Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA
Language English Country United States Media print-electronic
Document type Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S.
Grant support
P50 MH094268
NIMH NIH HHS - United States
R13 TR005094
NCATS NIH HHS - United States
P50 MH064045
NIMH NIH HHS - United States
R01 EB015611
NIBIB NIH HHS - United States
R01 MH094524
NIMH NIH HHS - United States
U01 AG068057
NIA NIH HHS - United States
R01 MH060722
NIMH NIH HHS - United States
R01 MH116147
NIMH NIH HHS - United States
T32 AG058507
NIA NIH HHS - United States
R01 DA027680
NIDA NIH HHS - United States
T32 MH067533
NIMH NIH HHS - United States
R01 MH107730
NIMH NIH HHS - United States
RF1 MH123163
NIMH NIH HHS - United States
UL1 TR000153
NCATS NIH HHS - United States
T32 MH019112
NIMH NIH HHS - United States
R01 MH112847
NIMH NIH HHS - United States
P41 EB015922
NIBIB NIH HHS - United States
P20 GM103472
NIGMS NIH HHS - United States
T32 MH073526
NIMH NIH HHS - United States
R21 MH097196
NIMH NIH HHS - United States
R01 EB006841
NIBIB NIH HHS - United States
R01 MH105660
NIMH NIH HHS - United States
R01 MH117601
NIMH NIH HHS - United States
U01 MH108148
NIMH NIH HHS - United States
U54 EB020403
NIBIB NIH HHS - United States
P50 AA022534
NIAAA NIH HHS - United States
U24 RR021992
NCRR NIH HHS - United States
K23 MH085096
NIMH NIH HHS - United States
P50 MH103222
NIMH NIH HHS - United States
R01 MH092443
NIMH NIH HHS - United States
R01 MH121246
NIMH NIH HHS - United States
R01 MH112180
NIMH NIH HHS - United States
S10 OD023696
NIH HHS - United States
R01 MH074797
NIMH NIH HHS - United States
R01 MH085646
NIMH NIH HHS - United States
R01 AA021771
NIAAA NIH HHS - United States
G0500092
Medical Research Council - United Kingdom
PubMed
32470572
PubMed Central
PMC7524039
DOI
10.1016/j.neuroimage.2020.116956
PII: S1053-8119(20)30442-0
Knihovny.cz E-resources
- Keywords
- Brain, Cortical thickness, Gray matter, Mega-analysis, Neuroimaging, Schizophrenia, Volume,
- MeSH
- Algorithms MeSH
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Magnetic Resonance Imaging methods MeSH
- Meta-Analysis as Topic MeSH
- Young Adult MeSH
- Cerebral Cortex diagnostic imaging MeSH
- Neuroimaging MeSH
- Image Processing, Computer-Assisted methods MeSH
- Schizophrenia diagnostic imaging MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).
CIBERSAM Madrid Spain; FIDMAG Germanes Hospitalàries Research Foundation Barcelona Spain
Department of Psychiatry and Human Behavior University of California Irvine Irvine CA USA
Department of Psychiatry and Neuropsychology Maastricht University Maastricht The Netherlands
Department of Psychiatry and Psychotherapy Philipps University Marburg Marburg Germany
Department of Psychiatry University of Münster Münster Germany
Department of Psychiatry University of Oxford Oxford UK
Department of Psychiatry University of Pennsylvania Philadelphia PA USA
Departments of Psychiatry Johns Hopkins School of Medicine Baltimore MD USA
Georgia State University Atlanta GA USA
Imaging Genetics Center Department of Neurology University of Southern California Los Angeles CA USA
Imaging of Mood and Anxiety Related Disorders Barcelona Spain
Maryland Psychiatric Research Center University of Maryland School of Medicine Baltimore MD USA
Melbourne Neuropsychiatry Centre Dept of Psychiatry University of Melbourne Melbourne VIC Australia
Mental Health Research Center Moscow Russia
Murdoch Children's Research Institute Melbourne VIC Australia; The University of Melbourne Australia
Psychiatry Research Center Beijing Huilongguan Hospital Beijing China
University of Newcastle Newcastle NSW Australia
University of Stellenbosch Cape Town Western Province South Africa
See more in PubMed
Boedhoe PS, Schmaal L, Abe Y, Ameis SH, Arnold PD, Batistuzzo MC, Benedetti F, Beucke JC, Bollettini I, Bose A, Brem S, Calvo A, Cheng Y, Cho KI, Dallaspezia S, Denys D, Fitzgerald KD, Fouche JP, Gimenez M, Gruner P, Hanna GL, Hibar DP, Hoexter MQ, Hu H, Huyser C, Ikari K, Jahanshad N, Kathmann N, Kaufmann C, Koch K, Kwon JS, Lazaro L, Liu Y, Lochner C, Marsh R, Martinez-Zalacain I, Mataix-Cols D, Menchon JM, Minuzzi L, Nakamae T, Nakao T, Narayanaswamy JC, Piras F, Piras F, Pittenger C, Reddy YC, Sato JR, Simpson HB, Soreni N, Soriano-Mas C, Spalletta G, Stevens MC, Szeszko PR, Tolin DF, Venkatasubramanian G, Walitza S, Wang Z, van Wingen GA, Xu J, Xu X, Yun JY, Zhao Q, Group EOW, Thompson PM, Stein DJ, van den Heuvel OA, 2017. Distinct subcortical volume Alterations in pediatric and adult OCD: a worldwide meta- and mega-analysis. Am. J. Psychiatr 174, 60–69. PubMed PMC
Boedhoe PSW, Heymans MW, Schmaal L, Abe Y, Alonso P, Ameis SH, Anticevic A, Arnold PD, Batistuzzo MC, Benedetti F, Beucke JC, Bollettini I, Bose A, Brem S, Calvo A, Calvo R, Cheng Y, Cho KIK, Ciullo V, Dallaspezia S, Denys D, Feusner JD, Fitzgerald KD, Fouche JP, Fridgeirsson EA, Gruner P, Hanna GL, Hibar DP, Hoexter MQ, Hu H, Huyser C, Jahanshad N, James A, Kathmann N, Kaufmann C, Koch K, Kwon JS, Lazaro L, Lochner C, Marsh R, Martinez-Zalacain I, Mataix-Cols D, Menchon JM, Minuzzi L, Morer A, Nakamae T, Nakao T, Narayanaswamy JC, Nishida S, Nurmi EL, O’Neill J, Piacentini J, Piras F, Piras F, Reddy YCJ, Reess TJ, Sakai Y, Sato JR, Simpson HB, Soreni N, Soriano-Mas C, Spalletta G, Stevens MC, Szeszko PR, Tolin DF, van Wingen GA, Venkatasubramanian G, Walitza S, Wang Z, Yun JY, Working- Group, E.-O., Thompson PM, Stein DJ, van den Heuvel OA, Twisk JWR, 2018. An empirical comparison of meta- and mega-analysis with data from the ENIGMA obsessive-compulsive disorder working group. Front. Neuroinf 12, 102. PubMed PMC
Favre P, Pauling M, Stout J, Hozer F, Sarrazin S, Abe C, Alda M, Alloza C, Alonso-Lana S, Andreassen OA, Baune BT, Benedetti F, Busatto GF, Canales-Rodriguez EJ, Caseras X, Chaim-Avancini TM, Ching CRK, Dannlowski U, Deppe M, Eyler LT, Fatjo-Vilas M, Foley SF, Grotegerd D, Hajek T, Haukvik UK, Howells FM, Jahanshad N, Kugel H, Lagerberg TV, Lawrie SM, Linke JO, McIntosh A, Melloni EMT, Mitchell PB, Polosan M, Pomarol-Clotet E, Repple J, Roberts G, Roos A, Rosa PGP, Salvador R, Sarro S, Schofield PR, Serpa MH, Sim K, Stein DJ, Sussmann JE, Temmingh HS, Thompson PM, Verdolini N, Vieta E, Wessa M, Whalley HC, Zanetti MV, Leboyer M, Mangin JF, Henry C, Duchesnay E, Houenou J, Group EBDW, 2019. Widespread white matter microstructural abnormalities in bipolar disorder: evidence from mega- and meta-analyses across 3033 individuals. Neuropsychopharmacology 44, 2285–2293. PubMed PMC
Thompson PM, Stein JL, Medland SE, Hibar DP, Vasquez AA, Renteria ME, Toro R, Jahanshad N, Schumann G, Franke B, Wright MJ, Martin NG, Agartz I, Alda M, Alhusaini S, Almasy L, Almeida J, Alpert K, Andreasen NC, Andreassen OA, Apostolova LG, Appel K, Armstrong NJ, Aribisala B, Bastin ME, Bauer M, Bearden CE, Bergmann O, Binder EB, Blangero J, Bockholt HJ, Boen E, Bois C, Boomsma DI, Booth T, Bowman IJ, Bralten J, Brouwer RM, Brunner HG, Brohawn DG, Buckner RL, Buitelaar J, Bulayeva K, Bustillo JR, Calhoun VD, Cannon DM, Cantor RM, Carless MA, Caseras X, Cavalleri GL, Chakravarty MM, Chang KD, Ching CR, Christoforou A, Cichon S, Clark VP, Conrod P, Coppola G, Crespo-Facorro B, Curran JE, Czisch M, Deary IJ, de Geus EJ, den Braber A, Delvecchio G, Depondt C, de Haan L, de Zubicaray GI, Dima D, Dimitrova R, Djurovic S, Dong H, Donohoe G, Duggirala R, Dyer TD, Ehrlich S, Ekman CJ, Elvsashagen T, Emsell L, Erk S, Espeseth T, Fagerness J, Fears S, Fedko I, Fernandez G, Fisher SE, Foroud T, Fox PT, Francks C, Frangou S, Frey EM, Frodl T, Frouin V, Garavan H, Giddaluru S, Glahn DC, Godlewska B, Goldstein RZ, Gollub RL, Grabe HJ, Grimm O, Gruber O, Guadalupe T, Gur RE, Gur RC, Goring HH, Hagenaars S, Hajek T, Hall GB, Hall J, Hardy J, Hartman CA, Hass J, Hatton SN, Haukvik UK, Hegenscheid K, Heinz A, Hickie IB, Ho BC, Hoehn D, Hoekstra PJ, Hollinshead M, Holmes AJ, Homuth G, Hoogman M, Hong LE, Hosten N, Hottenga JJ, Hulshoff Pol HE, Hwang KS, Jack CR Jr., Jenkinson M, Johnston C, Jonsson EG, Kahn RS, Kasperaviciute D, Kelly S, Kim S, Kochunov P, Koenders L, Kramer B, Kwok JB, Lagopoulos J, Laje G, Landen M, Landman BA, Lauriello J, Lawrie SM, Lee PH, Le Hellard S, Lemaitre H, Leonardo CD, Li CS, Liberg B, Liewald DC, Liu X, Lopez LM, Loth E, Lourdusamy A, Luciano M, Macciardi F, Machielsen MW, Macqueen GM, Malt UF, Mandl R, Manoach DS, Martinot JL, Matarin M, Mather KA, Mattheisen M, Mattingsdal M, Meyer-Lindenberg A, McDonald C, McIntosh AM, McMahon FJ, McMahon KL, Meisenzahl E, Melle I, Milaneschi Y, Mohnke S, Montgomery GW, Morris DW, Moses EK, Mueller BA, Munoz Maniega S, Muhleisen TW, Muller-Myhsok B, Mwangi B, Nauck M, Nho K, Nichols TE, Nilsson LG, Nugent AC, Nyberg L, Olvera RL, Oosterlaan J, Ophoff RA, Pandolfo M, Papalampropoulou-Tsiridou M, Papmeyer M, Paus T, Pausova Z, Pearlson GD, Penninx BW, Peterson CP, Pfennig A, Phillips M, Pike GB, Poline JB, Potkin SG, Putz B, Ramasamy A, Rasmussen J, Rietschel M, Rijpkema M, Risacher SL, Roffman JL, Roiz-Santianez R, Romanczuk-Seiferth N, Rose EJ, Royle NA, Rujescu D, Ryten M, Sachdev PS, Salami A, Satterthwaite TD, Savitz J, Saykin AJ, Scanlon C, Schmaal L, Schnack HG, Schork AJ, Schulz SC, Schur R, Seidman L, Shen L, Shoemaker JM, Simmons A, Sisodiya SM, Smith C, Smoller JW, Soares JC, Sponheim SR, Sprooten E, Starr JM, Steen VM, Strakowski S, Strike L, Sussmann J, Samann PG, Teumer A, Toga AW, Tordesillas-Gutierrez D, Trabzuni D, Trost S, Turner J, Van den Heuvel M, van der Wee NJ, van Eijk K, van Erp TG, van Haren NE, van ‘t Ent D, van Tol MJ, Valdes Hernandez MC, Veltman DJ, Versace A, Volzke H, Walker R, Walter H, Wang L, Wardlaw JM, Weale ME, Weiner MW, Wen W, Westlye LT, Whalley HC, Whelan CD, White T, Winkler AM, Wittfeld K, Woldehawariat G, Wolf C, Zilles D, Zwiers MP, Thalamuthu A, Schofield PR, Freimer NB, Lawrence NS, Drevets W, Alzheimer’s Disease Neuroimaging Initiative, E.C.I.C.S.Y.S.G, 2014. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav 8, 153–182. PubMed PMC
van Erp TG, Hibar DP, Rasmussen JM, Glahn DC, Pearlson GD, Andreassen OA, Agartz I, Westlye LT, Haukvik UK, Dale AM, Melle I, Hartberg CB, Gruber O, Kraemer B, Zilles D, Donohoe G, Kelly S, McDonald C, Morris DW, Cannon DM, Corvin A, Machielsen MW, Koenders L, de Haan L, Veltman DJ, Satterthwaite TD, Wolf DH, Gur RC, Gur RE, Potkin SG, Mathalon DH, Mueller BA, Preda A, Macciardi F, Ehrlich S, Walton E, Hass J, Calhoun VD, Bockholt HJ, Sponheim SR, Shoemaker JM, van Haren NE, Hulshoff Pol HE, Ophoff RA, Kahn RS, Roiz-Santianez R, Crespo-Facorro B, Wang L, Alpert KI, Jonsson EG, Dimitrova R, Bois C, Whalley HC, McIntosh AM, Lawrie SM, Hashimoto R, Thompson PM, Turner JA, 2016. Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol. Psychiatr 21, 547–553. PubMed PMC
van Erp TGM, Walton E, Hibar DP, Schmaal L, Jiang W, Glahn DC, Pearlson GD, Yao N, Fukunaga M, Hashimoto R, Okada N, Yamamori H, Bustillo JR, Clark VP, Agartz I, Mueller BA, Cahn W, de Zwarte SMC, Hulshoff Pol HE, Kahn RS, Ophoff RA, van Haren NEM, Andreassen OA, Dale AM, Doan NT, Gurholt TP, Hartberg CB, Haukvik UK, Jorgensen KN, Lagerberg TV, Melle I, Westlye LT, Gruber O, Kraemer B, Richter A, Zilles D, Calhoun VD, Crespo-Facorro B, Roiz-Santianez R, Tordesillas-Gutierrez D, Loughland C, Carr VJ, Catts S, Cropley VL, Fullerton JM, Green MJ, Henskens FA, Jablensky A, Lenroot RK, Mowry BJ, Michie PT, Pantelis C, Quide Y, Schall U, Scott RJ, Cairns MJ, Seal M, Tooney PA, Rasser PE, Cooper G, Shannon Weickert C, Weickert TW, Morris DW, Hong E, Kochunov P, Beard LM, Gur RE, Gur RC, Satterthwaite TD, Wolf DH, Belger A, Brown GG, Ford JM, Macciardi F, Mathalon DH, O’Leary DS, Potkin SG, Preda A, Voyvodic J, Lim KO, McEwen S, Yang F, Tan Y, Tan S, Wang Z, Fan F, Chen J, Xiang H, Tang S, Guo H, Wan P, Wei D, Bockholt HJ, Ehrlich S, Wolthusen RPF, King MD, Shoemaker JM, Sponheim SR, De Haan L, Koenders L, Machielsen MW, van Amelsvoort T, Veltman DJ, Assogna F, Banaj N, de Rossi P, Iorio M, Piras F, Spalletta G, McKenna PJ, Pomarol-Clotet E, Salvador R, Corvin A, Donohoe G, Kelly S, Whelan CD, Dickie EW, Rotenberg D, Voineskos AN, Ciufolini S, Radua J, Dazzan P, Murray R, Reis Marques T, Simmons A, Borgwardt S, Egloff L, Harrisberger F, Riecher-Rossler A, Smieskova R, Alpert KI, Wang L, Jonsson EG, Koops S, Sommer IEC, Bertolino A, Bonvino A, Di Giorgio A, Neilson E, Mayer AR, Stephen JM, Kwon JS, Yun JY, Cannon DM, McDonald C, Lebedeva I, Tomyshev AS, Akhadov T, Kaleda V, Fatouros-Bergman H, Flyckt L, Karolinska Schizophrenia P, Busatto GF, Rosa PGP, Serpa MH, Zanetti MV, Hoschl C, Skoch A, Spaniel F, Tomecek D, Hagenaars SP, McIntosh AM, Whalley HC, Lawrie SM, Knochel C, Oertel-Knochel V, Stablein M, Howells FM, Stein DJ, Temmingh HS, Uhlmann A, Lopez-Jaramillo C, Dima D, McMahon A, Faskowitz JI, Gutman BA, Jahanshad N, Thompson PM, Turner JA, 2018. Cortical brain abnormalities in 4474 individuals with schizophrenia and 5098 control subjects via the enhancing Neuro imaging Genetics through meta analysis (ENIGMA) consortium. Biol. Psychiatr 84, 644–654. PubMed PMC
van Rooij D, Anagnostou E, Arango C, Auzias G, Behrmann M, Busatto GF, Calderoni S, Daly E, Deruelle C, Di Martino A, Dinstein I, Duran FLS, Durston S, Ecker C, Fair D, Fedor J, Fitzgerald J, Freitag CM, Gallagher L, Gori I, Haar S, Hoekstra L, Jahanshad N, Jalbrzikowski M, Janssen J, Lerch J, Luna B, Martinho MM, McGrath J, Muratori F, Murphy CM, Murphy DGM, O’Hearn K, Oranje B, Parellada M, Retico A, Rosa P, Rubia K, Shook D, Taylor M, Thompson PM, Tosetti M, Wallace GL, Zhou F, Buitelaar JK, 2018. Cortical and subcortical brain morphometry differences between patients with autism spectrum disorder and healthy individuals across the lifespan: results from the ENIGMA ASD working group. Am. J. Psychiatr 175, 359–369. PubMed PMC
Wong TY, Radua J, Pomarol-Clotet E, Salvador R, Albajes-Eizagirre A, Solanes A, Canales-Rodriguez EJ, Guerrero-Pedraza A, Sarro S, Kircher T, Nenadic I, Krug A, Grotegerd D, Dannlowski U, Borgwardt S, Riecher-Rossler A, Schmidt A, Andreou C, Huber CG, Turner J, Calhoun V, Jiang W, Clark S, Walton E, Spalletta G, Banaj N, Piras F, Ciullo V, Vecchio D, Lebedeva I, Tomyshev AS, Kaleda V, Klushnik T, Filho GB, Zanetti MV, Serpa MH, Penteado Rosa PG, Hashimoto R, Fukunaga M, Richter A, Kramer B, Gruber O, Voineskos AN, Dickie EW, Tomecek D, Skoch A, Spaniel F, Hoschl C, Bertolino A, Bonvino A, Di Giorgio A, Holleran L, Ciufolini S, Marques TR, Dazzan P, Murray R, Lamsma J, Cahn W, van Haren N, Diaz-Zuluaga AM, Pineda-Zapata JA, Vargas C, Lopez-Jaramillo C, van Erp TGM, Gur RC, Nickl-Jockschat T, 2019. An overlapping pattern of cerebral cortical thinning is associated with both positive symptoms and aggression in schizophrenia via the ENIGMA consortium. Psychol. Med 1–12. PubMed
Albajes-Eizagirre A, Radua J, 2018. What do results from coordinate-based meta-analyses tell us? Neuroimage 176, 550–553. PubMed
Albajes-Eizagirre A, Solanes A, Vieta E, Radua J, 2019. Voxel-based meta-analysis via permutation of subject images (PSI): theory and implementation for SDM. Neuroimage 186, 174–184. PubMed
Bates D, Maechler M, Bolker B, Walker S, 2015. Fitting linear mixed-effects models using lme4. J. Stat. Software 67, 1–48.
Blakesley RE, Mazumdar S, Dew MA, Houck PR, Tang G, Reynolds CF 3rd, Butters MA, 2009. Comparisons of methods for multiple hypothesis testing in neuropsychological research. Neuropsychology 23, 255–264. PubMed PMC
Chen B, Benedetti A, 2017. Quantifying heterogeneity in individual participant data meta-analysis with binary outcomes. Syst. Rev 6, 243. PubMed PMC
Chepkoech JL, Walhovd KB, Grydeland H, Fjell AM, Alzheimer’s Disease Neuroimaging I, 2016. Effects of change in FreeSurfer version on classification accuracy of patients with Alzheimer’s disease and mild cognitive impairment. Hum. Brain Mapp 37, 1831–1841. PubMed PMC
Dale AM, Fischl B, Sereno MI, 1999. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179–194. PubMed
Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ, 2006. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980. PubMed
Eickhoff SB, Laird AR, Grefkes C, Wang LE, Zilles K, Fox PT, 2009. Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: a random-effects approach based on empirical estimates of spatial uncertainty. Hum. Brain Mapp 30, 2907–2926. PubMed PMC
Eickhoff SB, Bzdok D, Laird AR, Kurth F, Fox PT, 2012. Activation likelihood estimation meta-analysis revisited. Neuroimage 59, 2349–2361. PubMed PMC
Fischl B, 2012. FreeSurfer. Neuroimage 62, 774–781. PubMed PMC
Fischl B, Sereno MI, Dale AM, 1999. Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system. Neuroimage 9, 195–207. PubMed
Fortin JP, Parker D, Tunc B, Watanabe T, Elliott MA, Ruparel K, Roalf DR, Satterthwaite TD, Gur RC, Gur RE, Schultz RT, Verma R, Shinohara RT, 2017. Harmonization of multi-site diffusion tensor imaging data. Neuroimage 161, 149–170. PubMed PMC
Fortin JP, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, Adams P, Cooper C, Fava M, McGrath PJ, McInnis M, Phillips ML, Trivedi MH, Weissman MM, Shinohara RT, 2018. Harmonization of cortical thickness measurements across scanners and sites. Neuroimage 167, 104–120. PubMed PMC
Gronenschild EH, Habets P, Jacobs HI, Mengelers R, Rozendaal N, van Os J, Marcelis M, 2012. The effects of FreeSurfer version, workstation type, and Macintosh operating system version on anatomical volume and cortical thickness measurements. PLoS One 7, e38234. PubMed PMC
Holm S, 1979. A simple sequentially rejective multiple test procedure. Scand. J. Stat 6, 65–70.
Huber G, 1957. Pneumencephalographische und psychopathologische Bilder bei endogenen Psychosen. Springer, Berlin, Heidelberg.
Johnson WE, Li C, Rabinovic A, 2007. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127. PubMed
Kay SR, Fiszbein A, Opler LA, 1987. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr. Bull 13, 261–276. PubMed
Kuznetsova A, Brockhoff PB, Christensen RHB, 2017. lmerTest package: tests in linear mixed effects models. J. Stat. Software 82, 1–26.
Leek JT, Johnson WE, Parker HS, Fertig EJ, Jaffe AE, Storey JD, Zhang Y, Torres LC, 2019. Sva: Surrogate Variable Analysis. R package.
Radua J, Mataix-Cols D, 2012. Meta-analytic methods for neuroimaging data explained. Biol. Mood Anxiety Disord 2, 6. PubMed PMC
Radua J, Mataix-Cols D, Phillips ML, El-Hage W, Kronhaus DM, Cardoner N, Surguladze S, 2012. A new meta-analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps. Eur. Psychiatr 27, 605–611. PubMed
Tustison NJ, Cook PA, Klein A, Song G, Das SR, Duda JT, Kandel BM, van Strien N, Stone JR, Gee JC, Avants BB, 2014. Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements. Neuroimage 99, 166–179. PubMed
Viechtbauer W, 2010. Conducting meta-analyses in R with the metafor package. J. Stat. Software 36, 1–48.
Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE, 2014. Permutation inference for the general linear model. Neuroimage 92, 381–397. PubMed PMC
Yu M, Linn KA, Cook PA, Phillips ML, McInnis M, Fava M, Trivedi MH, Weissman MM, Shinohara RT, Sheline YI, 2018. Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data. Hum. Brain Mapp 39, 4213–4227. PubMed PMC
Large-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium
NTR
NTR5094