Friday, June 18, 2021

DMCC55B supplemental as tutorial: basic fMRI QC: temporal mean, SD, and tSNR

This is the fourth post in a series describing the DMCC55B supplemental files. The first introduces the dataset, the second questionnaire data, and the third motion. Here I'll highlight another basic fMRI QC practice: creating and checking temporal mean, standard deviation, and tSNR images. For background on these types of QC images I suggest starting with this post, especially the multiple links in the first sentence.

For DMCC55B, we preprocessed the images as both volume and surface, so the QC was also both volume and surface. I generally find QC easier to judge with unmasked volumes, but if surface analysis is planned, the surface representations must be checked as well.

During DMCC data collection we create a QC summary file for each individual participant (example here), with volumes, surfaces, and motion (realignment parameters) for every run. A first QC check for (unmasked) volume images is straightforward: do they look like brains? This ("got brains?") check doesn't apply to surfaces, since any valid gifti image will be brain-shaped. Instead, our first QC pass for the surface images is to check the temporal mean for "tiger stripes" around the central sulcus. Below is the vertex-wise means for Bas Stern AP from the example, with the blue lines pointing to the stripes. If these stripes are not visible, something likely went wrong with the preprocessing.

For ease of group comparison, the DMCC55B QC supplemental files are split by type rather than participant: QC_SD_surface.pdf for vertex-wise temporal standard deviation, QC_tSNR_surface.pdf for vertex-wise tSNR; QC_SD_volume.pdf and QC_tSNR_volume.pdf for the corresponding voxel-wise images. With eight runs for each of 55 participants these QC summary files are still rather long, but the consistent scaling (same intensity used for all participants when plotting) makes it possible to scroll through and spot trends and oddities. Such group review can be very beneficial, especially before embarking on statistical analyses.

For example, the SD images for some participants/runs are much brighter and fuzzier than others (compare f2157me and f2499cq on page 8). Broadly, the more the temporal SD images resemble maps of brain vessel density, the better the quality, though of course the resolution will vary with fMRI acquisition parameters (e.g., in the 2.4 mm isotropic DMCC images the Circle of Willis should be clearly visible). Artifacts can also be spotted, such as the ghostly "crescents" in some participant's PA images (e.g., f5001ob).

Implementation notes:

The .rnw file with each (knitr) pdf has the R code to create the temporal statistic images, as well as produce the summary .pdfs. The startup code chunk of each document has the code to make the images (from the 4d niftis and giftis produced by fmriprep), while the later chunk plots the images. 

The volumetric code uses the AFNI 3dTstat and 3dcalc functions (called from R using system2()) to make the temporal mean, standard deviation, and tSNR images, which are written out as NIfTI images. Saving the statistical images is useful for compilation speed, but more importantly, so that they can be examined in 3d if closer checking is needed. I strongly suggest using existing functions from a well-established program (such as AFNI) whenever possible, for clarity and to avoid introducing bugs.

The volumetric images are plotted with base R image() rather than with my volumetric plotting function, because I wanted to plot one slice through each plane. Thus, the _volume 55B supplementals can also serve as an example of simple NIfTI image plotting. (This is my general volume-plotting knitr tutorial.) 

Finally, there is a comment in the volume .rnw files that the input images are already in LPI orientation. I strongly suggest transforming NIfTI images to LPI immediately after preprocessing (e.g., with 3dresample), if they are not already, as this often avoids left/right flipping. 

Unlike volumes, the surface QC code calculates the vertex-wise mean and standard deviation in R, and my gifti plotting functions. Many AFNI functions can work with giftis, but these calculations (mean, standard deviation) are so simple, and plotting surfaces from a vector so straightforward, that I decided to save the images as text rather than gifti.

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