Monday, September 5, 2016

respiration and movement: more experiences

Annika Linke, a postdoc at SDSU, contacted me, reporting that she's also seen oscillations in multiband acquisitions, with a Siemens Trio and Prisma, plus a GE Discovery. She kindly sent examples of her findings, and allowed me to share and describe a few of them here. More examples can be downloaded here and here.

Annika ran a multiband 8 sequence on a GE 3T Discovery MR750 scanner (UCSD CFMRI; TR = 800 msec, mb_factor 8, 32 channel head coil, 2 x 2 x 2 mm voxels; AP - PA encoding directions), and also saw prominent oscillations in the motion regressors, linked to respiration:

The subject was sleeping in this scan, and periodically stopped breathing. The movement in the motion regressors stopped and starts with the abnormal breathing periods, very similar to the traces from the purposeful breath-holding experiment we ran. I was also struck by the size of the oscillations in the movement regressors: somewhere between 0.5 and 1 mm, which neatly matches the size of the larger oscillations we've seen. Annika has results for an awake adult and toddlers, all of whom show oscillations (particularly in the A-P (y) and I-S (z) axes); see this pdf. These comparisons suggest the oscillation magnitude is not directly related to participant weight: the toddler and adult magnitudes are similar, though toddlers of course are smaller and have a faster respiration rate.

Here are some motion regressors Annika collected on a different scanner (Siemens 3T Prisma at Robarts Research Institute, Western University; CMRR release 10b, VD13D, 64 channel head coil, 3 x 3 x 3 mm voxels), at different MB factors. The oscillation is in all runs, though lowest amplitude with MB1.  

Finally, here's a set of motion regressors collected on a Trio at MB2 (ipat2 acceleration, 2 mm voxels, TR=1300 msec, AP/PA phase encoding directions, Robarts Research Institute, Western University). Each subplot is a run from a different person. All again have oscillations mostly in the y and z directions, though the magnitude is less than the MB8 plots above.

Annika's results make it clear that the magnitude of the oscillations in the motion regressors is not due to some weird fluke with our scanner, but rather some aspect of the multiband sequences (or some related factor, such as reconstruction, etc.); hopefully these additional examples will help us understand what's going on.

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.

multiband acquisition sequence testing: timecourses

This post is about timecourses found in our multiband acquisition sequence testing; see the previous post for the introduction and following for respiration recordings. The HCP MOTOR task is good for these tests since the expected effects are clear: left hand movement should cause strong activation in right M1, etc. The plots for one ROI are in this post; plots for hand and visual parcels in each hemisphere are in this pdf. Note also that averaging voxel timecourses within parcels somewhat corrects for the different voxel size (and presumably signal) associated with each acquisition: the number of voxels in each parcel for each acquisition is given in the plot titles (e.g., for parcel 3 below, there are 275 voxels at MB8, 180 at MB4, and 73 at MB0).

The black line in these plots is the average timecourse for Gordon parcel 3, part of the SMmouthL community, at our three test acquisitions (timecourses extracted from preprocessed images). This  parcel should be most responsive to the tongue-moving task (green blocks). Task-related activity is indeed apparent with all sequences, but superimposed oscillations are also apparent (which sort of look like the HCP data), particularly at MB8. By eye, it's harder to separate the task-related activation with the MB8 sequence, which is worrying for our event-related analyses.

Not wanting to visually mistake the difference in sampling frequency for a smoother signal, I resampled the MB8 timeseries to match the MB4 frequency, shown below (second section of the pdf). Downsampling does reduce the visual difference between the signals, but by eye, MB4 generally still has a clearer signal.

The magnitude of the oscillation varies with acquisition: greatest in MB8, then MB4, and smallest in MB0. The movement regressors (from the HCP pipelines for MB8 and MB4, from SPM for MB0) are shown in light colors on the plots (first column green, second blue, third pink). The oscillation in the activation timecourses looks to be clearly related to the oscillation in the movement lines, which in turn is related to respiration (examples of respiration recordings in the next post, as well as this previous post). An effect of respiration on BOLD is expected and known for a long time; the problem here is that the effect seems larger in magnitude, and perhaps higher in MB8 than MB4. By my eye, the magnitude of the oscillation doesn't appear totally consistent effect across the brain, making it potentially harder to model out post-processing; but this variation is simply my impression so far.

I'm happy to share volumes, etc. if any of you have ideas for additional analyses or want to explore further, and appreciate any thoughts. The preprocessing pipelines also generated surface versions of the files, which I am unlikely to be able to look at any time soon.

multiband acquisition sequence testing: introduction

In the previous couple of posts I shared some strange-looking motion regression curves, which turned out to be related to respiration. I also posted examples of average voxel timecourses which I've been looking at to get a feel for the task-related signal in these images, and how it might be impacted by the respiration-related oscillations. Task-related signal is of primary importance to us (e.g., MVPA and FIR analyses), and ideally analysis of single events. So, everything here is through that lens; implications for resting state, functional connectivity analyses, etc. are likely very different, and I'm not going to speculate on that at all.

We've done a lot of analyses this week, and people have been kind enough to share some of their data and thoughts. This is a lot of material and fairly preliminary, but I think will be of great interest to people using multiband sequences and/or HCP data, so I'll put a few posts together, rather than trying to fit everything into one.

Last Sunday we ran a series of test scans, using as participant the lab member with the most prominent oscillations (examples of which are shown here), and the HCP MOTOR task. This task is short (just over 3.5 minutes long), and has blocks of right or left hand movement, right or left foot movement, and tongue movement. I showed example timecourses from the HCP in the previous post; the graphs from our test runs (see this post) are the same, except that I color-coded the task blocks by body type for easier visibility.

We ran three main test sequences, plus a few variations. Acquisition details are below the fold. The three main sequences were:
  • MB8: The same multiband 8 sequence we've been using (described here), except with LeakBlock enabled. TR = 800 msec, acquired voxels 2 x 2 x 2 mm.
  • MB4: A new (for us) multiband 4 sequence. TR = 1200 msec, acquired voxels 2.4 x 2.4 x 2.4 mm.
  • MB0: A control, non-multiband sequence, the ep2d_bold built-in scanner sequence.TR = 2500 msec, acquired voxels 4 x 4 x 4 mm.
Preprocessing was through the HCP pipelines for the multiband sequences, through SPM12 for the control (realignment and spatial normalization; voxels resampled to 3x3x3); motion regressors plotted (here) are as generated by those programs. I generated voxel timecourses as described before, using afni and the Gordon parcellation for ROIs. We have respiration belt recordings for the multiband sequences, but not for the control.

The functional images we collected with the multiband acquisitions are beautiful. The screenshot above shows a frame from an AP run of each type (the second left-hand movement block, to be precise), before (top) and after (bottom) preprocessing. The MB8 functional images practically look like anatomical images, there's so much detail.

The next few posts will get into the respiration and activity timecourses we found in each.

acquisition details for MB4 and MB0 below the jump; see the previous post for MB8.

Friday, August 26, 2016

more motion and activation plotting

After my initial "what is this?" post I've become convinced that the oscillations are due to respiration, both from my own data and others' reports. I'll leave it to the physicists to understand why the patterns are especially prominent in these sequences, but I've been trying to understand their impact for our work: we're collecting task data and want to do event-related and FIR analyses. Is the oscillation something superimposed on (but independent of) our real signal such that we can "ignore" or process it out (maybe with signal separation techniques such FIX ICA) after image collection, or is our event-related BOLD getting lost, and we should change the acquisition parameters?

These images show some of the plots I've been playing with to try evaluating those questions, using HCP data for comparison. The x-axis is frame number, with thick vertical grey lines marking one-minute intervals and thin vertical grey lines marking 30-second intervals. The left-side y-axis is translation in mm (from the Movement_Regressors.txt file), while the right-side y-axis is the average BOLD of the voxels in each parcel (which I generated via afni; details below; these are the minimally preprocessed images). Note that the left y-axis (translation) is fixed in all graphs while the right-side axis (activation) varies.

The first three columns of Movement_Regressors.txt are plotted in light green, light blue, and light red (in that order), with the average activation overlaid in black (right-side axis). The dark blue line that tracks the activation is a lowess smooth. I generated these plots for four HCP people, in each of four Gordon parcels (4, 41, 45, 201), and for six task runs (MOTOR, GAMBLING, and EMOTION, RL and LR of each). I chose those task runs because they all involve hand motion, blocked (MOTOR) or as event responses (GAMBLING and EMOTION). The block or button-push time is shown in the graphs as little tick marks along the x-axis, with R and L marking the right and left-hand block onsets for MOTOR. I picked four parcels of similar size; 45 is in the left-hemisphere and 201 in the right-hemisphere SMhand communities, and should show strong hand-motion-related activation.

Plots for all four people, runs, and parcels are in this pdf, with two sets copied in here. These are plots for the four parcels, the MOTOR_RL run only. The purple lines show when we expect hand-motion task activation to be strongest for parcels 45 and 201, and it is, particularly in the smoothed line (good, signal!). The oscillation is also clearly visible, and related (by my eye, at least) to the pattern in the motion regressors. There are also spikes, some of which I marked with red dots. It's a bit hard to see in these plots, but these spikes strike me as aligned to jags in the motion regressor lines (and presumably, respiration).

Looking at a lot of these plots makes me understand why I've had trouble (in the other dataset as well) with single-TR and short temporal-compression analyses (e.g., averaging 4 or 5 TRs after an event): I'm picking up too much of the oscillation. Signal clearly is present here, but trying to detect it for single (or short) events is proving tricky.

(example afni command after the jump)

Tuesday, August 16, 2016

that's motion? respiration

In the previous post I showed some motion regressors with very regular oscillations. Several of you pointed towards respiration, and I was able today to extract the signal from a respiration belt for a couple of runs. The respiration signal (black line plots) is not perfectly temporally aligned to the movement regressors, but close enough to convince me that respiration is driving the oscillation. We're still looking into various acquisition details for why it is so prominent in this dataset; I am certainly not an MR physicist, but will summarize when we figure something out.

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.