Friday, January 11, 2019
comparing fMRIPrep and HCP Pipelines: parcelwise GLM statistics
As explained in the previous post (the introduction is here) with the target knots and contrasts for each task defined, and with the Schaefer parcellation to identify comparable sets of voxels and vertices, we're now able to see if there's a difference in the GLM between the four preprocessing combinations (surface, volume, fMRIPrep, HCP pipelines).
In the previous post I showed some of the group-average TENT curves. Above are the coefficients going into those curves, but for all 13 test people - the four green dots for AX-CPT parcel 90 above are (almost) the same means as the four lines at knot 4 (shaded) in the previous post ("almost" since the green dots here are normal means, while the previous ones were robust; the individual participant's coefficients are the same). A unique plotting symbol is used for each participant, and the four columns of points at each parcel are the four preprocessings. Plots with the distributions for all 400 parcels are here (source here).
For a simple way to describe the distribution at each parcel I used t-tests: is the across-subjects mean different than zero? These plots have each parcel colored by t, HCP in the first row, then fMRIPrep, then their difference. The differences are HCP - fMRIPrep, so cool colors indicate parcels with larger ts when preprocessed with fMRIPrep. There are a lot of cool colors in the difference images, but it's still a lot of data to absorb; the next (final?) post in this series will summarize these statistics for just 20 of these parcels.
comparing fMRIPrep and HCP Pipelines: GLM TENT curves
As described in the introduction
to this series of posts, the primary goal of these analyses is to
compare the GLM estimates: is there a difference in the high vs. low
cognitive control statistics produced from the different preprocessing
pipelines (HCP, fMRIPrep) and spaces (volume, surface)? The previous step of the analysis confirmed that the whole-brain GLMs produced sensible results, so here I'll start showing the statistics averaged within parcels. As noted before, averaging the statistics within Schaefer parcels is especially useful here, since they are defined in all three results spaces (MNI volume; surface fsaverage5, surface HCP). We've been using the 400-parcel resolution in our lab, so I adopted it here as well; I haven't tried with other resolutions.
Below is a detailed explanation of how to read these figures. They're complex, but the best way I've found (so far!) to sensibly compare sets of GLM results for this dataset. Surface-derived curves are shown in the top pair of figures (with the surface brains), and volume-derived in the lower (with the volume brains). These are group (over the 13 test people) estimates for two informative parcels (90 and 91 in the left hemisphere). Files with all parcels can be downloaded here; this zip contains the compiled pdfs (that I took these images from) as well as the source knitr (.rnw).
There are five panes in each row. The first has a picture of the parcel and its name, surface for surface statistics (though I need to fix the text label spacing) and volume for volume statistics. The bar graphs by the parcel image show the sustained (block) regressors (robust mean and SEM) for each of the four tasks, in alphabetical order (same as the line graphs). Aside: the volume bar graph has space for "P" and "R" - these are the proactive and reactive DMCC sessions. I adapted this knitr template from one that included all three sessions; please ignore the empty spaces and "PRO" "REA" labels (fMRIPrep preprocessing was only run on Baseline session images).
The other four panes show the event-related part of the model for each task; the two regressors subtracted to make the high-low cognitive control contrast for each task are listed in the title after the task name. In the curve figures, the thick line at each knot (x-axis) is the across-subjects (robust) mean coefficient difference; the thin lines are the (robust) SEMs. Trimming of 0.1 was used in both cases; trimse from the WRS2 package was used for the SEM and base R for the trimmed mean. The solid line is calculated from the HCP-preprocessed images, dashed with fMRIPrep (abbreviated fp). The small bars below each set of lines gives the difference at the corresponding knot; HCP-fMRIPrep (so if the HCP-derived mean coefficient is larger the difference will be positive and the little bar will be above the line, on the side marked with "hcp").
The number of knots (x-axis) in each graph varies, since the tasks vary in duration: Sternberg trials are longest, Stroop trials are shortest. Each knot is 2 TRs (2.4 ms) in all tasks. The Stroop curve looks a bit HRF-ish; since each trial is just a few seconds, as is reasonable. The others are a bit delayed or doubled (in the case of Sternberg) due to the trial dynamics; there's lots more detail on the DMCC website, if you're curious.
For the purpose of the preprocessing comparisons, it's most relevant that we identified one knot for each task that should be most relevant for these high vs. low cognitive control contrasts, which is shaded in grey on the plots. Focusing on a single knot in each task gives a way to summarize the curves - we're not equally interested in all knots' estimates - which will be shown in the next post. Note that which knots are "most relevant" and which contrasts (and GLMs) would be used for high vs. low was determined before these preprocessing comparisons were conceived, primarily on the basis of task design, but also checking against the volumetric HCP-preprocessed GLM results.
I described these two parcels (90 and 91) as "informative" because in all four cases (surface, volume, HCP, fMRIPrep) the TENT curves have a fairly sensible shape (given the task timing) in all tasks, with a positive (high > low cognitive control) mean at the target knot (shaded). These two parcels are also located in areas that are sensible for having more activity in high cognitive control conditions. In the next post I'll show whole-brain results again, but summarized within parcel and at the target knot.
Below is a detailed explanation of how to read these figures. They're complex, but the best way I've found (so far!) to sensibly compare sets of GLM results for this dataset. Surface-derived curves are shown in the top pair of figures (with the surface brains), and volume-derived in the lower (with the volume brains). These are group (over the 13 test people) estimates for two informative parcels (90 and 91 in the left hemisphere). Files with all parcels can be downloaded here; this zip contains the compiled pdfs (that I took these images from) as well as the source knitr (.rnw).
The other four panes show the event-related part of the model for each task; the two regressors subtracted to make the high-low cognitive control contrast for each task are listed in the title after the task name. In the curve figures, the thick line at each knot (x-axis) is the across-subjects (robust) mean coefficient difference; the thin lines are the (robust) SEMs. Trimming of 0.1 was used in both cases; trimse from the WRS2 package was used for the SEM and base R for the trimmed mean. The solid line is calculated from the HCP-preprocessed images, dashed with fMRIPrep (abbreviated fp). The small bars below each set of lines gives the difference at the corresponding knot; HCP-fMRIPrep (so if the HCP-derived mean coefficient is larger the difference will be positive and the little bar will be above the line, on the side marked with "hcp").
The number of knots (x-axis) in each graph varies, since the tasks vary in duration: Sternberg trials are longest, Stroop trials are shortest. Each knot is 2 TRs (2.4 ms) in all tasks. The Stroop curve looks a bit HRF-ish; since each trial is just a few seconds, as is reasonable. The others are a bit delayed or doubled (in the case of Sternberg) due to the trial dynamics; there's lots more detail on the DMCC website, if you're curious.
For the purpose of the preprocessing comparisons, it's most relevant that we identified one knot for each task that should be most relevant for these high vs. low cognitive control contrasts, which is shaded in grey on the plots. Focusing on a single knot in each task gives a way to summarize the curves - we're not equally interested in all knots' estimates - which will be shown in the next post. Note that which knots are "most relevant" and which contrasts (and GLMs) would be used for high vs. low was determined before these preprocessing comparisons were conceived, primarily on the basis of task design, but also checking against the volumetric HCP-preprocessed GLM results.
I described these two parcels (90 and 91) as "informative" because in all four cases (surface, volume, HCP, fMRIPrep) the TENT curves have a fairly sensible shape (given the task timing) in all tasks, with a positive (high > low cognitive control) mean at the target knot (shaded). These two parcels are also located in areas that are sensible for having more activity in high cognitive control conditions. In the next post I'll show whole-brain results again, but summarized within parcel and at the target knot.
comparing fMRIPrep and HCP Pipelines: full-brain GLM images
As described in the introduction to this series of posts, the primary goal of these analyses is to compare the GLM estimates: is there a difference in the high vs. low cognitive control statistics produced from the different preprocessing pipelines (HCP, fMRIPrep) and spaces (volume, surface)? As explained in the introductory post, I've found comparing the statistics within Schaefer parcels most useful, since these are defined in all four results spaces.
But the parcel-averaged statistics were calculated from mass-univariate (every voxel and vertex separately) GLMs, and it's dangerous to make parcel averages without first looking at some of the full-brain GLM results to make sure everything seems sensible. So, here I'll show a few single-subject GLM results, before moving on to group and parcel-averaged in the next post(s).
The "buttons" GLMs are not of direct interest - they only model the button presses, not the cognitive tasks. But they're useful for a positive control: activity should be in motor areas and possibly visual (given the linking of response and visual change in some of our tasks). Below are the "button1" F statistics from the Buttons GLM for the same participant used in the previous posts. (Since these are TENT models, the Fstat provides a single summary of activity across the set of knots.)
First, the volume results. Since they're both in the same output space I can subtract the statistics voxel-wise, for the third row of images for each task. I subtracted HCP-fMRIPrep, so cool colors are voxels where the fMRIPrep-volume-preprocessed statistic was larger than the HCP-volume-preprocessed. As expected, the peaks are generally in grey matter, and pretty similar between the two pipelines: the two AX-CPT images look more similar to each other than do the HCP-preprocessed AX-CPT with the HCP-preprocessed Cued task-switching image. This is very good: I expect some variation between the three tasks (though all should have some motor), but the two pipelines ought to make similar statistics, given that they started with the same DICOMs, event timings, etc. While the two pipelines' images are visually similar, the difference images have quite a bit of blue in sensible brain areas, indicating that the fMRIPrep-preprocessed images had larger Fs in those voxels.
And here are the same statistics for the HCP and fMRIPrep-preprocessed surfaces. I couldn't subtract the two surfaces, since the two pipelines use different surface targets (so there isn't vertex-to-vertex correspondence; there are about 3x as many vertices in the HCP surface than fsaverage5). The inflation of the two underlays doesn't match exactly, but it's possible to tell that the statistics sort much more by task than preprocessing, which, as noted before, is very reassuring. There's also clear motor activity in all images, less in AX-CPT than the others, consistent with what is seen in the volume images for this person.
This person is quite typical, as are the conclusions drawn from this button1 F statistic: the full-brain statistical images tend to sort much more by statistic (which contrast, which task) than by preprocessing. But there are differences in the statistic magnitudes and cluster boundaries, and these are hard to evaluate qualitatively, particularly on the surfaces. Averaging the statistics within parcels makes it possible to quantify these differences, and so directly compare the four sets of results.
But the parcel-averaged statistics were calculated from mass-univariate (every voxel and vertex separately) GLMs, and it's dangerous to make parcel averages without first looking at some of the full-brain GLM results to make sure everything seems sensible. So, here I'll show a few single-subject GLM results, before moving on to group and parcel-averaged in the next post(s).
The "buttons" GLMs are not of direct interest - they only model the button presses, not the cognitive tasks. But they're useful for a positive control: activity should be in motor areas and possibly visual (given the linking of response and visual change in some of our tasks). Below are the "button1" F statistics from the Buttons GLM for the same participant used in the previous posts. (Since these are TENT models, the Fstat provides a single summary of activity across the set of knots.)
First, the volume results. Since they're both in the same output space I can subtract the statistics voxel-wise, for the third row of images for each task. I subtracted HCP-fMRIPrep, so cool colors are voxels where the fMRIPrep-volume-preprocessed statistic was larger than the HCP-volume-preprocessed. As expected, the peaks are generally in grey matter, and pretty similar between the two pipelines: the two AX-CPT images look more similar to each other than do the HCP-preprocessed AX-CPT with the HCP-preprocessed Cued task-switching image. This is very good: I expect some variation between the three tasks (though all should have some motor), but the two pipelines ought to make similar statistics, given that they started with the same DICOMs, event timings, etc. While the two pipelines' images are visually similar, the difference images have quite a bit of blue in sensible brain areas, indicating that the fMRIPrep-preprocessed images had larger Fs in those voxels.
This person is quite typical, as are the conclusions drawn from this button1 F statistic: the full-brain statistical images tend to sort much more by statistic (which contrast, which task) than by preprocessing. But there are differences in the statistic magnitudes and cluster boundaries, and these are hard to evaluate qualitatively, particularly on the surfaces. Averaging the statistics within parcels makes it possible to quantify these differences, and so directly compare the four sets of results.
Thursday, January 10, 2019
comparing fMRIPrep and HCP Pipelines: tSNR images
See this for the introduction to this series of posts. Yesterday I showed that the motion regressors produced by the two programs are nearly identical (as we'd hope, since they started with the same DICOMs!). But how do the preprocessed images compare? Short version: the preprocessed images are qualitatively similar in appearance (and standard QC images), with differences between people more obvious than differences between the pipeline. But only "similar", not "nearly identical" (which is how I described the motion regressors).
(Much) Longer version: The main goal of these preprocessing tests is to compare the GLM estimates (described a bit in the introduction and more later), not to perform QC on the images from the pipelines. This adds a complication, however, because the images that go into the GLMs are not exactly the same as the images that come out of the pipelines. It strikes me as most useful to look at the images as ready to go into the GLMs, since that's the final step at which things vary - identical GLMs were performed at every voxel and vertex.
We wanted to make the GLM estimate comparisons as direct and fair as possible, so aimed to do equivalent transformations of the surface and volume preprocessed images from each pipeline. If you think we missed that target, please let me know. We use afni for the after-preprocessing steps, as summarized here. First, both fMRIPrep and the HCP pipelines produce MNI-normalized volumes, but the bounding box is a bit different, so we used 3dresample to match the fMRIPrep images to the HCP, as well as to reorient to LPI. Both the fMRIPrep and HCP pipeline volumes are then smoothed (3dBlurToFWHM, -FWHM 4); surfaces are NOT smoothed with afni commands, since they were smoothed in the preprocessing pipelines. Finally, the timeseries at each voxel (or vertex) was scaled (in the usual way for afni, see this or this), using 3dcalc -a full.timeseries -b mean.timeseries -expr 'min(200, a/b*100)*step(a)*step(b)', where mean.timeseries is an image of the voxel (or vertex)-wise means, calculated with 3dTstat.
Single frames (this is #100 from the same run as in the previous post) from the ready-for-afni images look similar; an obvious difference is that the HCP pipelines mask the brain, while fMRIPrep does not. I have the color scaling the same in both images (90 to 100) and the intensity in the brain is similar (as it should be, given the scaling); the voxel under the crosshairs has the value 99.8 after fMRIPrep and 98.6 after the HCP pipelines.The frontal dropout (yellow arrow) is obvious in both, but looks like weird striping in the HCP image because of the masking; without masking it blends into the background. Spot-checking frames of the images I can't say that I've found one preprocessing consistently "better" than the other; by eye, sometimes one looks a bit better, sometimes the other.
Here's the same frame, from the surfaces. The HCP pipelines project to a different underlay surface anatomy than fMRIPrep, so the brain shapes are a bit different (fMRIPrep to fsaverage5, 10242 vertices/hemisphere; HCP with 32492 vertices/hemisphere). To be honest, I find these harder to interpret ... swirly colors.
If the single frames look similar, what about QC images? Well, by eye they also strike me as "different but similar", without an overwhelming preference for one being better than the other. The above are tSNR images of the same test run, all with the same color scaling.
Is the story in this whole series of comparison posts going to be "similar"? No, differences turn up in the GLM results, especially when summarizing within parcels ... stay tuned.
Note: the movement graphs and tSNR images were made with R knitr code. The full pdf (four tasks for this person) is here, and its source here. The paths/etc. won't work to compile the knitr (unless you're on our network!), but the plotting code and afni commands may prove useful; the sourced gifti plotting functions are here.
(Much) Longer version: The main goal of these preprocessing tests is to compare the GLM estimates (described a bit in the introduction and more later), not to perform QC on the images from the pipelines. This adds a complication, however, because the images that go into the GLMs are not exactly the same as the images that come out of the pipelines. It strikes me as most useful to look at the images as ready to go into the GLMs, since that's the final step at which things vary - identical GLMs were performed at every voxel and vertex.
We wanted to make the GLM estimate comparisons as direct and fair as possible, so aimed to do equivalent transformations of the surface and volume preprocessed images from each pipeline. If you think we missed that target, please let me know. We use afni for the after-preprocessing steps, as summarized here. First, both fMRIPrep and the HCP pipelines produce MNI-normalized volumes, but the bounding box is a bit different, so we used 3dresample to match the fMRIPrep images to the HCP, as well as to reorient to LPI. Both the fMRIPrep and HCP pipeline volumes are then smoothed (3dBlurToFWHM, -FWHM 4); surfaces are NOT smoothed with afni commands, since they were smoothed in the preprocessing pipelines. Finally, the timeseries at each voxel (or vertex) was scaled (in the usual way for afni, see this or this), using 3dcalc -a full.timeseries -b mean.timeseries -expr 'min(200, a/b*100)*step(a)*step(b)', where mean.timeseries is an image of the voxel (or vertex)-wise means, calculated with 3dTstat.
Single frames (this is #100 from the same run as in the previous post) from the ready-for-afni images look similar; an obvious difference is that the HCP pipelines mask the brain, while fMRIPrep does not. I have the color scaling the same in both images (90 to 100) and the intensity in the brain is similar (as it should be, given the scaling); the voxel under the crosshairs has the value 99.8 after fMRIPrep and 98.6 after the HCP pipelines.The frontal dropout (yellow arrow) is obvious in both, but looks like weird striping in the HCP image because of the masking; without masking it blends into the background. Spot-checking frames of the images I can't say that I've found one preprocessing consistently "better" than the other; by eye, sometimes one looks a bit better, sometimes the other.
Here's the same frame, from the surfaces. The HCP pipelines project to a different underlay surface anatomy than fMRIPrep, so the brain shapes are a bit different (fMRIPrep to fsaverage5, 10242 vertices/hemisphere; HCP with 32492 vertices/hemisphere). To be honest, I find these harder to interpret ... swirly colors.
Is the story in this whole series of comparison posts going to be "similar"? No, differences turn up in the GLM results, especially when summarizing within parcels ... stay tuned.
Note: the movement graphs and tSNR images were made with R knitr code. The full pdf (four tasks for this person) is here, and its source here. The paths/etc. won't work to compile the knitr (unless you're on our network!), but the plotting code and afni commands may prove useful; the sourced gifti plotting functions are here.
Wednesday, January 9, 2019
comparing fMRIPrep and HCP Pipelines: motion regressors
See this post for the introduction to this series of posts. Here, I'll describe and compare the motion regressors produced by the fMRIPrep and HCP Pipelines. Short version: the file formats are a bit different, but the estimated motion is nearly identical.
Long version: the first step is figuring out where the motion regressors are put by the two pipelines. If you kept the HCP file structure, there will be one Movement_Regressors.txt file for each preprocessed run. The files are always called Movement_Regressors.txt; you track which one goes with which run and person by the directory structure. For example, the file for subject 203418, run SternBas2_PA is /MINIMALLY_PREPROCESSED/203418/MNINonLinear/Results/tfMRI_SternBas2_PA/Movement_Regressors.txt.
The above image is the first bit of the file, read into R. It has 12 columns: x, y, z, roll, pitch, yaw, then their six derivatives (stated on page 75 of HCP_S900_Release_Reference_Manual.pdf). The rotation columns are in degrees, translation in mm. There are 600 rows in this particular text file, matching the number of volumes acquired in the run.
fMRIPrep outputs a file ending in _confounds.tsv for each run, with its location and name following the BIDS naming conventions. For example, the motion regressors file for the same subject and run as above is /sub-203418/fMRIPrep_output/fmriprep/sub-203418/ses-01/func/sub-203418_ses-01_task-Stern_acq-MB42p4PA_run-02_bold_confounds.tsv.
The above is the first bit of the file, read into R. As you can tell in the screenshot, this file has many more columns than the HCP version: 35 instead of 12 (the same number of rows, though: one per acquired volume). The columns are named, and the documentation gives details on each. The motion regressors I want are in six columns, called X, Y, Z, RotX, RotY, and RotZ. "RotX","RotY","RotZ" correspond to roll, pitch, and yaw, but are in radians; translation is in mm.
UPDATE (24 April 2019): fMRIPrep version 1.3.2 names the columns "trans_x", "trans_y", "trans_z", "rot_x", "rot_y", "rot_z".
Now I can plot both sets of motion parameters to see if they vary. I converted the fMRIPrep rotations to degrees, then plotted the six estimates from each program in the top of this figure. (TRs (1.2 ms) are along the x-axis; the vertical grey lines give 1-minute intervals.) As you hopefully can see (click the figure to enlarge), it's actually hard to tell that there are 12 lines in the top - those from fMRIPrep and the HCP pipelines are so close that it mostly looks like there are only 6 lines.
The lower frame has the FD version of the same two sets of motion estimates; HCP Pipelines in black and fMRIPrep in green (this is unfiltered FD, and our censoring threshold of 0.9 is shown in red). A little separation between the two FD lines is visible, but again, quite minimal.
I picked this run for this post because there's a bit of actual head motion, and if you zoom in around frame 525 you can see that there is a little separation between the translation estimates. I had to check a few runs to find one with any visible separation; in our 13 test people (with 8 runs each) this is pretty much as large as the discrepancy gets, which is why at this time I'm describing the motion estimates from fMRIPrep and the HCP pipeline as essentially identical.
Note: The figure above is from a knitr template that I made for doing these QC comparisons; see note at the end of this post.
Long version: the first step is figuring out where the motion regressors are put by the two pipelines. If you kept the HCP file structure, there will be one Movement_Regressors.txt file for each preprocessed run. The files are always called Movement_Regressors.txt; you track which one goes with which run and person by the directory structure. For example, the file for subject 203418, run SternBas2_PA is /MINIMALLY_PREPROCESSED/203418/MNINonLinear/Results/tfMRI_SternBas2_PA/Movement_Regressors.txt.
The above image is the first bit of the file, read into R. It has 12 columns: x, y, z, roll, pitch, yaw, then their six derivatives (stated on page 75 of HCP_S900_Release_Reference_Manual.pdf). The rotation columns are in degrees, translation in mm. There are 600 rows in this particular text file, matching the number of volumes acquired in the run.
fMRIPrep outputs a file ending in _confounds.tsv for each run, with its location and name following the BIDS naming conventions. For example, the motion regressors file for the same subject and run as above is /sub-203418/fMRIPrep_output/fmriprep/sub-203418/ses-01/func/sub-203418_ses-01_task-Stern_acq-MB42p4PA_run-02_bold_confounds.tsv.
The above is the first bit of the file, read into R. As you can tell in the screenshot, this file has many more columns than the HCP version: 35 instead of 12 (the same number of rows, though: one per acquired volume). The columns are named, and the documentation gives details on each. The motion regressors I want are in six columns, called X, Y, Z, RotX, RotY, and RotZ. "RotX","RotY","RotZ" correspond to roll, pitch, and yaw, but are in radians; translation is in mm.
UPDATE (24 April 2019): fMRIPrep version 1.3.2 names the columns "trans_x", "trans_y", "trans_z", "rot_x", "rot_y", "rot_z".
Now I can plot both sets of motion parameters to see if they vary. I converted the fMRIPrep rotations to degrees, then plotted the six estimates from each program in the top of this figure. (TRs (1.2 ms) are along the x-axis; the vertical grey lines give 1-minute intervals.) As you hopefully can see (click the figure to enlarge), it's actually hard to tell that there are 12 lines in the top - those from fMRIPrep and the HCP pipelines are so close that it mostly looks like there are only 6 lines.
I picked this run for this post because there's a bit of actual head motion, and if you zoom in around frame 525 you can see that there is a little separation between the translation estimates. I had to check a few runs to find one with any visible separation; in our 13 test people (with 8 runs each) this is pretty much as large as the discrepancy gets, which is why at this time I'm describing the motion estimates from fMRIPrep and the HCP pipeline as essentially identical.
Note: The figure above is from a knitr template that I made for doing these QC comparisons; see note at the end of this post.
Friday, January 4, 2019
comparing fMRIPrep and HCP Pipelines: introduction
This is the first in a series of posts describing a set of comparisons we've done between fMRIPrep and the HCP Pipelines: if the same task fMRI dataset is run through both pipelines, extracting surfaces and volumes from each, how do the eventual GLM results compare?
These sorts of comparisons are conceptually easy but tricky to design and time-consuming to complete. I submitted this as a poster for OHBM 2019, we will likely write this up as a paper, and plan to release the code and images. But these blog posts are a start, and feedback is most welcome. Let me know if you want additional details or files and I can try to get them to you sooner than later.
roadmap
The DMCC is a big dataset (currently around 80 people with at least 3 scanning sessions, and we're not done). To keep the preprocessing comparisons tractable I picked just 13 of the participants to include, requiring that they have complete Baseline session data (two runs of each of the four tasks), be unrelated, have acceptable movement (a qualitative judgement), and have visible motor activity in their single-subject Buttons GLMs (with HCP Pipeline preprocessing). This last is an effort for an unbiased QC estimate: in three of our tasks the participants push a response button, so GLMs using these button-pushes as the only events of interest (ignoring all of the cognitive conditions) should show motor activation if the signal quality is acceptable. I used the volumetric HCP Pipeline preprocessing for screening because that's what we've been using (and so have results for all people); that may introduce a bit of bias (favoring people that did well with the volumetric HCP Pipelines).
These sorts of comparisons are conceptually easy but tricky to design and time-consuming to complete. I submitted this as a poster for OHBM 2019, we will likely write this up as a paper, and plan to release the code and images. But these blog posts are a start, and feedback is most welcome. Let me know if you want additional details or files and I can try to get them to you sooner than later.
roadmap
- This post describes the dataset, preprocessing, tasks, GLMs, and parcellation (for ROIs).
- The next post(s) will show the output of the two pipelines, as motion regressors, QC metrics, and images.
- Then posts will describe and compare the GLM statistics calculated from each set of images: full-brain images, parcel-wise curves, by target knots in the parcels, and a subset of the parcels.
dataset
I am particularly interested in how the preprocessing would affect the statistics for the Dual Mechanisms of Cognitive Control (DMCC) project since that's the project I'm most involved with. I've previously shown bits from this dataset and described the acquisition parameters; briefly, each DMCC scanning session includes two runs each of four cognitive control tasks (AX-CPT, Cued task-switching, Sternberg, and Stroop). Task runs were each approximately 12 minutes, alternating AP/PA. All scanning was with a 3T SIEMENS Prisma scanner with a 32 channel head coil, 2.4 mm isotropic voxels, TR = 1.2 s, and an MB4 CMRR sequence.The DMCC is a big dataset (currently around 80 people with at least 3 scanning sessions, and we're not done). To keep the preprocessing comparisons tractable I picked just 13 of the participants to include, requiring that they have complete Baseline session data (two runs of each of the four tasks), be unrelated, have acceptable movement (a qualitative judgement), and have visible motor activity in their single-subject Buttons GLMs (with HCP Pipeline preprocessing). This last is an effort for an unbiased QC estimate: in three of our tasks the participants push a response button, so GLMs using these button-pushes as the only events of interest (ignoring all of the cognitive conditions) should show motor activation if the signal quality is acceptable. I used the volumetric HCP Pipeline preprocessing for screening because that's what we've been using (and so have results for all people); that may introduce a bit of bias (favoring people that did well with the volumetric HCP Pipelines).
pipelines, GLMs, and parcels
These analyses use version 3.17.0 of the HCP Pipelines (adapted several years ago for the DMCC acquisitions) and fMRIPrep version 1.1.7; each pipeline began with the same DICOM images.
The same GLMs (event onsets, model types, etc.) were run on the images from each version (surface, volume) of each pipeline; all with AFNI. The "standard" DMCC GLMs are not particularly simple because the task runs are mixed (block and event), and we're fitting GLMs to model both "sustained" (block) and event-related activity. The GLMs use 3dDeconvolve and 3dREMLfit, with TENTzero (FIR-type) for the events (one knot for two TRs). Most of the GLM model parameters were dictated by the experimental design, but tweaking (e.g., how long to make the TENT knots, whether to include regressors for the block onsets and offsets) was done with the HCP pipeline preprocessed images.
To keep these preprocessing comparisons tractable at the GLM level, we only included Baseline session images, and focused on contrasting high and low cognitive control. Which trials correspond to high and low cognitive control varies across the four tasks, and we don't expect identical levels of difficulty or brain activity in the tasks. Regardless, high vs. low cognitive control conditions gives a fairly "apples to apples" basis, and is a reasonably strong effect.
Another complication for preprocessing comparisons is working out the anatomical correspondence: every voxel does not have a single vertex in the surface representation (and each vertex doesn't match a single voxel). While we had both the fMRIPrep and HCP Pipelines output MNI-normalized volumes (so volume-to-volume voxel-level correspondence), the two pipelines use different surface meshes (fsaverage5 vs. HCP), so there isn't an exact match between the vertices (see this thread, for example).
For these comparisons I deal with corresponding across the output formats by making qualitative assessments (e.g., if this GLM output statistic is thresholded the same, do the same areas tend to show up in the volumes and surfaces?), but more practically by summarizing the statistics within anatomic parcels. The parcellation from (Schaefer et al. 2017) is ideal for this: they released it in volume (MNI) and surface (both fsaverage5 and HCP) formats.We used the 400-area version here, allowing direct comparisons: a mean of parcel #6 can be computed in all four output images (HCP volume, HCP surface, fMRIPrep volume, fMRIPrep surface). The number of voxels or vertices making up each parcel varies between the volume and surfaces, of course.
UPDATE 10 January 2019: Here's the command actually used to run fMRIPrep; thanks Mitch Jeffers!
singularity run \
--cleanenv \
-B ${MOUNT}:/tmp \
/home/ccp_hcp/fmriprep/SingularityImages/fmriprep-1.1.7.simg \
--fs-license-file /tmp/.license/freesurfer/license.txt \
-w /tmp \
/tmp/${INPUT} /tmp/${OUTPUT} \
participant --participant_label ${SUBJECT} \
--output-space fsaverage5 \
-n-cpus 16 --omp-nthreads 4 \
--mem-mb 64000 -vvvv \
--use-plugin /tmp/plugin.yml
UPDATE 6 September 2019: The raw and preprocessed images of the dataset we used for these comparisons is now on openneuro, called DMCC13benchmark.
UPDATE 4 January 2021: Corrected DMCC13benchmark openneuro links.
Friday, July 6, 2018
RSA: how to describe with a single number? - update
Some years ago I posted about quantifying RSA matrices with a single number. Since then, it seems that the field has settled on two methods for quantifying RSA matrices: one based on differences and the other based on Kendall's tau-a.
To illustrate the methods, suppose this is the RSA matrix for one person, and the corresponding "reference matrix"; the numbers in the cells are Pearson correlations. In this case, we expect the two cells with the same letter (2,F and 0,F; 2,P and 0,P) to have higher correlations than all the other cells (which mix letters).
The matrix is symmetrical, with 1s along the diagonal, so we'll only use the numbers from the lower triangle. We can "unwrap" these values into vectors, like this:
I previously called the difference-based method "averaging the triangles"; conceptually, we take the difference between the average of the two cells we expect to have a large value (cells with 1s in the Reference), and the average of the four cells we expect to have smaller values (0 in the Reference). This can also be thought of as using a contrast matrix, such as in Oedekoven, et. al (2017). Since you can't do math on correlations without going through the Fisher R-to-Z transform, here's an example of the calculation:
(It's of course equivalent to calculate this by multiplying the sum of the y == 1 cells by 0.5 and y == 0 cells by 0.25.)
The Kendall's tau-a based method is recommended in Nili, et. al (2014). There's a function to calculate this in the DescTools R package; since it's rank-based, there's no need to do the Fisher transformation:
Which method should you use? One consideration is if you want the magnitude of the numbers in the RSA matrix cells to matter, or just their relative sizes (ranks). Here, I made a RSA matrix with the same rank ordering as before (in x), but the difference between same-letter and different-letter cells are more extreme:
The Kendall's tau-a value for x1 and y is exactly the same as x and y, but the mean difference is larger. Is it better to be sensitive to magnitude? Not necessarily. In practice, when I've calculated both methods on the same dataset I've found extremely similar results, which seems proper; it's hard to imagine a strong effect that would be only be detected by Kendall's tau-a.
UPDATE 12 December 2018: I removed transforming the difference scores back to correlations, leaving the differences as Fisher-transformed zs, since this seems easier for later interpretation and calculations.
To illustrate the methods, suppose this is the RSA matrix for one person, and the corresponding "reference matrix"; the numbers in the cells are Pearson correlations. In this case, we expect the two cells with the same letter (2,F and 0,F; 2,P and 0,P) to have higher correlations than all the other cells (which mix letters).
x <- c(.728, .777, .705, .709, .873, .705); # one person
y <- c( 0, 1, 0, 0, 1, 0); # reference
I previously called the difference-based method "averaging the triangles"; conceptually, we take the difference between the average of the two cells we expect to have a large value (cells with 1s in the Reference), and the average of the four cells we expect to have smaller values (0 in the Reference). This can also be thought of as using a contrast matrix, such as in Oedekoven, et. al (2017). Since you can't do math on correlations without going through the Fisher R-to-Z transform, here's an example of the calculation:
get.FTrz <- function(in.val) { return(.5 * log((1+in.val)/(1-in.val))); } # Fisher's r-to-z transformation
get.FTzr <- function(in.val) { return((exp(2*in.val)-1)/(exp(2*in.val)+1)); } # Fisher's z-to-r transformation
mean(get.FTrz(x[which(y == 1)])) - mean(get.FTrz(x[which(y == 0)]));
# [1] 0.3006614
(It's of course equivalent to calculate this by multiplying the sum of the y == 1 cells by 0.5 and y == 0 cells by 0.25.)
The Kendall's tau-a based method is recommended in Nili, et. al (2014). There's a function to calculate this in the DescTools R package; since it's rank-based, there's no need to do the Fisher transformation:
KendallTauA(x,y)
# [1] 0.5333333
Which method should you use? One consideration is if you want the magnitude of the numbers in the RSA matrix cells to matter, or just their relative sizes (ranks). Here, I made a RSA matrix with the same rank ordering as before (in x), but the difference between same-letter and different-letter cells are more extreme:
x1 <- c(0.2, 0.9, 0.1, 0.01, 0.83, 0.22);
KendallTauA(x1,y)
# [1] 0.5333333 # same
mean(get.FTrz(x1[which(y == 1)])) - mean(get.FTrz(x1[which(y == 0)]));
# [1] 1.195997 # bigger
The Kendall's tau-a value for x1 and y is exactly the same as x and y, but the mean difference is larger. Is it better to be sensitive to magnitude? Not necessarily. In practice, when I've calculated both methods on the same dataset I've found extremely similar results, which seems proper; it's hard to imagine a strong effect that would be only be detected by Kendall's tau-a.
UPDATE 12 December 2018: I removed transforming the difference scores back to correlations, leaving the differences as Fisher-transformed zs, since this seems easier for later interpretation and calculations.
Subscribe to:
Posts (Atom)