[[11]{.chapter-number}  [MRIQC]{.chapter-title}]{#sec-mriqc .quarto-section-identifier}

11 MRIQC

library(readr)
library(dplyr)
library(ggplot2)
library(duckplyr)
library(scattermore)

Part of the A2CPS MRI quality control procedure Section 4.1.2 uses metrics derived from the MRI Quality Control (MRIQC) pipeline (Esteban et al., 2017). However, MRIQC provides several metrics beyond the subset used by A2CPS. This kit reviews those metrics and shows how they compare with others that are available in the MRIQC database.

11.1 Starting Project

11.1.1 Locate Data

On TACC, the neuroimaging data are stored underneath the releases. For example, data release v2.#.# is underneath

pre-surgery/mris

The MRIQC metrics are underneath derivatives/mriqc.

11.1.2 Extract Data

The A2CPS pipeline produces a standard set of MRIQC outputs, including both participant- and group-level results. For a detailed explanation of the outputs, please see the official MRIQC documentation. At a high-level, the outputs include:

participant-level metrics: underneath sub-[recordid]/ses-[protocolid]/{anat,func,dwi}
participant-level reports: html documents that display participant-level figures related to data quality and MRIQC preprocessing
group-level results: files with the prefix group_ that collate the participant-level results

$ ls /corral-secure/projects/A2CPS/products/consortium-data/pre-surgery/mris/derivatives/mriqc | head
group_bold.html
group_bold.tsv
group_dwi.html
group_dwi.tsv
group_T1w.html
group_T1w.tsv
sub-10003
sub-10003_ses-V1_dwi.html
sub-10003_ses-V1_T1w.html
sub-10003_ses-V1_task-cuff_run-01_bold.html

For example, here are all of the metrics available for the functional data.

group_bold <- read_tsv("data/mriqc/group_bold.tsv")
group_bold |>
  head()

size_x	summary_fg_p05	gsr_x	fd_perc	fwhm_x	fwhm_avg	gsr_y	size_y	gcor	summary_bg_k	fwhm_z	spacing_z	summary_bg_median	summary_fg_mean	summary_fg_p95	size_z	summary_bg_mad	spacing_x	summary_bg_n	summary_bg_p95	dvars_nstd	summary_bg_stdv	summary_fg_k	spacing_tr	size_t	snr	summary_fg_stdv	tsnr	dvars_vstd	dvars_std	aqi	fd_mean	fd_num	fber	dummy_trs	spacing_y	aor	efc	summary_bg_mean	fwhm_y	summary_bg_p05	summary_fg_mad	summary_fg_median	summary_fg_n	bids_name
90	255	-0.0121202	92.77652	3.069183	2.910943	0.0296651	90	0.0039961	69.6477	2.866987	2.4	10	1072.0242	1873	60	14.8260	2.40000	389198	167	114.37294	89.8264	2.5801	0.8000000	443	2.220860	468.2846	16.08951	0.9862437	1.283511	0.0532005	0.9142547	411	10858.5127	7	2.40000	0.0045815	0.4243	38.3237	2.796658	0	356.6813	1040	96802	sub-10355_ses-V3_task-cuff_run-01_bold
90	290	0.0088436	47.40406	3.250654	2.996301	0.0279804	90	0.0035719	97.8685	2.940925	2.4	14	1215.6718	2224	60	20.7531	2.40000	366337	248	39.78583	153.3359	4.6670	0.8000000	443	2.032800	569.6553	26.74147	0.9690394	1.090245	0.0189762	0.2202484	210	7218.0190	7	2.40000	0.0046997	0.4688	58.3281	2.797325	0	399.2685	1158	119663	sub-20041_ses-V3_task-cuff_run-02_bold
90	182	-0.0173981	38.47875	2.851042	3.053996	0.0641578	90	0.0022321	50.8674	3.215412	2.4	17	471.1883	806	60	4.6468	2.40000	388706	127	36.21926	59.6670	0.5428	0.8000000	447	2.444261	184.9220	35.41455	0.9611394	1.029062	0.0133520	0.1717237	172	731.6926	3	2.40000	0.0003907	0.4995	35.7919	3.095533	14	165.4644	452	97294	sub-20399_ses-V3_task-cuff_run-02_bold
96	6845	-0.0110442	94.15730	2.686887	2.534638	0.0287072	96	0.0061725	70.4831	2.449446	2.4	26	19412.4011	27486	54	38.5477	2.21875	394502	3262	66.94345	1492.6184	1.4292	0.7999997	445	3.529797	5676.2202	25.88404	0.9959189	1.174573	0.0453041	0.7045787	419	584686.2500	5	2.21875	0.0025314	0.4514	699.5358	2.467583	0	3896.1295	20036	103162	sub-11069_ses-V3_task-cuff_run-02_bold
90	306	-0.0119028	55.32880	3.275942	3.119962	0.0276843	90	0.0013895	276.4821	3.100054	2.4	12	1143.9366	1970	60	17.7912	2.40000	385415	181	41.74820	138.2481	3.0032	0.8000000	441	2.274493	490.6563	20.88673	0.9053607	1.014526	0.0416413	0.2101655	244	8937.8770	9	2.40000	0.0025078	0.4332	45.6822	2.983891	0	366.2935	1116	100585	sub-10295_ses-V1_task-cuff_run-01_bold
90	191	0.0039095	29.93197	2.805417	2.664839	0.0147271	90	0.0099024	98.7085	2.593362	2.4	11	1129.2300	1991	60	16.3053	2.40000	398773	163	38.32907	103.9497	2.2260	0.8000000	441	2.206411	513.9539	31.49358	0.9654090	1.134152	0.0140956	0.1442416	132	11567.4209	9	2.40000	0.0010844	0.4023	40.0061	2.595737	0	367.4937	1134	87227	sub-10326_ses-V1_task-cuff_run-02_bold

For a detailed description of these metrics, please see the official MRIQC documentation.

11.1.3 Comparison with Reference Distributions

One strength of MRIQC is that the pipeline automatically stores the metrics in a centralized database. The centralized database is available via the MRIQC Web API. An example of interacting with the API can be found in https://github.com/psadil/mriqc-export. In this kit, the metrics have already been downloaded and stored in a set parquet files. Let’s use that reference set to rate A2CPS. One of the most important determiners of quality is motion, so let’s focus on the average Framewise Displacement.

all_bold <- duckplyr::read_parquet_duckdb(
  fs::dir_ls(
    "data/mriqcwebapi/bold",
    recurse = TRUE,
    glob = "*parquet"
  )
) |>
  dplyr::select(`_id`, fd_mean) |>
  collect()

nrow(all_bold)

[1] 1245400

Loading them, we see that there are over 1 million entries in the database.

Let’s create a simple scatter plot to compare motion in A2CPS against motion in the MRIQC database. Note that because there are so many points to display, we’re using the package scattermore.

dplyr::bind_rows(list(a2cps = group_bold, mriqc = all_bold), .id = "source") |>
  ggplot(aes(x = source, y = fd_mean)) +
  geom_violin() +
  geom_scattermore(position = position_jitter(height = 0)) +
  coord_cartesian(ylim = c(0, 5))

Quantitatively, the dataset is in the upper quantiles of motion, which is to be expected given the study population.

empirical_motion_cdf <- ecdf(all_bold$fd_mean)

group_bold |>
  mutate(
    fd_quantile = empirical_motion_cdf(fd_mean)
  ) |>
  ggplot(aes(x = fd_quantile)) +
  geom_histogram()

11.2 Considerations While Working on the Project

11.2.1 Framewise Displacement and A2CPS

A key element of the A2CPS Quality Ratings for raw data is based on Framewise Displacement. Although MRIQC provides a measurement of framewise displacement, it is not the one that is entered into the rating. Instead, A2CPS uses the estimate of motion from fMRIPrep, what it refers to as rmsd, also referred to as FD_Jenk, in reference to the method as described in Jenkinson et al. (2002). The measure of motion provided by MRIQC is sometimes referred to as FD_Power (e.g., Parkes et al. (2018)), referring to Power et al. (2012). FD_Power measures the distance travelled by a point on the edge of a sphere between frames (usually using a sphere of 50 mm), whereas FD_Jenk measures the average distance traveled by the entire sphere (usually using a sphere of 80 mm). The two measures are often almost perfectly correlated, but FD_Power tends to be about 1.7x as high as FD_Jenk.

Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 17(2), 825–841. https://doi.org/10.1006/nimg.2002.1132

Parkes, L., Fulcher, B., Yücel, M., & Fornito, A. (2018). An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. Neuroimage, 171, 415–436. https://doi.org/10.1016/j.neuroimage.2017.12.073

Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage, 59(3), 2142–2154. https://doi.org/10.1016/j.neuroimage.2011.10.018

11.2.2 MRIQC Versions

Please be aware that, across versions of MRIQC, the manner in which MRIQC calculates metrics has changed. These changes can lead to substantial differences in the distribution of metrics. For one set of examples, please see this GitHub issue.

11.2.3 Variability Across Scanners

Many of these metrics exhibit substantial variability across the scanners, which may confound some analyses. For example, the scanner “NS” (serial number: 70032) has the “Prescan Normalization” feature turned off, which produces substantial intensity inhomogeneity in the images. This inhomogeneity is very noticeable in the images, resulting in much voxels near the edge of the field-of-view being much brighter than voxels in the center of the field-of-view. Unfortunately, this spatial pattern confounds one of the metrics produced by MRIQC that is designed to estimate ghosting: the “Ghost-to-Signal” Ratio. That is, the values for this ratio suggest that the NS scanner suffers from substantial ghosting, but those values are instead driven primarily by intensity inhomogeneity.

As another example, the scanner UC (serial number: 71399) is unique in that the structural images were collected with “Image Domain Parallel Imaging”. This form of parallel imaging causes a near total suppression of the background signal (that is, most background voxels have intensity 0). Several of the MRIQC metrics incorporate the background signal, and so this noise suppression causes those metrics to have a fundamentally different scaling as compared to metrics calculated on images without that suppression. Consider the “Dietrich” version of the signal-to-noise ratio (Dietrich et al., 2007), which uses the standard deviation of the intensity in the background to scale the average intensity of the foreground, producing substantially larger estimates of the signal-to-noise ratio.

Dietrich, O., Raya, J. G., Reeder, S. B., Reiser, M. F., & Schoenberg, S. O. (2007). Measurement of signal-to-noise ratios in MR images: Influence of multichannel coils, parallel imaging, and reconstruction filters. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine, 26(2), 375–385. https://doi.org/10.1002/jmri.20969

For a detailed description of these particular issues, please see these slides. In general, interpreting the MRIQC metrics should be done with caution.

11.2.4 Citations

If you use these results, please cite the MRIQC report (Esteban et al., 2017) and all relevant citations of the pipeline configuration.

Esteban, O., Birman, D., Schaer, M., Koyejo, O. O., Poldrack, R. A., & Gorgolewski, K. J. (2017). MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PloS One, 12(9), e0184661. https://doi.org/10.1371/journal.pone.0184661

In publications or presentations including data from A2CPS, please include the following statement as attribution:

Data were provided [in part] by the A2CPS Consortium funded by the National Institutes of Health (NIH) Common Fund, which is managed by the Office of the Director (OD)/ Office of Strategic Coordination (OSC). Consortium components and their associated funding sources include Clinical Coordinating Center (U24NS112873), Data Integration and Resource Center (U54DA049110), Omics Data Generation Centers (U54DA049116, U54DA049115, U54DA049113), Multi-site Clinical Center 1 (MCC1) (UM1NS112874), and Multi-site Clinical Center 2 (MCC2) (UM1NS118922).

Note

The following published papers should be cited when referring to A2CPS Protocol and Biomarkers: Sluka et al. (2023) Berardi et al. (2022)

Berardi, G., Frey-Law, L., Sluka, K. A., Bayman, E. O., Coffey, C. S., Ecklund, D., Vance, C. G. T., Dailey, D. L., Burns, J., Buvanendran, A., McCarthy, R. J., Jacobs, J., Zhou, X. J., Wixson, R., Balach, T., Brummett, C. M., Clauw, D., Colquhoun, D., Harte, S. E., … Wandner, L. D. (2022). Multi-site observational study to assess biomarkers for susceptibility or resilience to chronic pain: The acute to chronic pain signatures (A2CPS) study protocol. Frontiers in Medicine, 9. https://doi.org/10.3389/fmed.2022.849214

Sluka, K. A., Wager, T. D., Sutherland, S. P., Labosky, P. A., Balach, T., Bayman, E. O., Berardi, G., Brummett, C. M., Burns, J., Buvanendran, A., et al. (2023). Predicting chronic postsurgical pain: Current evidence and a novel program to develop predictive biomarker signatures. Pain, 164(9), 1912–1926. https://doi.org/10.1097/j.pain.0000000000002938