5  FreeSurfer Measures

Cortical Mesh (source: https://freesurfer.net)

One of the core scans in the A2CPS neuroimaging protocol is a T1w scan, which is used to capture structural information. These scans are processed using a software suite called FreeSurfer. FreeSurfer takes the 3D image, segments it into pre-defined anatomical regions (like the hippocampus or amygdala), and also creates a triangular “mesh” of cerebral cortex from which it derives measurements of structural morphometry, including cortical thickness, surface area, and curvature. These measurements are related to a wide range of diseases, including chronic pain, and are often used as a source of features for predictive modeling. A2CPS has compiled the measurements into tabular files, providing a rich set of neuroanatomical variables ready for statistical analysis to explore brain-behavior relationships or group differences. This kit provides an overview of those tabular files.

5.1 Starting Project

5.1.1 Locate data

In the release, data are stored underneath the mris/derivatives folder:

/corral-secure/projects/A2CPS/products/consortium-data/pre-surgery/mris/derivatives/freesurfer

This folder contains all of the FreeSurfer outputs for every participant (e.g., this is the directory that could correspond to the FreeSurfer environment variable $SUBJECTS_DIR), which subject folders of the form sub-[recordid]_ses-[protocolid].

Most users will not need the raw FreeSurfer outputs and can instead rely on tables in which the morphological measures have been aggregated. There are three relevant tables

  • aparc.tsv
    • measurements derived from parcellations of the cortical surface (e.g., cortical thickness)
    • produced by aparcstats2table
  • aseg.tsv
    • measurements derived from segmentations of the 3d structural image (e.g., average signal intensity)
    • produced by asegstats2table
  • headers.tsv
    • information about the brain as a whole (e.g., estimated Total Intracranial Volume)
    • derived from the headers of the aparcstats2table and asegstats2table outputs

For each of these, there are data dictionaries in json files (e.g., aparc.json explains the columns of aparc.tsv).

$ ls /corral-secure/projects/A2CPS/products/consortium-data/pre-surgery/mris/derivatives/freesurfer/*{json,tsv}
/corral-secure/projects/A2CPS/products/consortium-data/pre-surgery/mris/derivatives/freesurfer/aparc.json  /corral-secure/projects/A2CPS/products/consortium-data/pre-surgery/mris/derivatives/freesurfer/gm_morph.tsv
/corral-secure/projects/A2CPS/products/consortium-data/pre-surgery/mris/derivatives/freesurfer/aparc.tsv   /corral-secure/projects/A2CPS/products/consortium-data/pre-surgery/mris/derivatives/freesurfer/headers.json
/corral-secure/projects/A2CPS/products/consortium-data/pre-surgery/mris/derivatives/freesurfer/aseg.json   /corral-secure/projects/A2CPS/products/consortium-data/pre-surgery/mris/derivatives/freesurfer/headers.tsv
/corral-secure/projects/A2CPS/products/consortium-data/pre-surgery/mris/derivatives/freesurfer/aseg.tsv

Copies of the data dictionaries are available online for preview: aparc.json, aseg.json, headers.json.

5.1.2 Extract data

In this kit, we will explore the cortical parcellations.

library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)

As described above, the parcellation information is stored in aparc.tsv.

aparc <- read_tsv("data/aparc.tsv", show_col_types = FALSE)
head(aparc)
StructName NumVert SurfArea GrayVol ThickAvg ThickStd MeanCurv GausCurv FoldInd CurvInd sub ses hemisphere parc
bankssts 1360 909 2055 2.516 0.397 0.112 0.020 10 1.1 10706 V1 lh aparc
caudalanteriorcingulate 852 604 1626 2.253 0.887 0.131 0.024 13 1.0 10706 V1 lh aparc
caudalmiddlefrontal 2870 1857 4018 2.164 0.485 0.104 0.018 22 2.2 10706 V1 lh aparc
cuneus 1595 1090 2019 1.738 0.515 0.141 0.027 19 2.0 10706 V1 lh aparc
entorhinal 480 306 1366 3.029 0.816 0.105 0.018 4 0.3 10706 V1 lh aparc
fusiform 3434 2414 8326 2.907 0.603 0.123 0.023 38 3.4 10706 V1 lh aparc

That table contains nine regional measurements (GrayVol, SurfArea) for the various structures that compose each of six parcellations.

A typical analysis will only rely on one parcellation. For details of the different parcellations, see the FreeSurfer documentation. Two good starting choices are either aparc or aparc.a2009s. The aparc atlas is a coarser parcellation, whereas aparc.a2009s is finer. Here, we’ll filter for aparc.a2009s.

a2009s <- aparc |>
  filter(parc == "aparc.a2009s")
head(a2009s)
StructName NumVert SurfArea GrayVol ThickAvg ThickStd MeanCurv GausCurv FoldInd CurvInd sub ses hemisphere parc
G_and_S_frontomargin 828 603 1341 2.098 0.476 0.131 0.026 10 0.9 10706 V1 lh aparc.a2009s
G_and_S_occipital_inf 1116 762 2218 2.474 0.643 0.127 0.025 11 1.1 10706 V1 lh aparc.a2009s
G_and_S_paracentral 1548 858 1810 1.886 0.580 0.097 0.019 10 1.5 10706 V1 lh aparc.a2009s
G_and_S_subcentral 1151 809 2142 2.443 0.413 0.129 0.021 13 1.0 10706 V1 lh aparc.a2009s
G_and_S_transv_frontopol 531 480 1424 2.378 0.585 0.203 0.052 11 1.3 10706 V1 lh aparc.a2009s
G_and_S_cingul-Ant 2005 1481 3804 2.463 0.531 0.140 0.028 31 2.5 10706 V1 lh aparc.a2009s

5.1.3 Example Analysis: Relationship between Cortical Surface Area and Gray Matter Volume

One of the most common features in predictive models is cortical thickness, but each of the morphological measurements may carry unique information. For example, cortical surface area may be more predictive of widespread chronic pain as compared to cortical thickness, which may be more closely related to chronic headaches (Bhatt et al., 2024). Let’s compare the regional volume with the regional surface area.

Bhatt, R. R., Haddad, E., Zhu, A. H., Thompson, P. M., Gupta, A., Mayer, E. A., & Jahanshad, N. (2024). Mapping brain structure variability in chronic pain: The role of widespreadness and pain type and its mediating relationship with suicide attempt. Biological Psychiatry, 95(5), 473–481. https://doi.org/10.1016/j.biopsych.2023.07.016 
a2009s |>
  ggplot(aes(x = SurfArea, y = GrayVol)) +
  geom_point(alpha = 0.1)

Figure 5.1: Cortical Gray Matter Volume (mm^3) and Surface Area (mm^2). Each point represents a single pair of measurements for a single region within a participant.

Clearly, there are groupings in the data. These groupings could partly be driven by participant demographics (e.g., some participants having larger or smaller regional volumes), but they may also be driven by different structures (e.g., the relationship between surface area and volume may differ across regions). Let’s zoom in on two of the larger clusters, and color the points by structure.

a2009s |>
  filter(between(SurfArea, 4000, 6000), between(GrayVol, 10000, 20000)) |>
  ggplot(aes(x = SurfArea, y = GrayVol, color = StructName)) +
  geom_point()

Figure 5.2: The data are plotted as in Figure 5.1, but with a restricted axis. Note the apparent variability in the relationship between surface area and volume.

The relationship between surface area and volume appears to vary by region. For example, the Superior Temporal Sulcus has a similar surface area to the Superior Frontal Gyrus, but that gyrus has a larger volume.

5.1.4 Pivoting the Data

The data have been shared in a “longer” format, with rows corresponding to region-level observations. Some analyses benefit from having the data in a “wider” format, with rows corresponding to participant-level observations. For example, if we’re predicting age from cortical thickness, we may want to drop the other morphological measures, and pivot the table such that columns correspond to regional thickness.

Table 5.1: Average Thickness in a Wider Format. Rows correspond to participants. Note that the region (StructName) has been combined with the hemisphere label. 
a2009s |>
  select(StructName, ThickAvg, sub, hemisphere) |>
  pivot_wider(
    names_from = c(StructName, hemisphere),
    values_from = ThickAvg
  ) |>
  head()
sub G_and_S_frontomargin_lh G_and_S_occipital_inf_lh G_and_S_paracentral_lh G_and_S_subcentral_lh G_and_S_transv_frontopol_lh G_and_S_cingul-Ant_lh G_and_S_cingul-Mid-Ant_lh G_and_S_cingul-Mid-Post_lh G_cingul-Post-dorsal_lh G_cingul-Post-ventral_lh G_cuneus_lh G_front_inf-Opercular_lh G_front_inf-Orbital_lh G_front_inf-Triangul_lh G_front_middle_lh G_front_sup_lh G_Ins_lg_and_S_cent_ins_lh G_insular_short_lh G_occipital_middle_lh G_occipital_sup_lh G_oc-temp_lat-fusifor_lh G_oc-temp_med-Lingual_lh G_oc-temp_med-Parahip_lh G_orbital_lh G_pariet_inf-Angular_lh G_pariet_inf-Supramar_lh G_parietal_sup_lh G_postcentral_lh G_precentral_lh G_precuneus_lh G_rectus_lh G_subcallosal_lh G_temp_sup-G_T_transv_lh G_temp_sup-Lateral_lh G_temp_sup-Plan_polar_lh G_temp_sup-Plan_tempo_lh G_temporal_inf_lh G_temporal_middle_lh Lat_Fis-ant-Horizont_lh Lat_Fis-ant-Vertical_lh Lat_Fis-post_lh Pole_occipital_lh Pole_temporal_lh S_calcarine_lh S_central_lh S_cingul-Marginalis_lh S_circular_insula_ant_lh S_circular_insula_inf_lh S_circular_insula_sup_lh S_collat_transv_ant_lh S_collat_transv_post_lh S_front_inf_lh S_front_middle_lh S_front_sup_lh S_interm_prim-Jensen_lh S_intrapariet_and_P_trans_lh S_oc_middle_and_Lunatus_lh S_oc_sup_and_transversal_lh S_occipital_ant_lh S_oc-temp_lat_lh S_oc-temp_med_and_Lingual_lh S_orbital_lateral_lh S_orbital_med-olfact_lh S_orbital-H_Shaped_lh S_parieto_occipital_lh S_pericallosal_lh S_postcentral_lh S_precentral-inf-part_lh S_precentral-sup-part_lh S_suborbital_lh S_subparietal_lh S_temporal_inf_lh S_temporal_sup_lh S_temporal_transverse_lh G_and_S_frontomargin_rh G_and_S_occipital_inf_rh G_and_S_paracentral_rh G_and_S_subcentral_rh G_and_S_transv_frontopol_rh G_and_S_cingul-Ant_rh G_and_S_cingul-Mid-Ant_rh G_and_S_cingul-Mid-Post_rh G_cingul-Post-dorsal_rh G_cingul-Post-ventral_rh G_cuneus_rh G_front_inf-Opercular_rh G_front_inf-Orbital_rh G_front_inf-Triangul_rh G_front_middle_rh G_front_sup_rh G_Ins_lg_and_S_cent_ins_rh G_insular_short_rh G_occipital_middle_rh G_occipital_sup_rh G_oc-temp_lat-fusifor_rh G_oc-temp_med-Lingual_rh G_oc-temp_med-Parahip_rh G_orbital_rh G_pariet_inf-Angular_rh G_pariet_inf-Supramar_rh G_parietal_sup_rh G_postcentral_rh G_precentral_rh G_precuneus_rh G_rectus_rh G_subcallosal_rh G_temp_sup-G_T_transv_rh G_temp_sup-Lateral_rh G_temp_sup-Plan_polar_rh G_temp_sup-Plan_tempo_rh G_temporal_inf_rh G_temporal_middle_rh Lat_Fis-ant-Horizont_rh Lat_Fis-ant-Vertical_rh Lat_Fis-post_rh Pole_occipital_rh Pole_temporal_rh S_calcarine_rh S_central_rh S_cingul-Marginalis_rh S_circular_insula_ant_rh S_circular_insula_inf_rh S_circular_insula_sup_rh S_collat_transv_ant_rh S_collat_transv_post_rh S_front_inf_rh S_front_middle_rh S_front_sup_rh S_interm_prim-Jensen_rh S_intrapariet_and_P_trans_rh S_oc_middle_and_Lunatus_rh S_oc_sup_and_transversal_rh S_occipital_ant_rh S_oc-temp_lat_rh S_oc-temp_med_and_Lingual_rh S_orbital_lateral_rh S_orbital_med-olfact_rh S_orbital-H_Shaped_rh S_parieto_occipital_rh S_pericallosal_rh S_postcentral_rh S_precentral-inf-part_rh S_precentral-sup-part_rh S_suborbital_rh S_subparietal_rh S_temporal_inf_rh S_temporal_sup_rh S_temporal_transverse_rh
10706 2.098 2.474 1.886 2.443 2.378 2.463 2.445 2.534 2.532 2.524 1.669 2.481 2.420 2.099 2.239 2.400 2.852 3.408 2.156 2.121 2.971 1.903 2.697 2.380 2.142 2.550 1.817 1.848 2.371 2.196 2.159 2.267 2.179 2.897 3.292 2.471 2.957 2.683 1.688 1.982 2.160 1.851 3.500 1.601 1.646 1.756 2.585 2.545 2.344 2.892 1.939 2.058 1.990 2.073 1.959 1.878 1.843 1.698 2.021 2.702 2.294 1.998 2.186 2.444 1.947 1.429 2.086 2.166 2.099 2.098 2.134 2.398 2.359 2.337 2.316 2.112 1.715 2.466 2.542 2.462 2.429 2.348 2.722 2.145 1.736 2.361 2.381 2.222 2.153 2.392 3.494 3.292 2.324 1.759 2.829 1.966 2.858 2.613 2.285 2.430 1.827 1.740 2.227 2.035 2.136 2.505 2.048 2.859 3.178 2.231 2.638 2.809 1.878 1.938 2.346 1.942 3.461 1.479 1.630 1.940 2.644 2.709 2.369 3.315 2.103 1.880 2.097 1.956 2.364 1.841 1.870 1.840 2.208 2.126 2.256 1.793 2.458 2.636 1.884 1.368 1.765 2.217 2.097 2.145 2.283 2.536 2.348 2.297
10734 2.463 2.110 1.959 2.483 2.443 2.410 2.357 2.361 2.668 2.103 1.682 2.520 2.550 2.414 2.465 2.584 2.835 3.001 2.161 1.906 2.592 1.842 2.948 2.572 2.414 2.490 2.136 1.759 2.500 2.349 2.404 2.320 2.206 3.044 2.830 2.496 2.826 2.822 1.729 2.251 1.919 1.465 3.147 1.613 1.852 1.763 2.738 2.267 2.180 2.375 1.613 2.051 2.241 2.340 2.009 2.000 1.728 1.929 2.082 2.343 2.115 2.010 2.056 2.486 1.776 1.347 1.924 2.212 2.279 1.969 2.202 2.313 2.303 2.139 2.454 2.216 1.800 2.267 2.364 2.467 2.474 2.410 2.478 2.323 1.529 2.683 2.504 2.533 2.519 2.564 3.210 2.871 2.352 1.914 2.609 1.929 2.998 2.643 2.418 2.433 2.043 1.747 2.488 2.344 2.473 2.573 2.227 2.935 3.035 2.297 2.780 2.682 1.869 2.250 2.056 1.671 3.071 1.598 1.712 1.949 2.421 2.002 2.365 2.199 1.703 2.178 2.260 2.338 2.129 1.847 1.721 1.839 1.903 2.125 2.029 2.123 2.222 2.488 1.924 1.454 1.783 2.243 2.223 1.669 2.321 2.283 2.206 1.777
20371 2.498 2.080 2.218 2.804 2.722 2.690 2.394 2.328 2.520 2.367 1.757 2.570 2.602 2.463 2.496 2.611 3.370 3.746 2.028 1.820 2.795 1.917 2.864 2.680 2.608 2.666 2.199 1.985 2.593 2.470 2.379 2.572 2.651 3.030 3.124 2.709 2.750 3.101 1.714 2.197 2.380 1.694 3.276 1.723 1.885 2.165 2.564 2.951 2.371 2.657 2.008 2.022 2.088 2.183 2.705 2.094 1.664 1.848 2.032 2.591 2.380 2.208 2.165 2.689 2.165 1.553 2.117 2.262 2.161 2.611 2.225 2.652 2.461 2.404 2.405 2.615 2.079 2.522 2.431 2.537 2.252 2.325 2.645 2.415 1.583 2.650 2.496 2.285 2.403 2.605 3.346 3.601 2.128 1.915 2.648 1.912 2.948 2.548 2.671 2.509 2.293 1.955 2.397 2.416 2.668 2.713 2.523 2.940 3.145 2.521 2.736 2.956 2.127 2.363 2.290 1.621 3.067 1.695 1.788 2.314 2.900 2.658 2.485 2.501 1.979 2.128 2.161 2.139 2.051 2.150 1.652 2.002 2.242 2.441 2.276 2.087 2.212 2.540 2.155 1.335 2.132 2.353 2.179 2.147 2.135 2.507 2.489 2.502
20108 2.037 2.204 2.025 2.509 2.350 2.638 2.351 2.386 2.592 2.042 1.607 2.625 2.721 2.549 2.454 2.704 3.342 3.520 1.904 1.731 2.637 1.804 2.815 2.535 2.381 2.566 1.986 1.738 2.509 2.374 2.408 2.677 2.555 2.787 2.887 2.453 2.550 2.433 1.999 2.000 2.549 1.767 3.484 1.893 1.873 2.146 2.863 2.445 2.332 2.570 2.145 2.216 2.242 2.414 1.928 2.063 1.641 2.268 2.104 2.524 2.089 2.003 1.947 2.569 1.977 1.539 2.015 2.376 2.340 2.829 2.352 2.260 2.291 1.988 2.100 1.873 2.009 2.578 2.419 2.631 2.449 2.452 2.666 2.108 1.584 2.702 2.392 2.554 2.449 2.644 3.491 3.654 1.678 1.540 2.863 1.947 3.140 2.701 1.812 2.475 1.737 1.845 2.602 2.420 2.782 2.690 2.868 3.179 3.027 2.526 2.646 2.685 2.182 2.197 2.270 1.517 3.187 1.835 1.958 2.314 2.580 2.332 2.350 2.952 2.236 2.142 2.097 2.243 2.177 1.915 2.010 2.018 2.018 2.454 2.156 1.851 2.276 2.679 2.045 1.294 2.016 2.156 2.104 2.174 2.294 2.295 2.384 2.609
10689 2.345 2.583 2.608 2.687 2.610 2.623 2.684 2.545 2.691 2.234 1.962 2.591 2.724 2.583 2.717 2.899 3.240 3.609 2.442 1.845 2.933 1.905 3.182 2.940 2.673 2.870 2.453 2.343 2.955 2.615 2.394 2.531 2.287 3.181 3.027 2.703 3.118 3.226 2.061 2.427 2.344 1.840 3.380 1.668 2.004 2.186 2.830 2.620 2.625 2.468 2.123 2.352 2.258 2.474 2.269 2.283 1.823 2.255 2.312 2.395 2.624 2.292 2.224 2.531 2.327 1.340 2.321 2.452 2.378 2.271 2.500 2.506 2.473 2.629 2.515 2.916 2.410 2.480 2.688 2.589 2.584 2.578 2.885 2.423 1.789 2.744 2.604 2.590 2.674 2.959 3.512 3.546 2.732 2.115 2.735 2.166 3.210 2.994 2.656 2.857 2.509 2.286 2.874 2.617 2.564 2.791 2.548 3.309 3.292 2.520 2.986 3.309 2.311 2.370 2.452 1.833 3.422 1.892 2.015 2.269 2.597 2.487 2.745 2.415 2.194 2.247 2.414 2.610 2.199 2.221 2.089 2.210 2.323 2.408 2.473 2.211 2.229 2.627 2.333 1.701 2.173 2.507 2.394 2.447 2.542 2.513 2.538 2.878
20020 2.174 1.939 2.221 2.647 2.285 2.480 2.376 2.230 2.691 2.316 1.839 2.571 2.639 2.492 2.392 2.645 3.024 3.446 2.271 1.817 2.633 1.922 3.039 2.719 2.542 2.691 2.331 2.113 2.698 2.556 2.578 2.367 2.335 2.973 3.308 2.368 2.857 3.071 2.407 2.082 2.283 1.696 3.297 1.697 1.898 1.951 2.458 2.292 2.405 2.883 1.624 2.066 1.927 2.245 2.308 2.022 1.779 1.844 2.055 2.198 2.288 1.773 2.102 2.493 1.997 1.399 2.134 2.143 2.195 2.036 2.128 2.405 2.392 1.951 2.379 2.182 2.360 2.670 2.211 2.445 2.185 2.361 2.840 2.615 1.834 2.545 2.475 2.005 2.511 2.608 3.096 3.468 2.531 2.030 2.639 1.907 2.977 2.760 2.483 2.579 2.241 2.107 2.909 2.398 2.629 2.576 2.572 3.021 3.178 2.420 2.895 2.973 2.195 2.464 2.276 1.748 3.388 1.782 1.826 2.057 2.591 2.321 2.555 2.626 1.659 2.128 1.991 2.214 2.098 1.940 1.970 1.942 2.161 2.534 2.189 1.899 2.434 2.581 1.897 1.570 1.994 2.275 2.271 1.827 2.143 2.310 2.375 2.440

5.1.5 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 Confluence.

5.1.6 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.

Currently, no QC has been performed on the underlying segmentations and parcellations. With adequate sample sizes, results based on whole-atlas FreeSurfer measurements are generally robust, although there are individual regions that may warrant close inspection (e.g., McCarthy et al. 2015, Vahermaa et al. 2023). Moreover, it remains unclear the extent to which FreeSurfer’s accuracy persists over the wide range of demographics found in the A2CPS dataset. The Imaging DIRC is actively exploring these aspects of quality.

5.1.7 Methods

FreeSurfer outputs were generated by the fMRIPrep pipeline. The fMRIPrep pipeline is similar to a typical call to FreeSurfer’s recon-all, with a majority of changes aiming to parallelize more parts of recon-all. However, there are differences, such as in how the brain is masked. The A2CPS use of fMRIPrep is even more different because it entails replacing the fMRIPrep masking procedure with SynthStrip. In general, these changes are expected to only improve the quality of the FreeSurfer outputs. However, they may hinder some comparisons between results derived from A2CPS and those in other studies.

5.1.8 Citations

The FreeSurfer package has been developed through extensive research. If you use these derivatives in your analyses, please follow the documentation for citing FreeSurfer: https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferMethodsCitation.

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).