Friday, August 12, 2016

that's motion?

While my blogging has unfortunately been sparse lately, I've still been doing lots of fMRI analysis and MVPA! One project I'm currently involved with is just starting to collect high spatial (voxels acquired at 2x2x2 mm) and temporal (TR=800 msec) resolution task fMRI data using a simultaneous multi-slice (SMS) EPI on a Siemens Prisma 3T scanner (details below the fold). We've been looking closely at participant motion and signal quality, and trying various control analyses (button pushes detected in motor areas, task vs. rest, etc.).

UPDATE: This pattern is related to respiration.

Some of the participants have an oscillation in the movement regressors, and I would love to get your impressions of the pattern. Below are two runs (encoding directions AP and PA) of a task for the participant with the most striking oscillation. Plotted are the six columns (translation in blue, rotation in pink) generated by our implementation of the HCP processing pipelines (the Movement_Regressors.txt file, to be precise; MCFLIRT produces a nearly identical set of values). Acquisition frames are along the x-axis, with vertical lines marking 1 minute intervals (these runs are a bit more than 12 minutes long), and short tick marks indicating event onsets (the events are in three blocks).

The overall motion is quite small: less than a mm over each run, well under the 2 mm isotropic voxel size. But the second column (blue below) has a very clear oscillation, that strikes me as too regular to be physiological in origin. Below are the same values again, but just the translation columns, zoomed-in.
This movement is not a simple bug in the preprocessing, but is visible in the raw (converted to NIfTI and defaced, but not motion-corrected, spatially normalized, or anything else) image (click here to see a movie of 08's Pro1 run). It's also in the voxel intensities. The graph below has the same column 2 translation values as the Pro1 graph above, with the average frame-to-frame intensity of a 1900-voxel box-shaped ROI I put in the frontal lobe superimposed in pink. The two curves clearly track pretty well.

I've never seen movement like this in non-SMS EPI datasets, and its regularity makes me suspect that it's related to the acquisition somehow. I'm certainly not a physicist, so very much would appreciate any insights, or if you've encountered similar movement.

The person whose images are shown in this post has the largest and most regular oscillation of any person we've scanned yet (around 8 people); check below the fold for a few more examples, along with details of the acquisition sequence.

The oscillations don't appear identical in all people, nor in all runs within a person. Below are images just showing the first three minutes of each run in the same person as above (08 Pro1), plus three others from our study. The second image shows the Movement_Regressors.txt file values for four of the HCP people (picked at random). Small oscillations are sometimes visible there as well, but in the green line - the first translation column. Could it be related to the difference in encoding directions (AP/PA for our study, RL/LR for the HCP)?

scanning details

Acquisition type 2D
Scan sequence EP
Sequence variant SK\SS
Scan options FS\EXT
Field Strength 3.0
Voxel resolution. 2.0, 2.0, 2.0
FOV 936 x 936
Orientation Tra
Subject position HFS
TR 800.0
TE 37.0
Flip 52
Echo Spacing (sec) 5.80008723331199E-4
Readout sample spacing 2100.0
Pixel bandwidth 2290.0
PhaseEncoding direction positve 0
In-plane phase encoding direction COL
In-plane phase encoding rotation -0.142593399888
Siemens Orientation Text Tra>Cor(-17.7)>Sag(5.0)
Siemens Table Position 0\0\0
Siemens GRADSPEC lOffset \\
Siemens GRADSPEC alShimCurrent 625\117\159\102\-44
HCP MB Recon Location 1
Siemens Mosaic Slice Count 72
Siemens iPAT factor 1
Siemens txRefAmp 259.23236084
Siemens Coil String HEA;HEP
Siemens sWipMemBlock.alFree[0] 265
Siemens sWipMemBlock.alFree[9] 3
Siemens sWipMemBlock.alFree[10] 9
Siemens sWipMemBlock.alFree[13] 8
Siemens sWipMemBlock.alFree[16] 1
Siemens sWipMemBlock.alFree[21] 40000
Siemens sSliceArray.asSlice[0].dThickness 2.0
Siemens sSliceArray.asSlice[0].dPhaseFOV 208.0
Siemens sSliceArray.asSlice[0].dReadoutFOV 208.0
Siemens sSliceArray.asSlice[0].dInPlaneRot -0.142593399888
Siemens sSliceArray.asSlice[0].sPosition.dSag 4.8157943446
Siemens sSliceArray.asSlice[0].sPosition.dCor -27.0112668928
Siemens sSliceArray.asSlice[0].sPosition.dTra -56.9037942277
Siemens sSliceArray.asSlice[0].sNormal.dSag -0.0871001188
Siemens sSliceArray.asSlice[0].sNormal.dCor 0.3032292559
Siemens sSliceArray.asSlice[0].sNormal.dTra 0.9489286526


  1. Do you have pulse ox data? With such a fast tr you may be looking at cardio effects. Participants will differ in how much they show this because resting HR differs, as does HR variability over the run.

  2. What is the frequency spectrum of the artifact? Heart rate is ~1Hz, resp ~0.3Hz.

  3. It looks like the frequency is closer to respiration than heart rate. We recorded respiration with a belt, but haven't been able to get the signals out yet; hopefully very soon. The current best guess is that the respiration isn't causing head motion, but rather the changing volume of air in lungs is the problem. Hopefully I'll have more information soon.

    1. Colleen Mills-FinnertyAugust 25, 2016 at 5:05 PM

      I am commenting with interest having just collected task based multiband imaging with a larger voxel, but smaller TR (400ms). I have seen a somewhat similar pattern informally on our real-time motion tracking plots. We also have respiration and pulseox for some of our people but if you figure out where the oscillatory pattern is coming from and how best to visualize it I'd be very interested in hearing more.

  4. > The person whose images are shown in this post has the largest and most regular oscillation

    It would be interesting to know/check if this participant has some sort of asthma.

    1. 08 is actually a lab member (piloting), so I know he doesn't have asthma, and is a typical physical size. He's probably had more time in scanners and knows the tasks better than anyone else, which might have been a factor.