Friday, September 2, 2016

multiband acquisition sequence testing: respiration

This post is about the relationship between respiration and the movement regressors that we've found in our multiband acquisition sequence testing; see this post for an introduction; this post for examples of activation timecourses, and this post for examples from different people and tasks.

During our acquisition testing session, our participant did breath-holding for one set of MB8 runs, instead of the hand/foot/tongue movements, using the task cues to pace his breath-holding periods.

The light blue line is the respiration recording from a chest belt, with the six columns of the movement regressors overlaid, and the colored blocks at the bottom indicating task cues (as in the plots in the previous posts). Note that the time alignment is not totally perfect for the respiration trace: our system records respiration longer than the fMRI run, and I can't (yet) extract the run onset and offset signals. I aligned the traces using the breath-holding periods, but won't guarantee the timing is to-the-second perfect.

Regardless, the breath-holding periods are clear, and it's also clear that the oscillations in the movement regressors are related to the breathing. The start and stop of motion is also visible in a movie of the raw (before preprocessing) image, which can be seen here: the brain "jitters", then stops, then jitters, then stops (the movie is of the PA MOTORbreath run).

Here are two traces from the MOTOR task runs; plots for the rest of the runs can be downloaded here. The oscillations in the movement regressors is clearly closely linked to the respiration.

Interestingly, the biggest oscillation in the movement regressors here is split between several columns (dark blue, medium blue, light red), where before it was confined to one column (same participant as here); perhaps the head position or packing varied a bit?

Again, it's not new to note that fMRI is affected by breathing. What does seem different is the magnitude of the effect: these scans seem more affected, both in the motion regressors and (more importantly for us, the BOLD). For example, this last set of images shows the movement regressors for the MB0 (no multiband) runs from or test session, and a person from a different MB0 dataset ("BSF117"; a different scanner). A few blips are visible, but smaller. The MB8 and BSF117 examples below were downsampled to match the MB0 TR; note that these are the same MB8 movement regressors as above: after downsampling the oscillations no longer tightly match the respiration, but are still more prominent than the others.


  1. Excellent stuff, Jo. For those following along at home, we're now looking at the particular aspects of the MB acquisition that may make it more sensitive to respiratory frequency shifts than conventional EPI. One possibility is/was the transmitter (Tx) frequency feedback, but tests on a Skyra scanner suggest this isn't the cause. The magnitude of oscillations was similar with the Tx freq feedback feature disabled (a new option for R014 of MB-EPI from CMRR).

    So now we're looking at the particulars of axial MB-EPI. For instance, the SMS method uses blipped CAIPI (, which comprises small gradient episodes added along the slice direction during the EPI readout. For axial slices, the slice direction and hence the orientation of the CAIPI blips is along Z, the magnet axis. This axis will likely exhibit the largest spread of respiration-induced frequency offsets.

    Imminent tests will compare, for example, sagittal MB-EPI to axial MB-EPI. I may also try different blipped CAIPI factors in axial MB-EPI, other parameters constant, to try to change the magnitude of oscillations. Keep up with Jo's blog for more information soon!

  2. Another possibility for those wanting to run their own tests: the flip angle. The Human Connectome Project used a TR of 720 ms and FA of 52 deg, to approximate the Ernst angle (max. SNR per unit time) for the T1 of gray matter. Similarly, Russ Poldrack used TR of 1160 ms and FA of 63 deg in his My Connectome project ( In my tests I've used much lower FA. I recently tested several MB factors on my Trio and the fluctuations observed were smaller than others are seeing. There also didn't seem to be much of a dependence on MB. However, I used TR=1200 ms and FA=35 deg throughout. Why such a low FA? Partly to ensure the heating (SAR) remains low, even with high MB factors, but mostly because there is a lot of evidence suggesting that BOLD sensitivity is independent of FA whereas physiologic noise, and presumably motion sensitivity, scale with FA (up to the Ernst angle).

    I've mentioned the work of Bandettini's group before on my blog, for example in an intro post to MB-EPI:

    Here's the pertinent section from the blog:

    "Flip angle: The HCP used an approximate Ernst angle for their TR; 52 degrees for TR = 720 ms. I tend to use a lower FA than the Ernst angle for two reasons. First, as shown in this paper from Gonzalez-Castillo et al. (doi:10.1016/j.neuroimage.2012.10.076), reducing the FA can reduce physiologic noise without reducing BOLD sensitivity. (Years ago on a 4 T we always used a very low FA, such as 20 degrees for a TR of 1-2 seconds, in order to reduce the inflow effects. It worked very well.) The second reason might be less of an option. The amount of subject heating (assessed by the specific absorption rate, SAR) scales as the square of the FA. So even a small reduction in FA can reduce SAR considerably. If you find yourself running into the SAR limit then you can reduce the FA until you're good to go. The down side to a lower than Ernst angle FA? Image contrast may be affected slightly, but since we prefer temporal stability over anatomical content then altered contrast shouldn't be a huge burden. (For Gonzalez-Castillo et al. it was an advantage because they were using water-excite rather than fat presaturation on a GE scanner and so they had low anatomical contrast at the Ernst angle.)"

    If, as I suspect, everyone is using relatively high FA as their TR reduces with MB factor, then what looks like an MB dependence may actually be an FA,TR dependence.