[[8]{.chapter-number}  [Voxel-Based Morphometry]{.chapter-title}]{#sec-cat12 .quarto-section-identifier}

8 Voxel-Based Morphometry

This starter kit walks through the voxel-based morphometry (VBM) outputs generated by the Computational Anatomy Toolbox (CAT12).

Voxel-Based Morphometry is an automated technique that uses statistics to identify differences in brain anatomy (most commonly gray matter density or volume) between groups of subjects. It achieves this by registering all structural images to a common stereotaxic space and then comparing voxels across subjects. This makes it an incredibly powerful tool for investigating structural brain differences associated with chronic pain or evaluating structural changes resulting from interventions. For more foundational knowledge on VBM, see the classic Ashburner and Friston (2000) paper.

8.1 Starting Project

8.1.1 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 morphometry measures are underneath derivatives/cat12.

8.1.2 Extract data

Data for each participant is stored in a sub-directory

$ ls -d mris/derivatives/cat12/sub* | head
mris/derivatives/cat12/sub-10003
mris/derivatives/cat12/sub-10005
mris/derivatives/cat12/sub-10008
mris/derivatives/cat12/sub-10009
mris/derivatives/cat12/sub-10010
mris/derivatives/cat12/sub-10011
mris/derivatives/cat12/sub-10013
mris/derivatives/cat12/sub-10014
mris/derivatives/cat12/sub-10015
mris/derivatives/cat12/sub-10017

For example:

$ tree mris/derivatives/cat12/sub-10003
mris/derivatives/cat12/sub-10003
└── ses-V1
    ├── label
    │   ├── catROI_sub-10003_ses-V1_T1w.mat
    │   └── catROI_sub-10003_ses-V1_T1w.xml
    ├── mri
    │   ├── misub-10003_ses-V1_T1w.nii
    │   ├── msub-10003_ses-V1_T1w.nii
    │   ├── mwp1sub-10003_ses-V1_T1w.nii
    │   ├── mwp2sub-10003_ses-V1_T1w.nii
    │   ├── mwp3sub-10003_ses-V1_T1w.nii
    │   ├── p0sub-10003_ses-V1_T1w.nii
    │   ├── p1sub-10003_ses-V1_T1w.nii
    │   ├── p2sub-10003_ses-V1_T1w.nii
    │   ├── p3sub-10003_ses-V1_T1w.nii
    │   ├── wmisub-10003_ses-V1_T1w.nii
    │   ├── wmsub-10003_ses-V1_T1w.nii
    │   ├── wp0sub-10003_ses-V1_T1w.nii
    │   ├── wp1sub-10003_ses-V1_T1w.nii
    │   ├── wp2sub-10003_ses-V1_T1w.nii
    │   ├── wp3sub-10003_ses-V1_T1w.nii
    │   └── y_sub-10003_ses-V1_T1w.nii
    ├── report
    │   ├── catlog_sub-10003_ses-V1_T1w.txt
    │   ├── cat_sub-10003_ses-V1_T1w.mat
    │   └── cat_sub-10003_ses-V1_T1w.xml
    └── surf
        ├── lh.central.sub-10003_ses-V1_T1w.gii
        ├── lh.pbt.sub-10003_ses-V1_T1w
        ├── lh.pial.sub-10003_ses-V1_T1w.gii
        ├── lh.sphere.reg.sub-10003_ses-V1_T1w.gii
        ├── lh.sphere.sub-10003_ses-V1_T1w.gii
        ├── lh.thickness.sub-10003_ses-V1_T1w
        ├── lh.white.sub-10003_ses-V1_T1w.gii
        ├── rh.central.sub-10003_ses-V1_T1w.gii
        ├── rh.pbt.sub-10003_ses-V1_T1w
        ├── rh.pial.sub-10003_ses-V1_T1w.gii
        ├── rh.sphere.reg.sub-10003_ses-V1_T1w.gii
        ├── rh.sphere.sub-10003_ses-V1_T1w.gii
        ├── rh.thickness.sub-10003_ses-V1_T1w
        └── rh.white.sub-10003_ses-V1_T1w.gii

5 directories, 35 files

Additionally, there is a table of “cluster volumes”, which contains information relevant to A2CPS primary and secondary biomarkers.

$ ls mris/derivatives/cat12/cluster*
mris/derivatives/cat12/cluster_volumes.json  mris/derivatives/cat12/cluster_volumes.tsv

$ head mris/derivatives/cat12/cluster_volumes.tsv 
sub ses mri atlas   cluster volume
10703   V1  wp1sub-10703_ses-V1_T1w tpl-MNI152NLin2009cAsym_atlas-smallwood_dseg    0   1998.106000518892
10703   V1  wp1sub-10703_ses-V1_T1w tpl-MNI152NLin2009cAsym_atlas-smallwood_dseg    1   2294.6824886463583
10703   V1  mwp1sub-10703_ses-V1_T1w    tpl-MNI152NLin2009cAsym_atlas-smallwood_dseg    0   1219.69091796875
10703   V1  mwp1sub-10703_ses-V1_T1w    tpl-MNI152NLin2009cAsym_atlas-smallwood_dseg    1   1443.6837158203125
10753   V1  mwp1sub-10753_ses-V1_T1w    tpl-MNI152NLin2009cAsym_atlas-smallwood_dseg    0   1673.0926513671875
10753   V1  mwp1sub-10753_ses-V1_T1w    tpl-MNI152NLin2009cAsym_atlas-smallwood_dseg    1   1950.593505859375
10753   V1  wp1sub-10753_ses-V1_T1w tpl-MNI152NLin2009cAsym_atlas-smallwood_dseg    0   2353.910433325509
10753   V1  wp1sub-10753_ses-V1_T1w tpl-MNI152NLin2009cAsym_atlas-smallwood_dseg    1   2952.608998143696
10040   V1  mwp1sub-10040_ses-V1_T1w    tpl-MNI152NLin2009cAsym_atlas-smallwood_dseg    0   1499.8414306640625

The json file contains a data dictionary, with links to confluence pages providing detailed information about each of the atlases.

{
    "sub": {
        "LongName": "Subject",
        "Description": "Study Participant, BIDS Subject ID",
        "TermURL": "https://bids-specification.readthedocs.io/en/v1.9.0/appendices/entities.html#sub"
    },
    "ses": {
        "LongName": "Session",
        "Description": "Visit, Protocol, BIDS Session ID",
        "Levels": {
            "V1": "baseline_visit",
            "V3": "3mo_postop"
        },
        "TermURL": "https://bids-specification.readthedocs.io/en/v1.9.0/appendices/entities.html#ses"
    },
    "mri": {
        "LongName": "MRI Filename",
        "Description": "Product of CAT12 in which volumes were calculated",
        "TermURL": "https://neuro-jena.github.io/cat12-help/#naming"
    },
    "atlas": {
        "LongName": "Atlas of Clusters",
        "Description": "Source file in which clusters are defined",
        "Levels": {
            "tpl-MNI152NLin2009cAsym_atlas-smallwood_dseg": "https://a2cps.atlassian.net/wiki/external/NjFmMGViNjA1OGI4NGIxNWJlMjk0ZmM5ODcyNDhhODc",
            "tpl-MNI152NLin2009cAsym_atlas-henn_desc-controlspatientgmtfce05_dseg": "https://doi.org/10.1097/j.pain.0000000000002681, Table 4"
        }
    },
    "cluster": {
        "LongName": "Cluster Number",
        "Description": "ID of cluster as reported by FSL. May not match ID in source paper."
    },
    "volume": {
        "LongName": "Cluster Volume",
        "Description": "Volume of cluster_id from atlas_id in mri_id calculated as number of voxels times voxel volume."
    }
}

8.2 Considerations While Working on the Project

8.2.1 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.

8.2.2 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.

8.2.3 Data Generation (CAT12 Configuration)

For details on CAT12’s methods see their documentation. CAT12 (12.9, r2582) was run with nearly the default configurations, changing only parameters that cause a few additional outputs to be saved. For the full batch script, see GitHub.

8.2.4 CAT12 Image Quality Ratings

Part of the Raw MRI data quality ratings are derived from an “Image Quality Rating” that is provided by CAT12. The ratings were pulled from the xml document in the reports subfolder. For example

$ grep -P "<IQR>|\(IQR\)" sub-10003/ses-V1/report/cat_sub-10003_ses-V1_T1w.xml
      <IQR>1.85445149536238</IQR>
      <item>Image Quality Rating (IQR): 86.46% (B)</item>

The version of the IQR reported as a percentage is equal to \(105 - 10*IQR\).

Note that CAT12 typically generates a PDF with an overview of processing and image quality. There is an ongoing issue with CAT12 that prevented the PDFs from being generated for Release 2.0.0. For details, see the associated issue on the CAT12 GitHub Repo.

8.2.5 Citations

The methods driving CAT12 are based on extensive research. If you use these results, please be sure to cite the relevant papers outlined here: https://neuro-jena.github.io/cat/index.html#About.

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).