The Hemodynamic Influence on Diffusivity Across b-values in the Healthy Human Cerebral White Matter
Poster No:
1914
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 magnetic resonance imaging (dMRI), by definition, is sensitive to all types of water molecule diffusion regardless of the origin, meaning that the unmodeled contribution from cerebral blood flow (CBF) potentially biases the dMRI measurements. This is especially of concern in the grey matter (GM), where CBF is higher, but has never been systematically assessed in the human white matter (WM) either. Although the dMRI image can be made less sensitive to fast flows by increasing the diffusion weighting (b values), previous research in rat models showed that mean diffusivity (MD) can differ by hemodynamic states even using medium to high b values (Ding et al., 2012; Rudrapatna et al., 2012). This work aims to characterize the role of perfusion in diffusion imaging in the human brain across multiple b values using a hypercapnia experiment, and to compare it to predictions based on simulations.
Methods:
Hypercapnia was used to induce temporary hemodynamic changes in the subject by inhaling 4% CO2 in a block-design protocol: 4min-off, 6 min-on, 2 min-off(Sicard & Duong, 2005). dMRI data was collected on the subject under both the baseline and hypercapnic conditions using a Siemens Prisma 3T scanner, TR/TE=3.1s/0.064s, 1.5mm isotropic resolution, 6, 50, and 50 directions on b=250, 1000, and 2000s/mm2 respectively. Pseudo-continuous arterial spin labelling (pCASL) data was collected with TR/TE=5.2s/0.013s, labelling duration of 0.7 s and post-labelling delay=1.5s. pCASL images were corrected on slice-timing, motion, and outliers, followed by cerebral blood flow (CBF) quantification. dMRI images were corrected for eddy currents and susceptibility distortions and then fitted into the diffusion tensor imaging (DTI) model to calculate the MD(Andersson et al., 2003). The difference in MD between two gas conditions was calculated in percentage voxelwise in WM. The effect of CBF change on MD at each b value was then simulated, which incorporated 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. CBF was found to primarily increase with CO2 in the grey matter and superficial WM, but decrease in part of deep WM regions (Figure 1d, 1e). Likewise, MD was found to be higher at hypercapnia in certain regions and lower in other regions by up to 23.38%, with the magnitude of changes gradually reduced with b-values (Figure 1a-1c). The predicted MD increased based on an expected CBF increase, with the level descending along b-values (Figure 2a-2b). A simulated CBF decrease also led to an overestimation of MD, and by a larger degree than positive CBF change (Figure 2c-2d).
Conclusions:
Our work showed that hypercapnia impacts WM MD in a region- and b-value-dependent manner. At lower diffusion weighting, the larger MD variations might indicate increased sensitivity to perfusion-related water motion. Although the medium to high diffusion-weighted images were less affected, the hemodynamic influence still persists, contrary to expectations. Also contrary to expectations, CBF decreased in large swathes of WM regions during hypercapnia. This is consistent with previous observations in WM pathologies(Mandell et al., 2008), and puts into question whether the changes in MD are mainly due to CBF changes. The observed variability in both MD and CBF changes across WM highlights the complexity of the physiological response to elevated CO2. The simulated predictions demonstrate the dependence of MD biases on the magnitude of CBF changes. The MD overestimation became more pronounced with negative simulated CBF changes, likely due to inclusion of CSF flow dependence on CO2. This pattern was consistent with the experimental data as indicated by arrows in Figure 1a-1d.
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
Cerebro Spinal Fluid (CSF)
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.
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.
Mandell, D. M., Han, J. S., Poublanc, J., Crawley, A. P., Kassner, A., Fisher, J. A., & Mikulis, D. J. (2008). Selective reduction of blood flow to white matter during hypercapnia corresponds with leukoaraiosis. Stroke; a Journal of Cerebral Circulation, 39(7), 1993–1998.
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.