to a negative maximum correlation coefficient with the global mean fMRI signal.
To classify patients, we divided them into groups 1 and 2 based on the maximum cross-correlation coefficient (MCCC) between the global mean signal and the SSS. Voxel-wise cross-correlation of SSS with brain signals was performed to map two pathways, classifying voxels as "shunting flow" or "regular flow" based on polarity and delay. A novel integrative approach was implemented to stabilize the resulting maps, which were then thresholded, standardized, and averaged for group interpretation.
Results:
Figures 2A and 2B show the initial cross-correlation coefficients and delay maps for healthy subjects, SCD group 1, and SCD group 2, with SCD groups identified based on the maximum cross-correlation value between the global fMRI signal and SSS.
Figure 2A illustrates well-defined regular flow patterns in healthy subjects, covering most brain areas. In SCD group 1, these patterns are reduced, and they further diminish in group 2, where shunting flow increases, mainly in regions with large veins and high-density arterioles at the brain's base, bypassing the capillary bed. Figure 2B shows corresponding delay maps, with healthy subjects displaying a clear sequential blood flow progression, while SCD groups 1 and 2 show altered patterns with reduced size and changes in flow direction.
Limitation: Regular and shunting flow patterns in SCD group 2 are highly heterogeneous. Only one example patient is shown, and further validation, especially of the shunting maps, is needed for group 2 patients
Figure 2A illustrates well-defined regular flow patterns in healthy subjects, covering most brain areas. In SCD group 1, these patterns are reduced, and they further diminish in group 2, where shunting flow increases, mainly in regions with large veins and high-density arterioles at the brain's base, bypassing the capillary bed. Figure 2B shows corresponding delay maps, with healthy subjects displaying a clear sequential blood flow progression, while SCD groups 1 and 2 show altered patterns with reduced size and changes in flow direction.
Limitation: Regular and shunting flow patterns in SCD group 2 are highly heterogeneous. Only one example patient is shown, and further validation, especially of the shunting maps, is needed for group 2 patients
Conclusions:
This study is the first to use fMRI to explore the hemodynamics in SCD patients, providing evidence for the dual hemodynamic pathway and offering new insights into the pathophysiology of SCD.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling
Methods Development
Novel Imaging Acquisition Methods:
BOLD fMRI
Physiology, Metabolism and Neurotransmission:
Cerebral Metabolism and Hemodynamics 2
Neurophysiology of Imaging Signals 1
Keywords:
Blood
Data analysis
FUNCTIONAL MRI
1|2Indicates the priority used for review
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Provide references using APA citation style.
DeBaun, M. R., Derdeyn, C. P., & McKinstry, R. C. (2006). Etiology of strokes in children with sickle cell anemia. In Mental Retardation and Developmental Disabilities Research Reviews (Vol. 12, Issue 3). https://doi.org/10.1002/mrdd.20118
Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012). Review FSL. NeuroImage, 62.
Juttukonda, M. R., Donahue, M. J., Waddle, S. L., Davis, L. T., Lee, C. A., Patel, N. J., Pruthi, S., Kassim, A. A., & Jordan, L. C. (2021). Reduced oxygen extraction efficiency in sickle cell anemia patients with evidence of cerebral capillary shunting. Journal of Cerebral Blood Flow and Metabolism, 41(3). https://doi.org/10.1177/0271678X20913123
Juttukonda, M. R., Jordan, L. C., Gindville, M. C., Davis, L. T., Watchmaker, J. M., Pruthi, S., & Donahue, M. J. (2017). Cerebral hemodynamics and pseudo-continuous arterial spin labeling considerations in adults with sickle cell anemia. NMR in Biomedicine, 30(2). https://doi.org/10.1002/nbm.3681
Kish, B., Yao, J., Frels, A. J., Budde, J., Nair, V. V., O’Brien, A., Wang, Y., & Tong, Y. (2024). A Novel Imaging Biomarker in Cerebral Blood Flow in Patients with Sickle Cell Disease using fMRI. Abstract Book 6: OHBM 2024 Annual Meeting, 425–427. https://doi.org/10.52294/001c.120596
Tong, Y., Lindsey, K. P., Hocke, L. M., Vitaliano, G., Mintzopoulos, D., & Frederick, B. D. (2017). Perfusion information extracted from resting state functional magnetic resonance imaging. Journal of Cerebral Blood Flow and Metabolism, 37(2). https://doi.org/10.1177/0271678X16631755