Target Audience

Researchers and their trainees who are interested in studying the physiological substrates of brain function using functional MRI methods, and those who are interested in exploring emerging, non-conventional fMRI methods.

Learning Objectives

Attendees should expect to learn about the advantage and main disadvantages of the BOLD technique, as well as the physical principles behind various non-BOLD techniques that aim to more directly measure physiological phenomena that give rise to the BOLD effect. Attendees will also learn about the advantages and caveats of these methods that may boost or limit their application in task-based as well as resting-state fMRI.

Background & Methods

The BOLD signal is a relative rather than quantitative measure of brain function that depends on the interplay of perfusion and oxygenation, leaving room for ambiguous interpretation. The predominant non-BOLD fMRI methods are based on endogenous contrast generated through blood water 1, and was used in the first CBF-based fMRI experiment 2. Dynamic changes in CBF in response to vascular stimuli or functional tasks can be derived from the time-series arterial-spin labeling (ASL) images without the quantification step 3,4. Though related to CBF, CBV offers unique diagnostic information 5, and dynamic CBV is particular useful for isolating vascular from metabolic contributions to the BOLD fMRI signal 6. The well-known power-law relationship first reported by Grubb et al 7 only relates CBF and CBV in their static than dynamic states. To date, three approaches to non-invasive dynamic CBV monitoring for fMRI have been introduced. The first, introduced by Lu et al., attempts to isolate blood from tissue based on blood-tissue longitudinal relaxation differences and has been termed vascular space occupancy (VASO) 8. This method has since been extended for mapping quantitative CBV 9. Other methods, including MOTIVE 10 and VERVE 11, sought to isolate arterial and venous CBV changes, respectively. In terms of CMRO2 mapping, a wealth of research has centred around calibrated BOLD fMRI 12,13. The first instance of calibrated fMRI called for the use of hypercapnia. An alternative calibration approach is through hyperoxia, assuming that O2 modulation does not induce vascular changes 14. However, both sets of assumptions may not hold under all circumstances. Thus, both hypercapnia and hyperoxia have been included in a comprehensive calibration model 15, with the advantage of relaxing the assumptions of both earlier models. Alternatively, gas-free calibration has also been proposed, based on the estimation of baseline tissue oxygenation via T2 and T2* 16,17. That is, T2’ is proportional to CMRO2 divided by CBF×CBV. The major limitation, however, is that a calibration factor that relates the T2* (hence T2’) value to the M value needs to be estimated, and this calibration factor is sequence dependent. One of the alternatives to calibrated fMRI is a technique termed quantitative BOLD, or “qBOLD” 18, which has been proposed for OEF estimation based on the interplay between T2 and T2* decay. The main challenges in broadening qBOLD’s application is the need to fit sometimes-noisy data to a multi-parametric model. Another novel approach, “quantitative imaging of extraction of oxygen and tissue consumption (QUIXOTIC) 19, uses venular-targeted velocity-selective spin labeling to isolate venous blood, followed by rapid refocusing to induce T2 sensitivity in the venous signal. The T2 is in turn related to venous oxygenation, which, in combination with CBF measurements, can be used to derive voxel-wise quantitative CMRO2. The main drawbacks of QUIXOTIC include the need to select a cutoff flow velocity for targeting venular blood flow as well as the long scan time. Yet another alternative to calibrated fMRI is susceptibility-weighted imaging (SWI). The phase images generated by SWI can be used to estimate OEF changes in response to stimuli or challenges 21. Quantitative susceptibility mapping (QSM), which does not require gas challenges and is based on the linear dependence of magnetic susceptibility on dHb concentration 22. QSM has been used to quantify blood oxygenation in veins 23,24 and brain tissues 25. More specifically, maps of task-induced CMRO2 and OEF changes can be computed using QSM and ASL data 26. The main challenges facing QSM include the need to solve an ill-posed inverse problem as well as the dependence of QSM estimates on slice orientation. In terms of connectivity mapping, the ability to robustly capture spontaneous CBF fluctuations using ASL has opened up the enticing possibility of mapping CBF-based resting-state FC. Thus far, efforts in this direction have been successful, but have also raised issues in signal preprocessing and interpretation. For instance, due to the relatively low temporal resolution of ASL scans, perfusion time series from multiple subjects are often concatenated. In addition, as CBF and BOLD provide complementary information, it is appealing to combine ASL and BOLD for characterizing spatiotemporal and quantitative properties of specific brain networks 27. Furthermore, joint CBF- and BOLD-based connectivity analyses offer mutual validation of findings using each modality, thereby improving confidence for statistical inferences. On the other hand, cross-modality differences in connectivity maps also drive the search for the neurovascular mechanisms underlying resting-state connectivity.

Discussion

While a comprehensive view of brain physiology is critical for understanding disease processes, every imaging technique discussed in this talk is associated with specific assumptions. For instance, whether using BOLD or perfusion-based fMRI methods, the measurement of neural response depends on intact neurovascular coupling. Awareness of these assumptions will be critical when interpreting data from patient populations.

Acknowledgements

Some of the presented work was supported by the Canadian Institutes of Health Research.

References

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