Education, tips and tricks to help you conduct better fMRI experiments.
Sure, you can try to fix it during data processing, but you're usually better off fixing the acquisition!

Sunday, January 13, 2019

Arterial carbon dioxide as an endogenous "contrast agent" for blood flow imaging

I nearly called this post Low Frequency Oscillations - part III since it closely follows the subject material I covered in the last two posts. But this is a slight tangent. Following the maxim "One scientist's noise is another scientist's signal," in this post I want to look at the utility of systemic LFO to map blood flow dynamics, an idea that was suggested in 2013 by Lv et al. based on the earlier work from Tong & Frederick that I reviewed last post. There is also at least one review of this topic, from 2017.

Let me first recap the last post. There is sufficient evidence, supported by multiple direct and indirect lines of inquiry, to suggest a blood-borne contrast mechanism that produces a prominent fluctuation at around 0.1 Hz in resting-state fMRI data. (Here, I assume a standard T₂*-weighted EPI acquisition for the resting-state fMRI data.) Furthermore, the same fluctuation can be found anywhere in the body. That is, the fluctuation is truly systemic. The best explanation to date is that non-stationary arterial CO₂ concentration, brought about by variations in breathing rate and/or depth, produces changes in arterial tone by virtue of the sensitivity of smooth muscle walls to the CO₂ dissolved in arterial blood. I shall assume such a mechanism throughout this post, while noting that the actual mechanism is less critical here than whether there is some utility to be exploited.

In the title I put "contrast agent" in quotes. That's because the CO₂ isn't the actual contrast agent, but a modulator of contrast changes. When the smooth muscle walls of an artery sense a changing CO₂ concentration, they either expand or contract locally, modulating the blood flow through that vessel. In the brain, a change in a blood vessel's diameter causes a concomitant change cerebral blood volume (CBV), hence cerebral blood flow (CBF). There may be a local change in magnetic susceptibility corresponding to the altered CBV in the arteries and capillaries. But the altered CBF will definitely produce the well-known change in magnetic susceptibility in and around the venous blood that can be detected downstream of the tissue, i.e. the standard BOLD effect. The actual contrast we detect is by virtue of changes in T₂* (for gradient echo EPI), plus the possibility of some flow weighting of the arterial blood depending on the combination of flip angle (FA) and repetition time (TR) being used. As a shorthand, however, I shall refer to arterial CO₂ as the endogenous contrast agent because whenever an artery senses a change in CO₂ concentration, there will be a concomitant change in vessel tone, and we will see a cascade of signal changes arising from it. (See Note 1 for some fun with acronyms!)

Time shift analysis

Most published studies attempting to exploit systemic LFO have used fixed time shifts, or lags, in their analysis. You just need a few minutes' worth of BOLD fMRI data, usually resting state (task-free). The analysis is then conceptually straightforward:
  1. Define a reference, or "seed," time course;
  2. Perform cross correlations between the "seed" and the time course of each voxel, using a set of time shifts that typically spans a range of 15-20 seconds (based on the expected brain hemodynamics);
  3. Determine for each voxel which time shift gives the largest cross correlation value, and plot that value (the delay, in seconds) to produce a lag map.

There are experimental variables, naturally. The duration of the BOLD time series varies, but most studies to date have used the 5-8 min acquisition that's common for resting-state connectivity. Some studies filter the data before starting the analysis. Different studies also tend to choose different seeds. There are pros and cons for each seed category that I assess in the next section. Time shifts are usually increments of TR, e.g. the lag range might be over +/- 5 TRs for a common TR of 2 sec. And, in producing the final lag maps, some studies apply acceptance criteria to reject low correlations.

Let's look at an example time shift analysis, from Siegel et al. (2016). The raw data were filtered with a pass-band of 0.009 - 0.09 Hz. For cross correlations, they used as their seed time course the global gray matter (GM) signal. Cross correlations were computed voxel-by-voxel for nine delays of TR = 2 sec increments, covering +/- 8 sec, followed by interpolation over the lag range. The time shift corresponding to the maximum cross correlation was assigned that voxel's lag value in the final map, as shown here:

Fig. 1 from Siegel et al. (2016).