4  MRI Image Derived Phenotypes

library(readr)
library(tidyselect)

Often, researchers may wish to simply have a set of features that they can use to train and test predictive models. When those features are extracted from neuroimaging data, we refer to them as Image-Derived Phenotypes (IDPs). For an excellent overview of how IDPs are generated and used in large-scale studies, see the UK Biobank Brain Imaging Documentation.

This kit describes a subset of IDPs that have been compiled into a single table. While this table does not encompass all available measurements, it is a great place to start your analyses. The selection of phenotypes was based on those available within the UK Biobank (Miller et al., 2016), derived from structural and functional MRI scans (diffusion MRI phenotypes will be available in a subsequent release). A full data dictionary is available here.

Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., Thomas, D. L., Yacoub, E., Xu, J., Bartsch, A. J., Jbabdi, S., Sotiropoulos, S. N., Andersson, J. L., et al. (2016). Multimodal population brain imaging in the UK biobank prospective epidemiological study. Nature Neuroscience, 19(11), 1523–1536. https://doi.org/10.1038/nn.4393

These IDPs include: - sMRI: 15 volumetric measures from FSL’s FIRST, 3 global measures from Chapter 7, and 24 global measures from Chapter 9. - fMRI: 104 nodal fractional amplitude of low-frequency fluctuation (fALFF) values and 5460 functional connectivities from the NeuroMark 2.1 multi-resolution components (Chapter 19), for each of the four functional scans.

4.1 Starting Project

4.1.1 Locate Data

On TACC, the data are stored underneath the releases. For example, data release v2.1.0 is underneath

/corral-secure/projects/A2CPS/products/consortium-data/pre-surgery-release-2-1-0

Paths in this kit are written relative to that release folder, so the neuroimaging data are underneath mris.

The primary IDP table, mri.tsv, is located underneath the idp folder. Let’s take a look.

$ ls mris/idp/
mask_volumes.json  mask_volumes.tsv  mri.json  mri.tsv

Even though this represents only a subset of all possible neuroimaging derivatives, the table is quite wide because it contains features extracted across many brain regions and multiple functional tasks.

$ awk '{print NF; exit}' mris/idp/mri.tsv 
21886

4.1.2 Extract Data

Because this table is saved as a tab-separated values (tsv) file, any software capable of reading delimited text can load it. However, because it contains tens of thousands of columns, it is often best practice to load only the specific features you need for your analysis.

For example, if we are only interested in the resting state (REST1) fALFF measurements for our participants, we can select just the sub (participant identifier) column and any column matching that specific functional task.

read_tsv(
  "data/pre-surgery/mris/idp/mri.tsv",
  col_select = c("sub", matches("task_rest_run_1.*falff"))
) |>
  head()
sub task_rest_run_1_component_1_falff task_rest_run_1_component_2_falff task_rest_run_1_component_3_falff task_rest_run_1_component_4_falff task_rest_run_1_component_5_falff task_rest_run_1_component_6_falff task_rest_run_1_component_7_falff task_rest_run_1_component_8_falff task_rest_run_1_component_9_falff task_rest_run_1_component_10_falff task_rest_run_1_component_11_falff task_rest_run_1_component_12_falff task_rest_run_1_component_13_falff task_rest_run_1_component_14_falff task_rest_run_1_component_15_falff task_rest_run_1_component_16_falff task_rest_run_1_component_17_falff task_rest_run_1_component_18_falff task_rest_run_1_component_19_falff task_rest_run_1_component_20_falff task_rest_run_1_component_21_falff task_rest_run_1_component_22_falff task_rest_run_1_component_23_falff task_rest_run_1_component_24_falff task_rest_run_1_component_25_falff task_rest_run_1_component_26_falff task_rest_run_1_component_27_falff task_rest_run_1_component_28_falff task_rest_run_1_component_29_falff task_rest_run_1_component_30_falff task_rest_run_1_component_31_falff task_rest_run_1_component_32_falff task_rest_run_1_component_33_falff task_rest_run_1_component_34_falff task_rest_run_1_component_35_falff task_rest_run_1_component_36_falff task_rest_run_1_component_37_falff task_rest_run_1_component_38_falff task_rest_run_1_component_39_falff task_rest_run_1_component_40_falff task_rest_run_1_component_41_falff task_rest_run_1_component_42_falff task_rest_run_1_component_43_falff task_rest_run_1_component_44_falff task_rest_run_1_component_45_falff task_rest_run_1_component_46_falff task_rest_run_1_component_47_falff task_rest_run_1_component_48_falff task_rest_run_1_component_49_falff task_rest_run_1_component_50_falff task_rest_run_1_component_51_falff task_rest_run_1_component_52_falff task_rest_run_1_component_53_falff task_rest_run_1_component_54_falff task_rest_run_1_component_55_falff task_rest_run_1_component_56_falff task_rest_run_1_component_57_falff task_rest_run_1_component_58_falff task_rest_run_1_component_59_falff task_rest_run_1_component_60_falff task_rest_run_1_component_61_falff task_rest_run_1_component_62_falff task_rest_run_1_component_63_falff task_rest_run_1_component_64_falff task_rest_run_1_component_65_falff task_rest_run_1_component_66_falff task_rest_run_1_component_67_falff task_rest_run_1_component_68_falff task_rest_run_1_component_69_falff task_rest_run_1_component_70_falff task_rest_run_1_component_71_falff task_rest_run_1_component_72_falff task_rest_run_1_component_73_falff task_rest_run_1_component_74_falff task_rest_run_1_component_75_falff task_rest_run_1_component_76_falff task_rest_run_1_component_77_falff task_rest_run_1_component_78_falff task_rest_run_1_component_79_falff task_rest_run_1_component_80_falff task_rest_run_1_component_81_falff task_rest_run_1_component_82_falff task_rest_run_1_component_83_falff task_rest_run_1_component_84_falff task_rest_run_1_component_85_falff task_rest_run_1_component_86_falff task_rest_run_1_component_87_falff task_rest_run_1_component_88_falff task_rest_run_1_component_89_falff task_rest_run_1_component_90_falff task_rest_run_1_component_91_falff task_rest_run_1_component_92_falff task_rest_run_1_component_93_falff task_rest_run_1_component_94_falff task_rest_run_1_component_95_falff task_rest_run_1_component_96_falff task_rest_run_1_component_97_falff task_rest_run_1_component_98_falff task_rest_run_1_component_99_falff task_rest_run_1_component_100_falff task_rest_run_1_component_101_falff task_rest_run_1_component_102_falff task_rest_run_1_component_103_falff task_rest_run_1_component_104_falff
10706 0.0786879 0.1015881 0.052049 0.0483026 0.1223071 0.0568095 0.0452842 0.3831314 0.0715804 0.1039394 0.1498999 0.0355465 0.0474389 0.0360338 0.0540700 0.1952123 0.0922157 0.0939778 0.1390081 0.1786949 0.0880629 0.1439291 0.0304071 0.7214057 0.2657748 0.4563607 0.1990452 0.7124557 0.4065492 0.1149822 0.7587397 0.3292898 0.1611246 0.5666743 0.3024356 1.3012945 0.5423628 0.2883330 0.1491495 0.1134375 0.2557162 0.1274462 0.1448448 0.0914177 0.0823300 0.4554486 0.3169977 0.0833417 0.0960591 0.1896266 0.138373 0.2508245 0.2748099 0.0828794 0.0739952 0.0484130 0.0645167 0.5624045 0.1218237 0.0997725 0.1455603 0.2138334 0.0744494 0.2924204 1.076772 0.0771103 0.9745319 0.8880736 0.1337948 0.347344 0.5187176 0.6862605 0.2558009 0.8931359 0.607946 1.2715726 4.309951 1.249261 0.6578997 0.1574201 0.1676942 0.3944165 0.1677946 0.1188327 0.0445988 0.0397639 0.6694421 0.8645875 0.1079284 0.3799769 0.6508472 0.1629966 0.1664630 0.0489054 0.073417 1.083527 0.6251677 0.4091274 0.1363527 0.161935 0.4375215 0.2133081 0.2437414 0.2533421
10734 0.9942507 2.1102403 1.547196 2.4625590 1.5024047 1.8553465 1.8630527 6.0606817 1.5015348 1.5943682 1.1525736 4.7941183 1.4797866 4.6484995 2.4963343 0.7730618 2.3270206 1.4694644 1.0579937 0.3780062 1.9572301 3.4679811 3.7798500 4.3290947 2.4068691 1.4361362 1.3618992 1.0506919 0.9222745 0.7537495 0.9053314 1.1041545 0.9498646 1.0920011 0.7996777 1.0813039 1.7382434 1.0461950 1.2975234 13.9701434 5.1231815 3.8254268 8.3948652 5.5891773 1.9383714 15.1377964 9.8289444 2.5115488 8.4633433 1.6675712 4.756754 1.2106880 8.0202187 2.5689193 1.6050369 2.0395170 11.3548738 1.5583567 0.8717730 2.9291386 1.1284211 4.1822458 1.5452485 6.1828488 6.874893 1.2820714 3.4751416 1.7748792 1.4954024 2.421393 1.4881492 4.3497479 6.7651989 1.4465459 2.504938 0.9376264 4.713245 9.317959 0.9477148 7.0279155 12.9169955 12.8882654 9.5773402 9.2145345 1.7171296 2.6753037 11.4222797 2.3485370 1.7919343 9.4807954 15.8440906 12.8968765 9.6820542 4.9523427 8.998409 6.489865 8.0880772 0.9490143 2.2256835 1.754093 2.1184884 1.0473221 2.2545155 2.2805632
20371 25.5749447 35.7224756 47.023694 29.2602679 29.5763099 13.0700966 27.3131944 29.0478421 14.6215353 22.1768745 41.7050432 1.4000048 1.7113886 2.2577183 2.9330957 1.7422246 2.3254635 1.6944268 2.7044531 0.9278033 1.5989925 1.3898004 2.0139688 4.5990620 1.9900991 3.5056201 2.7241976 5.8188841 0.6733409 1.6557804 5.2041519 0.4715949 0.6614720 0.5253330 0.3557549 4.1036786 3.3945785 2.1984961 3.4723373 3.4962072 4.7737226 3.0541800 5.3535195 6.9037354 3.2124410 8.7068544 6.7377384 5.4432010 3.9362429 5.2871229 5.075163 4.5380525 4.2747413 2.5143122 8.9091814 1.6816211 11.1533066 7.4848479 7.6151316 3.7642946 10.2232145 17.3030953 21.3839954 19.1631844 19.941696 4.5344094 6.2702269 2.6487904 4.8232639 5.105864 21.0235920 10.5076265 22.9399494 18.2594597 12.308736 14.7335989 20.820345 23.668191 14.3007943 6.9532808 16.5451816 10.8850655 8.9673138 17.7020588 8.2101824 3.9443151 8.3471716 2.9248076 7.6769079 9.3928185 8.2193844 4.6354884 8.8486823 12.0432286 11.716801 24.044725 17.4553251 25.0828435 23.0494995 26.421274 5.2569240 9.3275329 22.2273386 21.7283741
20108 18.6251409 3.1417886 12.489172 18.8977753 12.6509848 6.4476009 12.3650452 14.7008763 2.1481520 17.8531686 30.0152961 2.4437188 4.2241237 1.4010559 4.5990861 8.5309692 4.8122363 0.5706313 5.9267987 0.4973967 1.4873994 4.4417717 8.9664500 1.8649188 3.3708499 2.3901969 2.4497818 0.6905165 0.4830677 1.5785550 0.4449965 0.5673039 1.0162321 0.4204853 0.3619325 0.7271523 0.7240155 1.3887666 4.7342407 5.4870320 5.6309731 2.6717269 2.5617677 3.8972948 2.7862887 11.4274187 8.3295237 6.3799863 3.3841354 5.8237647 5.904620 7.1863873 6.3013154 1.1306020 2.7599192 0.9581853 10.8583483 11.0290449 16.1785894 6.0480217 1.9549181 1.5329131 1.8112361 5.6555663 3.047952 2.6708740 4.3666995 2.9124218 1.0410182 1.529801 1.1607579 0.7085752 12.2726830 4.3442416 3.564258 28.5755172 9.354329 1.178745 1.0709303 5.6325308 4.0920851 4.0586992 2.1854832 3.1881345 0.5686186 0.3829533 2.4884346 0.7910992 1.2569983 18.7833101 6.5724580 12.2587359 6.1051981 7.1708133 2.553131 1.622521 15.8567483 2.7604504 5.8545529 4.659103 4.4860115 3.7667571 37.9717441 10.8034622
10689 4.8982010 1.4236021 0.800056 1.3112649 0.9497531 0.6045389 0.8669525 6.7999566 0.7115647 1.3240181 1.5714654 1.0919076 1.1415398 0.6040889 0.7260734 0.5848013 0.6259627 0.5262468 0.5037600 0.5430432 0.5004719 0.8852444 0.4779471 16.1494909 1.1327411 0.6568852 0.6641081 0.3035981 0.1894327 1.1745409 0.2620231 0.3260040 0.1978448 0.2058264 0.3848176 0.2139824 0.3710489 2.2774761 3.5511112 1.8483530 4.6925348 1.8131454 2.7495919 1.9972919 1.0150926 2.2917144 1.7614677 1.1990434 0.8533773 0.7250450 2.418610 5.0206209 1.0729183 0.4206464 3.3935379 0.3003598 3.0941016 2.3796881 1.1290238 3.2210523 1.2453773 4.5433723 0.5043934 5.4808394 8.696819 0.6787812 0.5621154 0.7870903 0.5879351 3.321508 1.8274404 11.3025895 4.5229000 4.1824076 4.755424 8.6326317 6.225141 8.402936 1.1424090 8.9946358 14.0605290 11.7905303 20.9674751 14.5581269 0.3642943 0.2973175 0.4813669 0.2414017 1.6965179 5.1197413 2.6862918 1.2644760 0.8125645 2.2093241 11.396124 10.181205 1.0958561 2.1347624 6.4774805 4.656306 11.7436336 8.8303819 4.6515175 3.6431154
20020 17.2409967 2.5252950 5.199516 4.6340755 3.3336231 2.7513713 4.5937498 6.9096430 1.1342104 30.4941940 17.5514069 1.1133354 0.3793020 0.9090165 0.7270815 0.4793928 1.0078123 0.8848357 0.6620684 0.3889954 1.0319450 0.9244126 1.0063661 1.0086927 1.9070536 0.7699915 0.6979027 0.5940526 0.9497428 0.4843157 0.6160483 0.7675312 0.4592442 0.7381192 0.5883527 0.9145414 0.6995945 0.5791365 0.9574172 0.8199431 0.7445412 0.8871428 0.6728231 1.9786491 0.7111311 0.9194022 1.0333527 0.4637550 0.4121940 1.7460462 1.865285 1.3587674 1.0283474 1.2778628 13.8475735 0.7614329 5.9371546 5.7134020 1.5006237 1.5126430 1.8090558 14.9658967 4.0365297 20.2237074 22.552345 1.6811344 5.1213871 1.3904796 1.4479527 5.822941 0.9788415 3.3105665 5.6032127 7.7194289 6.598247 8.3796876 4.849479 9.768419 2.8364821 3.3928894 3.2180390 6.5159321 4.0093123 2.4757488 1.2992688 1.0498815 1.6185256 1.2782790 2.8467715 5.1559976 2.4635042 1.4991986 1.6414194 1.2584600 4.843212 16.043292 2.0781383 3.5518821 11.7072944 4.167257 20.7569567 12.6051395 9.5004396 1.8304764

4.1.3 Data Quality

As with any MRI derivative, all pipeline derivatives have been included. This means that products were included regardless of their quality, and so some products may have been generated from images that are known to have poor quality—rated “red”, or incomparable. For details on the ratings and how to exclude them, see Appendix A. Additionally, extensive QC has not yet been performed on the derivatives themselves, and so there may be cases where pipelines produced atypical outputs. For an overview of planned checks, see Confluence.

4.2 Considerations While Working on the Project

4.2.1 Data Generation

These image-derived phenotypes were not generated by a single pipeline, but rather aggregated from several validated neuroimaging tools. Structural IDPs were primarily generated using FSL and FreeSurfer, while functional IDPs were derived using the NeuroMark 2.1 independent component analysis pipeline. For deeper dives into how each of these derivatives was produced, see the respective starter kits linked in the introduction.

4.2.2 Other

Variability Across Scanners Many MRI biomarkers exhibit variability across the scanners, which may confound some analyses. For an up-to-date assessment of the issue and overview of current thinking, please see Appendix D.

4.2.3 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
Sadil, P., Arfanakis, K., Bhuiyan, E. H., Caffo, B., Calhoun, V. D., Clauw, D. J., DeLano, M. C., Ford, J. C., Gattu, R., Guo, X., Harris, R. E., Ichesco, E., Johnson, M. A., Jung, H., Kahn, A. B., Kaplan, C. M., Leloudas, N., Lindquist, M. A., Luo, Q., … Chronic Pain Signatures Consortium, T. A. to. (2024). Image processing in the acute to chronic pain signatures (A2CPS) project. bioRxiv. https://doi.org/10.1101/2024.12.19.627509

When using neuroimaging derivatives, please also cite Sadil et al. (2024).