A comparison of methods to harmonize cortical thickness measurements across scanners and sites

Delin Sun; Gopalkumar Rakesh; Courtney C. Haswell; Mark Logue; C. Lexi Baird; Erin N. O'Leary; Andrew S. Cotton; Hong Xie; Marijo Tamburrino; Tian Chen; Emily L. Dennis; Neda Jahanshad; Lauren E. Salminen; Sophia I. Thomopoulos; Faisal Rashid; Christopher R.K. Ching; Saskia B.J. Koch; Jessie L. Frijling; Laura Nawijn; Mirjam van Zuiden; Xi Zhu; Benjamin Suarez-Jimenez; Anika Sierk; Henrik Walter; Antje Manthey; Jennifer S. Stevens; Negar Fani; Sanne J.H. van Rooij; Murray Stein; Jessica Bomyea; Inga K. Koerte; Kyle Choi; Steven J.A. van der Werff; Robert R.J.M. Vermeiren; Julia Herzog; Lauren A.M. Lebois; Justin T. Baker; Elizabeth A. Olson; Thomas Straube; Mayuresh S. Korgaonkar; Elpiniki Andrew; Ye Zhu; Gen Li; Jonathan Ipser; Anna R. Hudson; Matthew Peverill; Kelly Sambrook; Evan Gordon; Lee Baugh; Gina Forster; Raluca M. Simons; Jeffrey S. Simons; Vincent Magnotta; Adi Maron-Katz; Stefan du Plessis; Seth G Disner; Nicholas Davenport; Daniel W. Grupe; Jack B. Nitschke; Terri A. deRoon-Cassini; Jacklynn M. Fitzgerald; John H. Krystal; Ifat Levy; Miranda Olff; Dick J. Veltman; Li Wang; Yuval Neria; Michael D. De Bellis; Tanja Jovanovic; Judith K. Daniels; Martha Shenton; Nic J.A. van de Wee; Christian Schmahl; Milissa L. Kaufman; Isabelle M. Rosso; Scott R. Sponheim; David Bernd Hofmann; Richard A. Bryant; Kelene A. Fercho; Dan J. Stein; Sven C. Mueller; Bobak Hosseini; K. Luan Phan; Katie A. McLaughlin; Richard J. Davidson; Christine L. Larson; Geoffrey May; Steven M. Nelson; Chadi G. Abdallah; Hassaan Gomaa; Amit Etkin; Soraya Seedat; Ilan Harpaz-Rotem; Israel Liberzon; Theo G.M. van Erp; Yann Quidé; Xin Wang; Paul M. Thompson; Rajendra A. Morey

doi:10.1016/j.neuroimage.2022.119509

A comparison of methods to harmonize cortical thickness measurements across scanners and sites

Delin Sun, Gopalkumar Rakesh, Courtney C. Haswell, Mark Logue, C. Lexi Baird, Erin N. O'Leary, Andrew S. Cotton, Hong Xie, Marijo Tamburrino, Tian Chen, Emily L. Dennis, Neda Jahanshad, Lauren E. Salminen, Sophia I. Thomopoulos, Faisal Rashid, Christopher R.K. Ching, Saskia B.J. Koch, Jessie L. Frijling, Laura Nawijn, Mirjam van ZuidenXi Zhu, Benjamin Suarez-Jimenez, Anika Sierk, Henrik Walter, Antje Manthey, Jennifer S. Stevens, Negar Fani, Sanne J.H. van Rooij, Murray Stein, Jessica Bomyea, Inga K. Koerte, Kyle Choi, Steven J.A. van der Werff, Robert R.J.M. Vermeiren, Julia Herzog, Lauren A.M. Lebois, Justin T. Baker, Elizabeth A. Olson, Thomas Straube, Mayuresh S. Korgaonkar, Elpiniki Andrew, Ye Zhu, Gen Li, Jonathan Ipser, Anna R. Hudson, Matthew Peverill, Kelly Sambrook, Evan Gordon, Lee Baugh, Gina Forster, Raluca M. Simons, Jeffrey S. Simons, Vincent Magnotta, Adi Maron-Katz, Stefan du Plessis, Seth G Disner, Nicholas Davenport, Daniel W. Grupe, Jack B. Nitschke, Terri A. deRoon-Cassini, Jacklynn M. Fitzgerald, John H. Krystal, Ifat Levy, Miranda Olff, Dick J. Veltman, Li Wang, Yuval Neria, Michael D. De Bellis, Tanja Jovanovic, Judith K. Daniels, Martha Shenton, Nic J.A. van de Wee, Christian Schmahl, Milissa L. Kaufman, Isabelle M. Rosso, Scott R. Sponheim, David Bernd Hofmann, Richard A. Bryant, Kelene A. Fercho, Dan J. Stein, Sven C. Mueller, Bobak Hosseini, K. Luan Phan, Katie A. McLaughlin, Richard J. Davidson, Christine L. Larson, Geoffrey May, Steven M. Nelson, Chadi G. Abdallah, Hassaan Gomaa, Amit Etkin, Soraya Seedat, Ilan Harpaz-Rotem, Israel Liberzon, Theo G.M. van Erp, Yann Quidé, Xin Wang, Paul M. Thompson, Rajendra A. Morey

Research output: Contribution to journal › Article › peer-review

22 Scopus citations

Abstract

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants’ demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LME_INT), (2) LME that models both site-specific random intercepts and age-related random slopes (LME_INT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2–81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3–85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ²(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ²(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ²(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects.

Original language	English (US)
Article number	119509
Journal	NeuroImage
Volume	261
DOIs	https://doi.org/10.1016/j.neuroimage.2022.119509
State	Published - Nov 1 2022

Bibliographical note

Funding Information:
DoD W81XWH-10-1-0925; Center for Brain and Behavior Research Pilot Grant; South Dakota Governor's Research Center Grant; CX001600 VA CDA ; NHMRC Program Grant # 1073041 ; R01 MH111671 ; VISN6 MIRECC ; German Research Foundation grant to J. K. Daniels ( DA 1222/4-1 and WA 1539/8-2 ); VA RR&D 1IK2RX000709 ; NIMH R01-MH043454 ; NIMH K01-MH122774 ; NIMH K01 MH118428-01 (Suarez-Jimenez); 5U01AA021681-08; K24MH71434; K24 DA028773; R01 MH63407; K99NS096116; VA RR&D 1K1RX002325; VA RR&D 1K2RX002922; MH101380; ZonMw, the Netherlands organization for Health Research and Development grant to Miranda Olff ( 40-00812-98-10041 ); Academic Medical Center Research Council grant to Miranda Olff ( 110614 ); VA CSR&D 1IK2CX001680; VISN17 Center of Excellence pilot funding; NIMH R01MH105535 ; NIMH 1R21MH102634 ; German Federal Ministry of Education and Research (BMBF RELEASE 01KR1303A ); German Research Society (Deutsche Forschungsgemeinschaft, DFG; SFB/TRR 58: C06, C07 ); R01MH117601 ; R01AG059874 ; MJFF 14848 ; MH098212 ; MH071537 ; M01RR00039 ; UL1TR000454 ; HD071982 ; HD085850 ; R21MH112956 ; Anonymous Women's Health Fund; Kasparian Fund; Trauma Scholars Fund; Barlow Family Fund; NIMH K01 MH118467 ; W81XWH-08-2-0159 ; Department of Veterans Affairs via support for the National Center for PTSD; NIAAA via its support for (P50) Center for the Translational Neuroscience of Alcohol; NCATS via its support of (CTSA) Yale Center for Clinical Investigation ; NIH R01 MH106574 ; F32MH109274 ; NIMH 1R21MH102634 ; R01MH113574 ; R01-MH103291 ; BOF 2–4 year project to Sven C. Mueller (01J05415); R01MH105355 ; Dana Foundation (to Dr. Nitschke); the University of Wisconsin Institute for Clinical and Translational Research ; a National Science Foundation Graduate Research Fellowship (to Dr. Grupe); the National Institute of Mental Health (NIMH) R01 MH63407 (to De Bellis), R01 AA12479 (to De Bellis), and R01 MH61744 (to De Bellis); R01-MH043454 and T32-MH018931 (to Dr. Davidson); core grant to the Waisman Center from the National Institute of Child Health and Human Development ( P30-HD003352 ); NIMH K23MH112873 ; Veterans Affairs Merit Review Program (10/01/08 – 09/30/13); L30 MH114379; South African Medical Research Council “SHARED ROOTS” Flagship Project; Grant -RFA-FSP-01-2013/SHARED ROOTS; South African Research Chair in PTSD from the Department of Science and Technology and the National Research Foundation ; US Department of Defense Grant W81XWH08-2-0159 (PI: Stein, Murray B); VA RR&D I01RX000622 ; CDMRP W81XWH-08-2-0038 ; South African Medical Research Council ; NARSAD Young Investigator ; Department of Defense award number W81XWH-12-2-0012 ; ENIGMA was also supported in part by U54 EB020403 from the Big Data to Knowledge (BD2K) program; R56AG058854; R01MH116147;; P41 EB015922; 1R01MH110483; 1R21 MH098198; R01MH105355-01A. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs, the United States Government, or any other funding sources listed here.

Publisher Copyright:
© 2022

Keywords

ComBat
ComBat-GAM
Cortical Thickness
Data Harmonization
General Additive Model
Linear Mixed-Effects Model
PTSD
Scanner Effects
Site Effects
Neuroimaging
Stress Disorders, Post-Traumatic
Humans
Middle Aged
Magnetic Resonance Imaging/methods
Male
Case-Control Studies
Young Adult
Adolescent
Aged, 80 and over
Adult
Female
Aged
Child

PubMed: MeSH publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Journal Article
Research Support, N.I.H., Extramural

Publisher link

10.1016/j.neuroimage.2022.119509

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Cite this

Sun, D., Rakesh, G., Haswell, C. C., Logue, M., Baird, C. L., O'Leary, E. N., Cotton, A. S., Xie, H., Tamburrino, M., Chen, T., Dennis, E. L., Jahanshad, N., Salminen, L. E., Thomopoulos, S. I., Rashid, F., Ching, C. R. K., Koch, S. B. J., Frijling, J. L., Nawijn, L., ... Morey, R. A. (2022). A comparison of methods to harmonize cortical thickness measurements across scanners and sites. NeuroImage, 261, Article 119509. https://doi.org/10.1016/j.neuroimage.2022.119509

Sun, D, Rakesh, G, Haswell, CC, Logue, M, Baird, CL, O'Leary, EN, Cotton, AS, Xie, H, Tamburrino, M, Chen, T, Dennis, EL, Jahanshad, N, Salminen, LE, Thomopoulos, SI, Rashid, F, Ching, CRK, Koch, SBJ, Frijling, JL, Nawijn, L, van Zuiden, M, Zhu, X, Suarez-Jimenez, B, Sierk, A, Walter, H, Manthey, A, Stevens, JS, Fani, N, van Rooij, SJH, Stein, M, Bomyea, J, Koerte, IK, Choi, K, van der Werff, SJA, Vermeiren, RRJM, Herzog, J, Lebois, LAM, Baker, JT, Olson, EA, Straube, T, Korgaonkar, MS, Andrew, E, Zhu, Y, Li, G, Ipser, J, Hudson, AR, Peverill, M, Sambrook, K, Gordon, E, Baugh, L, Forster, G, Simons, RM, Simons, JS, Magnotta, V, Maron-Katz, A, du Plessis, S, Disner, SG , Davenport, N, Grupe, DW, Nitschke, JB, deRoon-Cassini, TA, Fitzgerald, JM, Krystal, JH, Levy, I, Olff, M, Veltman, DJ, Wang, L, Neria, Y, De Bellis, MD, Jovanovic, T, Daniels, JK, Shenton, M, van de Wee, NJA, Schmahl, C, Kaufman, ML, Rosso, IM, Sponheim, SR, Hofmann, DB, Bryant, RA, Fercho, KA, Stein, DJ, Mueller, SC, Hosseini, B, Phan, KL, McLaughlin, KA, Davidson, RJ, Larson, CL, May, G, Nelson, SM, Abdallah, CG, Gomaa, H, Etkin, A, Seedat, S, Harpaz-Rotem, I, Liberzon, I, van Erp, TGM, Quidé, Y, Wang, X, Thompson, PM & Morey, RA 2022, 'A comparison of methods to harmonize cortical thickness measurements across scanners and sites', NeuroImage, vol. 261, 119509. https://doi.org/10.1016/j.neuroimage.2022.119509

@article{3accba6dc60b49b8a3e9c97ab8f7d3d0,

title = "A comparison of methods to harmonize cortical thickness measurements across scanners and sites",

abstract = "Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants{\textquoteright} demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LMEINT), (2) LME that models both site-specific random intercepts and age-related random slopes (LMEINT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2–81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3–85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ2(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ2(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ2(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects.",

keywords = "ComBat, ComBat-GAM, Cortical Thickness, Data Harmonization, General Additive Model, Linear Mixed-Effects Model, PTSD, Scanner Effects, Site Effects, Neuroimaging, Stress Disorders, Post-Traumatic, Humans, Middle Aged, Magnetic Resonance Imaging/methods, Male, Case-Control Studies, Young Adult, Adolescent, Aged, 80 and over, Adult, Female, Aged, Child",

author = "Delin Sun and Gopalkumar Rakesh and Haswell, {Courtney C.} and Mark Logue and Baird, {C. Lexi} and O'Leary, {Erin N.} and Cotton, {Andrew S.} and Hong Xie and Marijo Tamburrino and Tian Chen and Dennis, {Emily L.} and Neda Jahanshad and Salminen, {Lauren E.} and Thomopoulos, {Sophia I.} and Faisal Rashid and Ching, {Christopher R.K.} and Koch, {Saskia B.J.} and Frijling, {Jessie L.} and Laura Nawijn and {van Zuiden}, Mirjam and Xi Zhu and Benjamin Suarez-Jimenez and Anika Sierk and Henrik Walter and Antje Manthey and Stevens, {Jennifer S.} and Negar Fani and {van Rooij}, {Sanne J.H.} and Murray Stein and Jessica Bomyea and Koerte, {Inga K.} and Kyle Choi and {van der Werff}, {Steven J.A.} and Vermeiren, {Robert R.J.M.} and Julia Herzog and Lebois, {Lauren A.M.} and Baker, {Justin T.} and Olson, {Elizabeth A.} and Thomas Straube and Korgaonkar, {Mayuresh S.} and Elpiniki Andrew and Ye Zhu and Gen Li and Jonathan Ipser and Hudson, {Anna R.} and Matthew Peverill and Kelly Sambrook and Evan Gordon and Lee Baugh and Gina Forster and Simons, {Raluca M.} and Simons, {Jeffrey S.} and Vincent Magnotta and Adi Maron-Katz and {du Plessis}, Stefan and Disner, {Seth G} and Nicholas Davenport and Grupe, {Daniel W.} and Nitschke, {Jack B.} and deRoon-Cassini, {Terri A.} and Fitzgerald, {Jacklynn M.} and Krystal, {John H.} and Ifat Levy and Miranda Olff and Veltman, {Dick J.} and Li Wang and Yuval Neria and {De Bellis}, {Michael D.} and Tanja Jovanovic and Daniels, {Judith K.} and Martha Shenton and {van de Wee}, {Nic J.A.} and Christian Schmahl and Kaufman, {Milissa L.} and Rosso, {Isabelle M.} and Sponheim, {Scott R.} and Hofmann, {David Bernd} and Bryant, {Richard A.} and Fercho, {Kelene A.} and Stein, {Dan J.} and Mueller, {Sven C.} and Bobak Hosseini and Phan, {K. Luan} and McLaughlin, {Katie A.} and Davidson, {Richard J.} and Larson, {Christine L.} and Geoffrey May and Nelson, {Steven M.} and Abdallah, {Chadi G.} and Hassaan Gomaa and Amit Etkin and Soraya Seedat and Ilan Harpaz-Rotem and Israel Liberzon and {van Erp}, {Theo G.M.} and Yann Quid{\'e} and Xin Wang and Thompson, {Paul M.} and Morey, {Rajendra A.}",

note = "Funding Information: DoD W81XWH-10-1-0925; Center for Brain and Behavior Research Pilot Grant; South Dakota Governor's Research Center Grant; CX001600 VA CDA ; NHMRC Program Grant # 1073041 ; R01 MH111671 ; VISN6 MIRECC ; German Research Foundation grant to J. K. Daniels ( DA 1222/4-1 and WA 1539/8-2 ); VA RR&D 1IK2RX000709 ; NIMH R01-MH043454 ; NIMH K01-MH122774 ; NIMH K01 MH118428-01 (Suarez-Jimenez); 5U01AA021681-08; K24MH71434; K24 DA028773; R01 MH63407; K99NS096116; VA RR&D 1K1RX002325; VA RR&D 1K2RX002922; MH101380; ZonMw, the Netherlands organization for Health Research and Development grant to Miranda Olff ( 40-00812-98-10041 ); Academic Medical Center Research Council grant to Miranda Olff ( 110614 ); VA CSR&D 1IK2CX001680; VISN17 Center of Excellence pilot funding; NIMH R01MH105535 ; NIMH 1R21MH102634 ; German Federal Ministry of Education and Research (BMBF RELEASE 01KR1303A ); German Research Society (Deutsche Forschungsgemeinschaft, DFG; SFB/TRR 58: C06, C07 ); R01MH117601 ; R01AG059874 ; MJFF 14848 ; MH098212 ; MH071537 ; M01RR00039 ; UL1TR000454 ; HD071982 ; HD085850 ; R21MH112956 ; Anonymous Women's Health Fund; Kasparian Fund; Trauma Scholars Fund; Barlow Family Fund; NIMH K01 MH118467 ; W81XWH-08-2-0159 ; Department of Veterans Affairs via support for the National Center for PTSD; NIAAA via its support for (P50) Center for the Translational Neuroscience of Alcohol; NCATS via its support of (CTSA) Yale Center for Clinical Investigation ; NIH R01 MH106574 ; F32MH109274 ; NIMH 1R21MH102634 ; R01MH113574 ; R01-MH103291 ; BOF 2–4 year project to Sven C. Mueller (01J05415); R01MH105355 ; Dana Foundation (to Dr. Nitschke); the University of Wisconsin Institute for Clinical and Translational Research ; a National Science Foundation Graduate Research Fellowship (to Dr. Grupe); the National Institute of Mental Health (NIMH) R01 MH63407 (to De Bellis), R01 AA12479 (to De Bellis), and R01 MH61744 (to De Bellis); R01-MH043454 and T32-MH018931 (to Dr. Davidson); core grant to the Waisman Center from the National Institute of Child Health and Human Development ( P30-HD003352 ); NIMH K23MH112873 ; Veterans Affairs Merit Review Program (10/01/08 – 09/30/13); L30 MH114379; South African Medical Research Council “SHARED ROOTS” Flagship Project; Grant -RFA-FSP-01-2013/SHARED ROOTS; South African Research Chair in PTSD from the Department of Science and Technology and the National Research Foundation ; US Department of Defense Grant W81XWH08-2-0159 (PI: Stein, Murray B); VA RR&D I01RX000622 ; CDMRP W81XWH-08-2-0038 ; South African Medical Research Council ; NARSAD Young Investigator ; Department of Defense award number W81XWH-12-2-0012 ; ENIGMA was also supported in part by U54 EB020403 from the Big Data to Knowledge (BD2K) program; R56AG058854; R01MH116147;; P41 EB015922; 1R01MH110483; 1R21 MH098198; R01MH105355-01A. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs, the United States Government, or any other funding sources listed here. Publisher Copyright: {\textcopyright} 2022",

year = "2022",

month = nov,

day = "1",

doi = "10.1016/j.neuroimage.2022.119509",

language = "English (US)",

volume = "261",

journal = "NeuroImage",

issn = "1053-8119",

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TY - JOUR

T1 - A comparison of methods to harmonize cortical thickness measurements across scanners and sites

AU - Sun, Delin

AU - Rakesh, Gopalkumar

AU - Haswell, Courtney C.

AU - Logue, Mark

AU - Baird, C. Lexi

AU - O'Leary, Erin N.

AU - Cotton, Andrew S.

AU - Xie, Hong

AU - Tamburrino, Marijo

AU - Chen, Tian

AU - Dennis, Emily L.

AU - Jahanshad, Neda

AU - Salminen, Lauren E.

AU - Thomopoulos, Sophia I.

AU - Rashid, Faisal

AU - Ching, Christopher R.K.

AU - Koch, Saskia B.J.

AU - Frijling, Jessie L.

AU - Nawijn, Laura

AU - van Zuiden, Mirjam

AU - Zhu, Xi

AU - Suarez-Jimenez, Benjamin

AU - Sierk, Anika

AU - Walter, Henrik

AU - Manthey, Antje

AU - Stevens, Jennifer S.

AU - Fani, Negar

AU - van Rooij, Sanne J.H.

AU - Stein, Murray

AU - Bomyea, Jessica

AU - Koerte, Inga K.

AU - Choi, Kyle

AU - van der Werff, Steven J.A.

AU - Vermeiren, Robert R.J.M.

AU - Herzog, Julia

AU - Lebois, Lauren A.M.

AU - Baker, Justin T.

AU - Olson, Elizabeth A.

AU - Straube, Thomas

AU - Korgaonkar, Mayuresh S.

AU - Andrew, Elpiniki

AU - Zhu, Ye

AU - Li, Gen

AU - Ipser, Jonathan

AU - Hudson, Anna R.

AU - Peverill, Matthew

AU - Sambrook, Kelly

AU - Gordon, Evan

AU - Baugh, Lee

AU - Forster, Gina

AU - Simons, Raluca M.

AU - Simons, Jeffrey S.

AU - Magnotta, Vincent

AU - Maron-Katz, Adi

AU - du Plessis, Stefan

AU - Disner, Seth G

AU - Davenport, Nicholas

AU - Grupe, Daniel W.

AU - Nitschke, Jack B.

AU - deRoon-Cassini, Terri A.

AU - Fitzgerald, Jacklynn M.

AU - Krystal, John H.

AU - Levy, Ifat

AU - Olff, Miranda

AU - Veltman, Dick J.

AU - Wang, Li

AU - Neria, Yuval

AU - De Bellis, Michael D.

AU - Jovanovic, Tanja

AU - Daniels, Judith K.

AU - Shenton, Martha

AU - van de Wee, Nic J.A.

AU - Schmahl, Christian

AU - Kaufman, Milissa L.

AU - Rosso, Isabelle M.

AU - Sponheim, Scott R.

AU - Hofmann, David Bernd

AU - Bryant, Richard A.

AU - Fercho, Kelene A.

AU - Stein, Dan J.

AU - Mueller, Sven C.

AU - Hosseini, Bobak

AU - Phan, K. Luan

AU - McLaughlin, Katie A.

AU - Davidson, Richard J.

AU - Larson, Christine L.

AU - May, Geoffrey

AU - Nelson, Steven M.

AU - Abdallah, Chadi G.

AU - Gomaa, Hassaan

AU - Etkin, Amit

AU - Seedat, Soraya

AU - Harpaz-Rotem, Ilan

AU - Liberzon, Israel

AU - van Erp, Theo G.M.

AU - Quidé, Yann

AU - Wang, Xin

AU - Thompson, Paul M.

AU - Morey, Rajendra A.

N1 - Funding Information: DoD W81XWH-10-1-0925; Center for Brain and Behavior Research Pilot Grant; South Dakota Governor's Research Center Grant; CX001600 VA CDA ; NHMRC Program Grant # 1073041 ; R01 MH111671 ; VISN6 MIRECC ; German Research Foundation grant to J. K. Daniels ( DA 1222/4-1 and WA 1539/8-2 ); VA RR&D 1IK2RX000709 ; NIMH R01-MH043454 ; NIMH K01-MH122774 ; NIMH K01 MH118428-01 (Suarez-Jimenez); 5U01AA021681-08; K24MH71434; K24 DA028773; R01 MH63407; K99NS096116; VA RR&D 1K1RX002325; VA RR&D 1K2RX002922; MH101380; ZonMw, the Netherlands organization for Health Research and Development grant to Miranda Olff ( 40-00812-98-10041 ); Academic Medical Center Research Council grant to Miranda Olff ( 110614 ); VA CSR&D 1IK2CX001680; VISN17 Center of Excellence pilot funding; NIMH R01MH105535 ; NIMH 1R21MH102634 ; German Federal Ministry of Education and Research (BMBF RELEASE 01KR1303A ); German Research Society (Deutsche Forschungsgemeinschaft, DFG; SFB/TRR 58: C06, C07 ); R01MH117601 ; R01AG059874 ; MJFF 14848 ; MH098212 ; MH071537 ; M01RR00039 ; UL1TR000454 ; HD071982 ; HD085850 ; R21MH112956 ; Anonymous Women's Health Fund; Kasparian Fund; Trauma Scholars Fund; Barlow Family Fund; NIMH K01 MH118467 ; W81XWH-08-2-0159 ; Department of Veterans Affairs via support for the National Center for PTSD; NIAAA via its support for (P50) Center for the Translational Neuroscience of Alcohol; NCATS via its support of (CTSA) Yale Center for Clinical Investigation ; NIH R01 MH106574 ; F32MH109274 ; NIMH 1R21MH102634 ; R01MH113574 ; R01-MH103291 ; BOF 2–4 year project to Sven C. Mueller (01J05415); R01MH105355 ; Dana Foundation (to Dr. Nitschke); the University of Wisconsin Institute for Clinical and Translational Research ; a National Science Foundation Graduate Research Fellowship (to Dr. Grupe); the National Institute of Mental Health (NIMH) R01 MH63407 (to De Bellis), R01 AA12479 (to De Bellis), and R01 MH61744 (to De Bellis); R01-MH043454 and T32-MH018931 (to Dr. Davidson); core grant to the Waisman Center from the National Institute of Child Health and Human Development ( P30-HD003352 ); NIMH K23MH112873 ; Veterans Affairs Merit Review Program (10/01/08 – 09/30/13); L30 MH114379; South African Medical Research Council “SHARED ROOTS” Flagship Project; Grant -RFA-FSP-01-2013/SHARED ROOTS; South African Research Chair in PTSD from the Department of Science and Technology and the National Research Foundation ; US Department of Defense Grant W81XWH08-2-0159 (PI: Stein, Murray B); VA RR&D I01RX000622 ; CDMRP W81XWH-08-2-0038 ; South African Medical Research Council ; NARSAD Young Investigator ; Department of Defense award number W81XWH-12-2-0012 ; ENIGMA was also supported in part by U54 EB020403 from the Big Data to Knowledge (BD2K) program; R56AG058854; R01MH116147;; P41 EB015922; 1R01MH110483; 1R21 MH098198; R01MH105355-01A. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs, the United States Government, or any other funding sources listed here. Publisher Copyright: © 2022

PY - 2022/11/1

Y1 - 2022/11/1

N2 - Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants’ demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LMEINT), (2) LME that models both site-specific random intercepts and age-related random slopes (LMEINT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2–81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3–85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ2(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ2(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ2(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects.

AB - Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants’ demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LMEINT), (2) LME that models both site-specific random intercepts and age-related random slopes (LMEINT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2–81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3–85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ2(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ2(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ2(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects.

KW - ComBat

KW - ComBat-GAM

KW - Cortical Thickness

KW - Data Harmonization

KW - General Additive Model

KW - Linear Mixed-Effects Model

KW - PTSD

KW - Scanner Effects

KW - Site Effects

KW - Neuroimaging

KW - Stress Disorders, Post-Traumatic

KW - Humans

KW - Middle Aged

KW - Magnetic Resonance Imaging/methods

KW - Male

KW - Case-Control Studies

KW - Young Adult

KW - Adolescent

KW - Aged, 80 and over

KW - Adult

KW - Female

KW - Aged

KW - Child

UR - http://www.scopus.com/inward/record.url?scp=85135967385&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85135967385&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2022.119509

DO - 10.1016/j.neuroimage.2022.119509

M3 - Article

C2 - 35917919

AN - SCOPUS:85135967385

SN - 1053-8119

VL - 261

JO - NeuroImage

JF - NeuroImage

M1 - 119509

ER -

A comparison of methods to harmonize cortical thickness measurements across scanners and sites

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