Introduction

Gradient-Echo (GE) BOLD-fMRI is used with vasodilation to measure the cerebrovascular reactivity (CVR, i.e., the relative change in blood flow per unit of a vasodilatory stimulus).^1,2 However, GE-BOLD-fMRI is strongly weighted by cerebral blood volume (CBV).  The Spin-Echo (SE) BOLD signal is more selectively sensitive to the microvascular compartment^3,4 and, therefore, may be a better marker of CVR.

We implemented a GE/SE BOLD-ASL sequence, and we acquired data during breath-holding (BH)⁵, to compare CVR measurements.

Methods

Data from sixteen healthy young subjects (7 Females, Age=34±7years) were acquired on a 3T

MAGNETOM Prisma (Siemens Healthineers AG, Forchheim, Germany) with a 32-channel receive-only head coil.

A pseudo-continuous (pC)ASL research sequence, based on the vendor’s product 2DEPI ASL, was implemented using a GE EPI readout at a short TE=10ms for ASL and, following a second excitation, a longer TE=30ms for GE BOLD.⁶ A refocusing pulse was inserted after the second excitation to produce SE EPI with a TE=85ms (Fig.1).

The processing steps involved data normalization, motion and distortion correction, and

CO₂ end-tidal partial pressure (PetCO₂) signal analysis (Fig.2).^7-9,10-12

ASL CVR, GE BOLD-CVR and SE BOLD-CVR maps were warped into the MNI standard space.

Results and Discussion

Arterial CO₂ was estimated from end-tidal CO₂ and used as a regressor for voxel-wise estimation of CVRs.^13,14

The average grey matter (GM) SE-BOLD CVR correlated more strongly with the physiological (CBF) CVR, when compared to the GE-BOLD CVR, both across-subjects (voxel-wise CBF CVR estimates with an SNR>3) (Fig.3g), and across space (with clearer effects increasing number of subjects averaged) (Fig.3d-h).

Figures

Fig. 1 Sequence diagram (a) and parameters (b) of the pCASL acquisition with a modified dual excitation (DEXI) readout, where a spin-echo acquisition was added to the excitation-readout second module.

Fig. 2 Processing pipelines to calculate maps of CVR based on the CO₂ signal (mmHg) and breath-hold BOLD-ASL data. Abbreviations BH: breath-hold; BOLD: blood oxygenation leveldependent; CO₂: carbon dioxide; CVR: cerebrovascular reactivity; T1w: T1-weighted; ASL: Arterial Spin Labelling

Fig. 3 Left-Upper row a)-b): example of fMRI time-courses in the GM. (a) ASL and (b) BH GE and BH SE BOLD timecourses for one subject.

Left-Lower row c)-d): example of CVR, GE BOLD-CVR and SE BOLD-CVR maps (c) for one subject and (d) for the average maps. The same axial slice is displayed on each row.

Right e)-f)-g)-h): Correlation plot between (e) SE BOLD-CVR and GE BOLD-CVR, (f) GE BOLD-CVR and CVR, (g) SE BOLD-CVR and CVR. (h) Spatial correlations in the GM as a function of number of subjects averaged (randomly selected using bootstrapping): SE BOLD-CVR vs GE BOLD-CVR (yellow), GE BOLD-CVR vs CVR (blue), SE BOLD-CVR vs CVR (red). Pearson correlation (r) is reported. *p<0.05, ***p<10-3

Abbreviations. BH-BOLD: breath-hold blood oxygenation level-dependent signal; GE: gradient-echo; SE: spin-echo; ASL: Arterial Spin Labelling

Acknowledgements

European Union-NextGenerationEU- Italian Ministry of University and Research (MUR), Research National Program (PNR) and Projects of National Relevance (PRIN), Project Code: 2022BERM2F, Project Title: “Mapping Mitochondrial Function and Oxygen Metabolism in the Human Brain with Magnetic Resonance Imaging.” Funding call No. 104 of 02.02.2022, Concession decree No. 1065 of 18.07.2023 adopted by MUR, ERC Panel LS7 “Prevention, Diagnosis and Treatment of Human Diseases”. CUP: D53D23013410001

European Union-NextGenerationEU- Italian Ministry of University and Research (MUR), National Plan for Recovery and Resilience (PNRR) and Projects of National Relevance (PRIN), Project Code: P20225AEEE, Project Title: “Hybrid PET-MRI to simultaneously probe brain metabolism and cerebrovascular function in neurodegenerative diseases.” Funding call No. 1409 of 14.09.2022, Concession decree No. 1369 of 01.09.2023 adopted by MUR, ERC Panel LS7 “Prevention, Diagnosis and Treatment of Human Diseases”. CUP: D53D23021480001

European Union-NextGenerationEU- Italian Ministry of University and Research (MUR), National Plan for Recovery and Resilience (PNRR) and Projects of National Relevance (PRIN), Project Code: P2022ESHT4, Project Title: “Advancing MRI biomarkers of brain tissue microstructure and energetics in Multiple Sclerosis.” Funding call No. 1409 of 14.09.2022, Concession decree No. 1367 of 01.09.2023 adopted by MUR, ERC Panel LS5 “Neuroscience and Disorders of the Nervous System”. CUP: D53D23019210001

European Union – NextGenerationEU under the National Plan for Recovery and Resilience (PNRR), Mission 4 Component 2 – M4C2, Investment 1.5 – Call for tender No. 3277 of 30.12.2021 Italian Ministry of Universities Award Number: ECS00000041, Project Title: “VITALITY – Innovation, digitalization and sustainability for the diffused economy in Central Italy,” Concession Decree No. 1057 of 23.06.2022 adopted by the Italian Ministry of University and Research. CUP D73C22000840006.

European Union – NextGenerationEU under the National Plan for Recovery and Resilience (PNRR), Project Title: “MNESYS (PE0000006) – A Multiscale integrated approach to the study of the nervous system in health and disease,” Concession Decree No. 1553 of 11.10.2022 adopted by the Italian Ministry of University and Research.

EB has received funding from the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101066055—acronym HERMES. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible for them.

MG is supported by the Wellcome Trust [220575/Z/20/Z] and has received funding from the Engineering and Physical Sciences Research Council [EP/S025901/1]

In collaboration with Siemens Healthineers.

References

1. Hou, X., Liu, P., Li, Y., Jiang, D., Jill, B., Lin, Z., … & Lu, H. (2020). The association between BOLD-based cerebrovascular reactivity (CVR) and end-tidal CO2 in healthy subjects. Neuroimage, 207, 116365.

2. Sleight, E., Stringer, M. S., Marshall, I., Wardlaw, J. M., & Thrippleton, M. J. (2021). Cerebrovascular reactivity measurement using magnetic resonance imaging: a systematic review. Frontiers in physiology, 12, 643468.

3. Buxton, R. B. (2013). The physics of functional magnetic resonance imaging (fMRI). Reports on Progress in Physics, 76(9), 096601.

4. Diekhoff, S., Uludağ, K., Sparing, R., Tittgemeyer, M., Cavuşoğlu, M., von Cramon, D. Y., & Grefkes, C. (2011). Functional localization in the human brain: Gradient‐echo, spin‐echo, and arterial spin‐labeling fMRI compared with neuronavigated TMS. Human brain mapping, 32(3), 341-357.

5. Driver, I. D., Chiarelli, A. M., Chandler, H. L., Thomas, H., Manolova, S., Lu, H., … & Germuska, M. (2024). Breath-hold calibrated fMRI mapping of absolute cerebral metabolic rate of oxygen metabolism (CMRO2): an assessment of the accuracy and repeatability in a healthy adult population. Imaging Neuroscience, 2, 1-14.

6. Schmithorst, V. J., Hernandez-Garcia, L., Vannest, J., Rajagopal, A., Lee, G., & Holland, S. K. (2014). Optimized simultaneous ASL and BOLD functional imaging of the whole brain. J Magn Reson Imaging, 39(5), 1104–1117. https://doi.org/10.1002/jmri.24273

7. Tustison NJ, Cook PA, Holbrook AJ, Johnson HJ, Muschelli J, Devenyi GA, Duda JT, Das SR, Cullen NC, Gillen DL, Yassa MA, Stone JR, Gee JC, AvANTs BB. The ANTs X ecosystem for quantitative biological and medical imaging. Sci Rep. 2021 Apr 27;11(1):9068. doi: 10.1038/s41598-021-87564-6.

8. Zhang, Y., Brady, M., Smith, S., 2001. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20, 45–57. https://doi.org/10.1109/42.906424

9. J.L.R. Andersson, S. Skare, J. Ashburner. How to correct susceptibility distortions in spinecho echo-planar images: application to diffusion tensor imaging. NeuroImage, 20(2):870-888, 2003.

10. Greve DN, Fischl B. Accurate and robust brain image alignment using boundary-based registration. Neuroimage. 2009;48(1):63-72. doi:10.1016/j.neuroimage.2009.06.060

11. Jenkinson M, Smith S. A global optimisation method for robust affine registration of brain images. Med Image Anal. 2001;5(2):143-156. doi:10.1016/S1361-8415(01)00036-6

12. Jenkinson M. Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images. Neuroimage. 2002;17(2):825-841. doi:10.1016/S1053-8119(02)91132-8

13. Biondetti E, Chiarelli AM, Germuska M, Lipp I, Villani A, Caporale AS, Patitucci E, Murphy K, Tomassini V, Wise RG. Breath-hold BOLD fMRI without CO2 sampling enables estimation of venous cerebral blood volume: potential use in normalization of stimulusevoked BOLD fMRI data. Neuroimage. 2024 Jan;285:120492. doi: 10.1016/j.neuroimage.2023.120492. Epub 2023 Dec 7. PMID: 38070840.

14. Plewa, M.C., Sikora, S., Engoren, M., Tome, D., Thomas, J. and Deuster, A. (1995), Evaluation of Capnography in Nonintubated Emergency Department Patients with Respiratory Distress. Academic Emergency Medicine, 2: 901-908. https://doi.org/10.1111/j.1553-2712.1995.tb03106.x