The Hemodynamic Impact on Fractional Anisotropy Across b-values in Healthy Human White Matter
Poster No:
1912
Submission Type:
Abstract Submission
Authors:
Yutong Sun1,2, Nuwan Nanayakkara1, Jordan Chad1, J. Jean Chen1,2,3
Institutions:
1Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario, Canada, 2Medical Biophysics, University of Toronto, Toronto, Ontario, Canada, 3Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
First Author:
Yutong Sun
Rotman Research Institute, Baycrest Health Sciences|Medical Biophysics, University of Toronto
Toronto, Ontario, Canada|Toronto, Ontario, Canada
Rotman Research Institute, Baycrest Health Sciences|Medical Biophysics, University of Toronto
Toronto, Ontario, Canada|Toronto, Ontario, Canada
Co-Author(s):
J. Jean Chen
Rotman Research Institute, Baycrest Health Sciences|Medical Biophysics, University of Toronto|Biomedical Engineering, University of Toronto
Toronto, Ontario, Canada|Toronto, Ontario, Canada|Toronto, Ontario, Canada
Rotman Research Institute, Baycrest Health Sciences|Medical Biophysics, University of Toronto|Biomedical Engineering, University of Toronto
Toronto, Ontario, Canada|Toronto, Ontario, Canada|Toronto, Ontario, Canada
Introduction:
Diffusion MRI (dMRI) has been widely used to assess the microstructure of human cerebral white matter (WM) in vivo(Bihan & Iima, 2015). Fractional anisotropy (FA) has been widely used as an indicator of WM integrity, with healthy WM having high FA. However, dMRI assesses diffusion of water molecules regardless of the origin, making it likely to be biased by blood flow. This caveat has been assumed negligible due to the low cerebral blood flow (CBF) and presumed isotropy of blood flow (Vavilala et al., 2002). Nevertheless, work in the rat model showed that CBF influences the mean diffusivity (MD)(Ding et al., 2012; Rudrapatna et al., 2012). Given the quantitative relationship between MD and FA, we hypothesized that FA would also be contaminated by CBF in WM, and the severity of bias would differ across the level of diffusion-weighting (b-values) used when collecting dMRI images.
Methods:
Hypercapnia (inhalation of 4% CO2) was used to induce a temporary vasodilation and a raise in CBF in a block-design protocol: 4min-off, 6min-on, 2min-off(Sicard & Duong, 2005). dMRI scans were performed under both baseline and hypercapnia conditions using a Siemens Prisma 3T scanner, TR=3.1s, TE=0.064s, 1.5mm isotropic resolution, 6xb=250, 50xb=1000, and 50xb=2000s/mm2. Collected dMRI images were corrected on eddy current and susceptibility distortions, and fitted into a diffusion tensor imaging (DTI) model to quantify the FA. A pseudo-continuous arterial spin labelling (pCASL) scan was done with TR=5.2s, TE=0.013s, labelling duration of 0.7 s and post-labelling delay=1.5s. The pCASL data was corrected on slice-timing, motion, and outliers. OXFORD_ASL was used for CBF quantification(Chappell et al., 2020). The percentage differences between the baseline and hypercapnic FA and CBF were calculated voxelwise within the WM. The effect of CBF variation on FA and MD was also simulated using the same gradient directions, accounting for 4 aspects of a hypercapnic challenge: (1) a change in pseudo-diffusion of blood; (2) a proportionate change in cerebrospinal fluid (CSF) volume; (3) a change in pseudo-diffuson of CSF; (4) a change in blood oxygenation due to the CBF change, assuming no metabolic change from the CO2.
Results:
Data is shown for a single young healthy subject. In the WM, a region-dependent increase and decrease of CBF responding to CO2 were both observed (Figure 1e), although CBF mainly increased in the grey matter (Figure 1d). Likewise, a mixture of positive and negative changes was observed for FA under all b-values, with the b=250s/mm2 shell showing the most pronounced change (Figure 1a-1c). Notably, CO2-induced decrease in FA (up to 20.52%) was primarily observed in deep WM, while FA increased in superficial WM. The simulated CBF change by ±40% led to an increase of MD (Figure 2a,2c) and a decrease of FA (Figure 2b,2d).
Conclusions:
Our data showed the significant influence of CBF on WM FA. FA is more affected at low diffusion weighting (b=250s/mm2), suggesting heavier participation of blood-flow-related water molecule motions. The influence is lower but persists in b=1000 and 2000s/mm2, implying the hemodynamic contamination should not be neglected even in medium-to-high diffusion weightings. These trends echo our simulations. The region-dependent heterogeneity in FA changes suggests the complexity of the intersections between cerebral vasculature and microstructure. Since microvascular CBF can mimic incoherent diffusion, FA may decrease if the contribution of isotropic blood flow increases (i.e. in capillary beds). On the contrary, FA may increase in vascular-rich regions such as superficial WM where coherent vessel orientations align with WM, even at high b, since CBF is not uniform. If the directions of vessels and WM tracts do not agree, FA may decrease. Taken together, our work calls on more caution about hemodynamic bias when using FA to assess human cerebral WM.
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis
Novel Imaging Acquisition Methods:
Diffusion MRI 1
Physiology, Metabolism and Neurotransmission:
Cerebral Metabolism and Hemodynamics 2
Keywords:
Blood
Cerebral Blood Flow
Data analysis
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Chappell, M. A., Craig, M. S., & Woolrich, M. W. (2020). Stochastic variational Bayesian inference for a nonlinear forward model. In arXiv [eess.SP]. arXiv. http://arxiv.org/abs/2007.01675
Ding, A. Y., Chan, K. C., & Wu, E. X. (2012). Effect of cerebrovascular changes on brain DTI quantitation: a hypercapnia study. Magnetic Resonance Imaging, 30(7), 993–1001.
Rudrapatna, U. S., van der Toorn, A., van Meer, M. P. A., & Dijkhuizen, R. M. (2012). Impact of hemodynamic effects on diffusion-weighted fMRI signals. NeuroImage, 61(1), 106–114.
Sicard, K. M., & Duong, T. Q. (2005). Effects of hypoxia, hyperoxia, and hypercapnia on baseline and stimulus-evoked BOLD, CBF, and CMRO2 in spontaneously breathing animals. NeuroImage, 25(3), 850–858.
Vavilala, M. S., Lee, L. A., & Lam, A. M. (2002). Cerebral blood flow and vascular physiology. Anesthesiology Clinics of North America, 20(2), 247–264, v.