References
Arrieta, J., Aguerrebere, M., Raviola, G., Flores, H., Elliott, P.,
Espinosa, A., Reyes, A., Ortiz-Panozo, E., Rodriguez-Gutierrez, E. G.,
Mukherjee, J., Palazuelos, D., & Franke, M. F. (2017). Validity and
Utility of the Patient Health Questionnaire (PHQ)-2 and PHQ-9 for
Screening and Diagnosis of Depression in Rural Chiapas, Mexico: A
Cross-Sectional Study. Journal of Clinical Psychology,
73(9), 1076–1090. https://doi.org/10.1002/jclp.22390
Baliki, M. N., Petre, B., Torbey, S., Herrmann, K. M., Huang, L.,
Schnitzer, T. J., Fields, H. L., & Apkarian, A. V. (2012).
Corticostriatal functional connectivity predicts transition to chronic
back pain. Nature Neuroscience, 15(8), 1117–1119. https://doi.org/10.1038/nn.3153
Bashyam, V. M., Erus, G., Doshi, J., Habes, M., Nasrallah, I. M.,
Truelove-Hill, M., Srinivasan, D., Mamourian, L., Pomponio, R., Fan, Y.,
et al. (2020). MRI signatures of brain age and disease over the lifespan
based on a deep brain network and 14 468 individuals worldwide.
Brain, 143(7), 2312–2324. https://doi.org/10.1093/brain/awaa160
Bayman, E. O. (2017). Pain-Related Limitations in Daily Activities
Following Thoracic Surgery in a United States Population. Pain
Physician, 3(20;3), E367–E378. https://doi.org/10.36076/ppj.2017.e378
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
Biondo, F., Jewell, A., Pritchard, M., Aarsland, D., Steves, C. J.,
Mueller, C., & Cole, J. H. (2022). Brain-age is associated with
progression to dementia in memory clinic patients. NeuroImage:
Clinical, 36, 103175. https://doi.org/10.1016/j.nicl.2022.103175
Bridgeford, E. W., Wang, S., Wang, Z., Xu, T., Craddock, C., Dey, J.,
Kiar, G., Gray-Roncal, W., Colantuoni, C., Douville, C., et al. (2021).
Eliminating accidental deviations to minimize generalization error and
maximize replicability: Applications in connectomics and genomics.
PLoS Computational Biology, 17(9), e1009279. https://doi.org/10.1371/journal.pcbi.1009279
Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R., &
Kupfer, D. J. (1989). The Pittsburgh sleep quality index: A new
instrument for psychiatric practice and research. Psychiatry
Research, 28(2), 193–213. https://doi.org/10.1016/0165-1781(89)90047-4
Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. J. (2001). A
method for making group inferences from functional MRI data using
independent component analysis. Human Brain Mapping,
14(3), 140–151. https://doi.org/10.1002/hbm.1048
Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S.,
Amtmann, D., Bode, R., Buysse, D., Choi, S., Cook, K., DeVellis, R.,
DeWalt, D., Fries, J. F., Gershon, R., Hahn, E. A., Lai, J.-S.,
Pilkonis, P., Revicki, D., … Hays, R. (2010). The Patient-Reported
Outcomes Measurement Information System (PROMIS) developed and tested
its first wave of adult self-reported health outcome item banks:
20052008. Journal of Clinical Epidemiology,
63(11), 1179–1194. https://doi.org/10.1016/j.jclinepi.2010.04.011
Clausen, A. N., Fercho, K. A., Monsour, M., Disner, S., Salminen, L.,
Haswell, C. C., Rubright, E. C., Watts, A. A., Buckley, M. N.,
Maron-Katz, A., et al. (2022). Assessment of brain age in posttraumatic
stress disorder: Findings from the ENIGMA PTSD and brain age working
groups. Brain and Behavior, 12(1), e2413. https://doi.org/10.1002/brb3.2413
Cleeland CS Ryan KM. Pain assessment: global use of the Brief Pain
Inventory. Ann Acad Med Singapore (1994 Mar) 23(2):129-38. (1995).
Rehabilitation Oncology, 13(1), 29–30. https://doi.org/10.1097/01893697-199513010-00022
Cox, R. W., Ashburner, J., Breman, H., Fissell, K., Haselgrove, C.,
Holmes, C. J., Lancaster, J. L., Rex, D. E., Smith, S. M., Woodward, J.
B., et al. (2004). A (sort of) new image data format standard: NiFTI-1.
10th Annual Meeting of the Organization for Human Brain
Mapping, 22, 01. https://nifti.nimh.nih.gov/pub/dist/doc/hbm_nifti_2004.pdf
Dadi, K., Varoquaux, G., Machlouzarides-Shalit, A., Gorgolewski, K. J.,
Wassermann, D., Thirion, B., & Mensch, A. (2020). Fine-grain atlases
of functional modes for fMRI analysis. NeuroImage,
221, 117126. https://doi.org/10.1016/j.neuroimage.2020.117126
Darnall, B. D., Sturgeon, J. A., Cook, K. F., Taub, C. J., Roy, A.,
Burns, J. W., Sullivan, M., & Mackey, S. C. (2017). Development and
Validation of a Daily Pain Catastrophizing Scale. The Journal of
Pain, 18(9), 1139–1149. https://doi.org/10.1016/j.jpain.2017.05.003
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
Du, Y., & Fan, Y. (2013). Group information guided ICA for fMRI data
analysis. Neuroimage, 69, 157–197. https://doi.org/10.1016/j.neuroimage.2012.11.008
Du, Y., Fu, Z., Sui, J., Gao, S., Xing, Y., Lin, D., Salman, M., Abrol,
A., Rahaman, M. A., Chen, J., et al. (2020). NeuroMark: An automated and
adaptive ICA based pipeline to identify reproducible fMRI markers of
brain disorders. NeuroImage: Clinical, 28, 102375. https://doi.org/10.1016/j.nicl.2020.102375
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
Felitti, V. J., Anda, R. F., Nordenberg, D., Williamson, D. F., Spitz,
A. M., Edwards, V., & Marks, J. S. (1998). Relationship of childhood
abuse and household dysfunction to many of the leading causes of death
in adults: The adverse childhood experiences (ACE) study. American
Journal of Preventive Medicine, 14(4), 245–258. https://doi.org/10.1016/S0749-3797(98)00017-8
Freynhagen, R., Baron, R., Gockel, U., & Tölle, T. R. (2006).
painDETECT: a new screening questionnaire to
identify neuropathic components in patients with back pain. Current
Medical Research and Opinion, 22(10), 1911–1920. https://doi.org/10.1185/030079906x132488
Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S.,
Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O.,
et al. (2016). The brain imaging data structure, a format for organizing
and describing outputs of neuroimaging experiments. Scientific
Data, 3(1), 1–9. https://doi.org/10.1038/sdata.2016.44
Hahn, E. A., DeVellis, R. F., Bode, R. K., Garcia, S. F., Castel, L. D.,
Eisen, S. V., Bosworth, H. B., Heinemann, A. W., Rothrock, N., Cella,
D., et al. (2010). Measuring social health in the patient-reported
outcomes measurement information system (PROMIS): Item bank development
and testing. Quality of Life Research, 19(7),
1035–1044. https://doi.org/10.1007/s11136-010-9654-0
Halchenko, Y. O., Goncalves, M., Ghosh, S., Velasco, P., Oleggio
Castello, M. V. di, Salo, T., Wodder, J. T., Hanke, M., Sadil, P.,
Gorgolewski, K. J., et al. (2024). HeuDiConv—flexible DICOM conversion
into structured directory layouts. Journal of Open Source
Software, 9(99), 5839. https://doi.org/10.21105/joss.05839
Hobday, H., Cole, J. H., Stanyard, R. A., Daws, R. E., Giampietro, V.,
O’Daly, O., Leech, R., & Váša, F. (2022). Tissue volume estimation
and age prediction using rapid structural brain scans. Scientific
Reports, 12(1), 12005.
Iraji, A., Fu, Z., Faghiri, A., Duda, M., Chen, J., Rachakonda, S.,
DeRamus, T., Kochunov, P., Adhikari, B. M., Belger, A., et al. (2023).
Identifying canonical and replicable multi-scale intrinsic connectivity
networks in 100k+ resting-state fMRI datasets. Human Brain
Mapping, 44(17), 5729–5748. https://doi.org/10.1002/hbm.26472
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
Kircanski, K., LeMoult, J., Ordaz, S., & Gotlib, I. H. (2017).
Investigating the nature of co-occurring depression and anxiety:
Comparing diagnostic and dimensional research approaches. Journal of
Affective Disorders, 216, 123–135. https://doi.org/10.1016/j.jad.2016.08.006
Korponay, C., Janes, A. C., & Frederick, B. B. (2024). Brain-wide
functional connectivity artifactually inflates throughout functional
magnetic resonance imaging scans. Nature Human Behaviour,
8(8), 1568–1580. https://doi.org/10.1038/s41562-024-01908-6
Kratz, A. L., Schilling, S. G., Goesling, J., & Williams, D. A.
(2015). Development and Initial Validation of a Brief Self-Report
Measure of Cognitive Dysfunction in Fibromyalgia. The Journal of
Pain, 16(6), 527–536. https://doi.org/10.1016/j.jpain.2015.02.008
Kratz, A. L., Schilling, S., Goesling, J., & Williams, D. A. (2016).
The PROMIS FatigueFM profile: A self-report measure of fatigue for use
in fibromyalgia. Quality of Life Research, 25(7),
1803–1813. https://doi.org/10.1007/s11136-016-1230-9
Kroenke, K., Strine, T. W., Spitzer, R. L., Williams, J. B. W., Berry,
J. T., & Mokdad, A. H. (2009a). The PHQ-8 as a measure of current
depression in the general population. Journal of Affective
Disorders, 114(1-3), 163–173. https://doi.org/10.1016/j.jad.2008.06.026
Kroenke, K., Strine, T. W., Spitzer, R. L., Williams, J. B., Berry, J.
T., & Mokdad, A. H. (2009b). The PHQ-8 as a measure of current
depression in the general population. Journal of Affective
Disorders, 114(1-3), 163–173. https://doi.org/10.1016/j.jad.2008.06.026
Li, X., Morgan, P. S., Ashburner, J., Smith, J., & Rorden, C.
(2016). The first step for neuroimaging data analysis: DICOM to NIfTI
conversion. Journal of Neuroscience Methods, 264,
47–56. https://doi.org/10.1016/j.jneumeth.2016.03.001
McWilliams, L. A., Kowal, J., & Wilson, K. G. (2015). Development
and evaluation of short forms of the Pain Catastrophizing Scale and the
Pain Self-efficacy Questionnaire. European Journal of Pain,
19(9), 1342–1349. https://doi.org/10.1002/ejp.665
McWilliams, L., Kowal, J., & Wilson, K. (2015). Development and
evaluation of short forms of the pain catastrophizing scale and the pain
self-efficacy questionnaire. European Journal of Pain,
19(9), 1342–1349. https://doi.org/10.1002/ejp.665
Montesino-Goicolea, S., Valdes-Hernandez, P., Nodarse, C. L., Johnson,
A. J., Cole, J. H., Antoine, L. H., Goodin, B. R., Fillingim, R. B.,
& Cruz-Almeida, Y. (2023). Brain-predicted age difference mediates
the association between PROMIS sleep impairment, and self-reported pain
measure in persons with knee pain. Aging Brain, 4,
100088. https://doi.org/10.1016/j.nbas.2023.100088
Nichols, T. E., Das, S., Eickhoff, S. B., Evans, A. C., Glatard, T.,
Hanke, M., Kriegeskorte, N., Milham, M. P., Poldrack, R. A., Poline,
J.-B., et al. (2017). Best practices in data analysis and sharing in
neuroimaging using MRI. Nature Neuroscience, 20(3),
299–303. https://doi.org/10.1038/nn.4500
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
Ringsted, T. K., Wildgaard, K., Kreiner, S., & Kehlet, H. (2013).
Pain-related Impairment of Daily Activities After Thoracic Surgery.
The Clinical Journal of Pain, 29(9), 791–799. https://doi.org/10.1097/ajp.0b013e318278d4e2
Roos, E. M., Roos, H. P., Lohmander, L. S., Ekdahl, C., & Beynnon,
B. D. (1998). Knee Injury and Osteoarthritis Outcome Score
(KOOS)Development of a Self-Administered Outcome Measure.
Journal of Orthopaedic & Sports Physical Therapy,
28(2), 88–96. https://doi.org/10.2519/jospt.1998.28.2.88
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
Sangha, O., Stucki, G., Liang, M. H., Fossel, A. H., & Katz, J. N.
(2003). The self-administered comorbidity questionnaire: A new method to
assess comorbidity for clinical and health services research.
Arthritis Care & Research: Official Journal of the American
College of Rheumatology, 49(2), 156–163. https://doi.org/10.1002/art.10993
Schrepf, A., Williams, D. A., Gallop, R., Naliboff, B. D., Basu, N.,
Kaplan, C., Harper, D. E., Landis, J. R., Clemens, J. Q., Strachan, E.,
et al. (2018). Sensory sensitivity and symptom severity represent unique
dimensions of chronic pain: A MAPP research network study.
Pain, 159(10), 2002–2011. https://doi.org/10.1097/j.pain.0000000000001299
Slepian, P. M., Ankawi, B., Himawan, L. K., & France, C. R. (2016b).
Development and initial validation of the pain resilience scale. The
Journal of Pain, 17(4), 462–472. https://doi.org/10.1016/j.jpain.2015.12.010
Slepian, P. M., Ankawi, B., Himawan, L. K., & France, C. R. (2016a).
Development and Initial Validation of the Pain Resilience Scale. The
Journal of Pain, 17(4), 462–472. https://doi.org/10.1016/j.jpain.2015.12.010
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
Smith, S. M., Vidaurre, D., Alfaro-Almagro, F., Nichols, T. E., &
Miller, K. L. (2019). Estimation of brain age delta from brain imaging.
Neuroimage, 200, 528–539. https://doi.org/10.1016/j.neuroimage.2019.06.017
Soto, C. J., & John, O. P. (2017). Short and extra-short forms of
the Big Five Inventory2: The BFI-2-S and BFI-2-XS.
Journal of Research in Personality, 68, 69–81. https://doi.org/10.1016/j.jrp.2017.02.004
Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006).
A Brief Measure for Assessing Generalized Anxiety Disorder. Archives
of Internal Medicine, 166(10), 1092. https://doi.org/10.1001/archinte.166.10.1092
Tong, Y., Hocke, L. M., & Frederick, B. B. (2019). Low frequency
systemic hemodynamic “noise” in resting state BOLD fMRI:
Characteristics, causes, implications, mitigation strategies, and
applications. Frontiers in Neuroscience, 13, 787. https://doi.org/10.3389/fnins.2019.00787
Topolski, T. D., LoGerfo, J., Patrick, D. L., Williams, B., Walwick, J.,
& Patrick, M. M. B. (2006). The rapid assessment of physical
activity (RAPA) among older adults. Preventing Chronic Disease,
3(4), A118.
Valdes-Hernandez, P. A., Nodarse, C. L., Johnson, A. J.,
Montesino-Goicolea, S., Bashyam, V., Davatzikos, C., Peraza, J. A.,
Cole, J. H., Huo, Z., Fillingim, R. B., et al. (2023). Brain-predicted
age difference estimated using DeepBrainNet is significantly associated
with pain and function—a multi-institutional and multiscanner study.
Pain, 164(12), 2822–2838. https://doi.org/10.1097/j.pain.0000000000002984
Visconti di Oleggio Castello, M., Dobson, J. E., Sackett, T., Kodiweera,
C., Haxby, J. V., Goncalves, M., Ghosh, S., & Halchenko, Y. O.
(2023). ReproNim/reproin: 0.11.6.2. Zenodo. https://doi.org/10.5281/ZENODO.7975330
Waddell, G., Newton, M., Henderson, I., Somerville, D., & Main, C.
J. (1993). A Fear-Avoidance Beliefs Questionnaire (FABQ) and the role of
fear-avoidance beliefs in chronic low back pain and disability.
Pain, 52(2), 157–168. https://doi.org/10.1016/0304-3959(93)90127-b
Wandner, L. D., Domenichiello, A. F., Beierlein, J., Pogorzala, L.,
Aquino, G., Siddons, A., Porter, L., Atkinson, J., & Institute, N.
P. C. (2022). NIH’s helping to end addiction long-termSM initiative (NIH
HEAL initiative) clinical pain management common data element program.
The Journal of Pain, 23(3), 370–378. https://doi.org/10.1016/j.jpain.2021.08.005
Wolfe, F., Clauw, D. J., Fitzcharles, M.-A., Goldenberg, D. L., Häuser,
W., Katz, R. L., Mease, P. J., Russell, A. S., Russell, I. J., &
Walitt, B. (2016). 2016 Revisions to the 2010/2011 fibromyalgia
diagnostic criteria. Seminars in Arthritis and Rheumatism,
46(3), 319–329. https://doi.org/10.1016/j.semarthrit.2016.08.012
Yu, L., Buysse, D. J., Germain, A., Moul, D. E., Stover, A., Dodds, N.
E., Johnston, K. L., & Pilkonis, P. A. (2012). Development of short
forms from the PROMIS™ sleep disturbance and sleep-related impairment
item banks. Behavioral Sleep Medicine, 10(1), 6–24. https://doi.org/10.1080/15402002.2012.636266
Zou, Q.-H., Zhu, C.-Z., Yang, Y., Zuo, X.-N., Long, X.-Y., Cao, Q.-J.,
Wang, Y.-F., & Zang, Y.-F. (2008). An improved approach to detection
of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI:
Fractional ALFF. Journal of Neuroscience Methods,
172(1), 137–141. https://doi.org/10.1016/j.jneumeth.2008.04.012