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1. Brain charts for the human lifespan

Author: Bethlehem, RAI, Seidlitz, J, White, SR, Vogel, JW, Anderson, KM, Adamson, C, Adler, S, Alexopoulos, GS, Anagnostou, E, Areces-Gonzalez, A, Astle, DE, Auyeung, B, Ayub, M, Bae, J, Ball, G, Baron-Cohen, S, Beare, R, Bedford, SA, Benegal, V, Beyer, F, Blangero, J, Blesa Cábez, M, Boardman, JP, Borzage, M, Bosch-Bayard, JF, Bourke, N, Calhoun, VD, Chakravarty, MM, Chen, C, Chertavian, C, Chetelat, G, Chong, YS, Cole, JH, Corvin, A, Costantino, M, Courchesne, E, Crivello, F, Cropley, VL, Crosbie, J, Crossley, N, Delarue, M, Delorme, R, Desrivieres, S, Devenyi, GA, Di Biase, MA, Dolan, R, Donald, KA, Donohoe, G, Dunlop, K, Edwards, AD, Elison, JT, Ellis, CT, Elman, JA, Eyler, L, Fair, DA, Feczko, E, Fletcher, PC, Fonagy, P, Franz, CE, Galan-Garcia, L, Gholipour, A, Giedd, J, Gilmore, JH, Glahn, DC, Goodyer, IM, Grant, PE, Groenewold, NA, Gunning, FM, Gur, RE, Gur, RC, Hammill, CF, Hansson, O, Hedden, T, Heinz, A, Henson, RN, Heuer, K, Hoare, J, Holla, B, Holmes, AJ, Holt, R, Huang, H, Im, K, Ipser, J, Jack, CR, Jackowski, AP, Jia, T, Johnson, KA, Jones, PB, Jones, DT, Kahn, RS, Karlsson, H, Karlsson, L, Kawashima, R, Kelley, EA, Kern, S, Kim, KW, Kitzbichler, MG, Kremen, WS, Lalonde, F, and Landeau, B
Subjects: Biological Psychology, Psychology, Aging, Clinical Research, Neurosciences, Brain Disorders, Biomedical Imaging, Neurological, Mental health, Body Height, Brain, Humans, Longevity, Magnetic Resonance Imaging, Neuroimaging, 3R-BRAIN, AIBL, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s Disease Repository Without Borders Investigators, CALM Team, Cam-CAN, CCNP, COBRE, cVEDA, ENIGMA Developmental Brain Age Working Group, Developing Human Connectome Project, FinnBrain, Harvard Aging Brain Study, IMAGEN, KNE96, Mayo Clinic Study of Aging, NSPN, POND, PREVENT-AD Research Group, VETSA, General Science & Technology
Abstract: Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.
Published: 2022

2. Beyond the Global Brain Differences: Intraindividual Variability Differences in 1q21.1 Distal and 15q11.2 BP1-BP2 Deletion Carriers

Author: Boen, R, Kaufmann, T, van der Meer, D, Frei, O, Agartz, I, Ames, D, Andersson, M, Armstrong, NJ, Artiges, E, Atkins, JR, Bauer, J, Benedetti, F, Boomsma, DI, Brodaty, H, Brosch, K, Buckner, RL, Cairns, MJ, Calhoun, V, Caspers, S, Cichon, S, Corvin, AP, Crespo-Facorro, B, Dannlowski, U, David, FS, de Geus, EJC, de Zubicaray, GI, Desrivieres, S, Doherty, JL, Donohoe, G, Ehrlich, S, Eising, E, Espeseth, T, Fisher, SE, Forstner, AJ, Fortaner-Uya, L, Frouin, V, Fukunaga, M, Ge, T, Glahn, DC, Goltermann, J, Grabe, HJ, Green, MJ, Groenewold, NA, Grotegerd, D, Grontvedt, GR, Hahn, T, Hashimoto, R, Hehir-Kwa, JY, Henskens, FA, Holmes, AJ, Haberg, AK, Haavik, J, Jacquemont, S, Jansen, A, Jockwitz, C, Joensson, EG, Kikuchi, M, Kircher, T, Kumar, K, Le Hellard, S, Leu, C, Linden, DE, Liu, J, Loughnan, R, Mather, KA, Mcmahon, KL, Mcrae, AF, Medland, SE, Meinert, S, Moreau, CA, Morris, DW, Mowry, BJ, Muehleisen, TW, Nenadic, I, Noethen, MM, Nyberg, L, Ophoff, RA, Owen, MJ, Pantelis, C, Paolini, M, Paus, T, Pausova, Z, Persson, K, Quide, Y, Marques, TR, Sachdev, PS, Sando, SB, Schall, U, Scott, RJ, Selbaek, G, Shumskaya, E, Silva, AI, Sisodiya, SM, Stein, F, Stein, DJ, Straube, B, Streit, F, Strike, LT, Teumer, A, Teutenberg, L, Thalamuthu, A, Tooney, PA, Tordesillas-Gutierrez, D, Trollor, JN, Van't Ent, D, van den Bree, MBM, van Haren, NEM, Vazquez-Bourgon, J, Voelzke, H, Wen, W, Wittfeld, K, Ching, CRK, Westlye, LT, Thompson, PM, Bearden, CE, Selmer, KK, Alnaes, D, Andreassen, OA, Sonderby, IE, Boen, R, Kaufmann, T, van der Meer, D, Frei, O, Agartz, I, Ames, D, Andersson, M, Armstrong, NJ, Artiges, E, Atkins, JR, Bauer, J, Benedetti, F, Boomsma, DI, Brodaty, H, Brosch, K, Buckner, RL, Cairns, MJ, Calhoun, V, Caspers, S, Cichon, S, Corvin, AP, Crespo-Facorro, B, Dannlowski, U, David, FS, de Geus, EJC, de Zubicaray, GI, Desrivieres, S, Doherty, JL, Donohoe, G, Ehrlich, S, Eising, E, Espeseth, T, Fisher, SE, Forstner, AJ, Fortaner-Uya, L, Frouin, V, Fukunaga, M, Ge, T, Glahn, DC, Goltermann, J, Grabe, HJ, Green, MJ, Groenewold, NA, Grotegerd, D, Grontvedt, GR, Hahn, T, Hashimoto, R, Hehir-Kwa, JY, Henskens, FA, Holmes, AJ, Haberg, AK, Haavik, J, Jacquemont, S, Jansen, A, Jockwitz, C, Joensson, EG, Kikuchi, M, Kircher, T, Kumar, K, Le Hellard, S, Leu, C, Linden, DE, Liu, J, Loughnan, R, Mather, KA, Mcmahon, KL, Mcrae, AF, Medland, SE, Meinert, S, Moreau, CA, Morris, DW, Mowry, BJ, Muehleisen, TW, Nenadic, I, Noethen, MM, Nyberg, L, Ophoff, RA, Owen, MJ, Pantelis, C, Paolini, M, Paus, T, Pausova, Z, Persson, K, Quide, Y, Marques, TR, Sachdev, PS, Sando, SB, Schall, U, Scott, RJ, Selbaek, G, Shumskaya, E, Silva, AI, Sisodiya, SM, Stein, F, Stein, DJ, Straube, B, Streit, F, Strike, LT, Teumer, A, Teutenberg, L, Thalamuthu, A, Tooney, PA, Tordesillas-Gutierrez, D, Trollor, JN, Van't Ent, D, van den Bree, MBM, van Haren, NEM, Vazquez-Bourgon, J, Voelzke, H, Wen, W, Wittfeld, K, Ching, CRK, Westlye, LT, Thompson, PM, Bearden, CE, Selmer, KK, Alnaes, D, Andreassen, OA, and Sonderby, IE
Abstract: BACKGROUND: Carriers of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants exhibit regional and global brain differences compared with noncarriers. However, interpreting regional differences is challenging if a global difference drives the regional brain differences. Intraindividual variability measures can be used to test for regional differences beyond global differences in brain structure. METHODS: Magnetic resonance imaging data were used to obtain regional brain values for 1q21.1 distal deletion (n = 30) and duplication (n = 27) and 15q11.2 BP1-BP2 deletion (n = 170) and duplication (n = 243) carriers and matched noncarriers (n = 2350). Regional intra-deviation scores, i.e., the standardized difference between an individual's regional difference and global difference, were used to test for regional differences that diverge from the global difference. RESULTS: For the 1q21.1 distal deletion carriers, cortical surface area for regions in the medial visual cortex, posterior cingulate, and temporal pole differed less and regions in the prefrontal and superior temporal cortex differed more than the global difference in cortical surface area. For the 15q11.2 BP1-BP2 deletion carriers, cortical thickness in regions in the medial visual cortex, auditory cortex, and temporal pole differed less and the prefrontal and somatosensory cortex differed more than the global difference in cortical thickness. CONCLUSIONS: We find evidence for regional effects beyond differences in global brain measures in 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants. The results provide new insight into brain profiling of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants, with the potential to increase understanding of the mechanisms involved in altered neurodevelopment.
Published: 2024

3. Volume of subcortical brain regions in social anxiety disorder: mega-analytic results from 37 samples in the ENIGMA-Anxiety Working Group.

Author: Groenewold, NA, Bas-Hoogendam, JM, Amod, AR, Laansma, MA, Van Velzen, LS, Aghajani, M, Hilbert, K, Oh, H, Salas, R, Jackowski, AP, Pan, PM, Salum, GA, Blair, JR, Blair, KS, Hirsch, J, Pantazatos, SP, Schneier, FR, Talati, A, Roelofs, K, Volman, I, Blanco-Hinojo, L, Cardoner, N, Pujol, J, Beesdo-Baum, K, Ching, CRK, Thomopoulos, SI, Jansen, A, Kircher, T, Krug, A, Nenadić, I, Stein, F, Dannlowski, U, Grotegerd, D, Lemke, H, Meinert, S, Winter, A, Erb, M, Kreifelts, B, Gong, Q, Lui, S, Zhu, F, Mwangi, B, Soares, JC, Wu, M-J, Bayram, A, Canli, M, Tükel, R, Westenberg, PM, Heeren, A, Cremers, HR, Hofmann, D, Straube, T, Doruyter, AGG, Lochner, C, Peterburs, J, Van Tol, M-J, Gur, RE, Kaczkurkin, AN, Larsen, B, Satterthwaite, TD, Filippi, CA, Gold, AL, Harrewijn, A, Zugman, A, Bülow, R, Grabe, HJ, Völzke, H, Wittfeld, K, Böhnlein, J, Dohm, K, Kugel, H, Schrammen, E, Zwanzger, P, Leehr, EJ, Sindermann, L, Ball, TM, Fonzo, GA, Paulus, MP, Simmons, A, Stein, MB, Klumpp, H, Phan, KL, Furmark, T, Månsson, KNT, Manzouri, A, Avery, SN, Blackford, JU, Clauss, JA, Feola, B, Harper, JC, Sylvester, CM, Lueken, U, Veltman, DJ, Winkler, AM, Jahanshad, N, Pine, DS, Thompson, PM, Stein, DJ, Van der Wee, NJA, Groenewold, NA, Bas-Hoogendam, JM, Amod, AR, Laansma, MA, Van Velzen, LS, Aghajani, M, Hilbert, K, Oh, H, Salas, R, Jackowski, AP, Pan, PM, Salum, GA, Blair, JR, Blair, KS, Hirsch, J, Pantazatos, SP, Schneier, FR, Talati, A, Roelofs, K, Volman, I, Blanco-Hinojo, L, Cardoner, N, Pujol, J, Beesdo-Baum, K, Ching, CRK, Thomopoulos, SI, Jansen, A, Kircher, T, Krug, A, Nenadić, I, Stein, F, Dannlowski, U, Grotegerd, D, Lemke, H, Meinert, S, Winter, A, Erb, M, Kreifelts, B, Gong, Q, Lui, S, Zhu, F, Mwangi, B, Soares, JC, Wu, M-J, Bayram, A, Canli, M, Tükel, R, Westenberg, PM, Heeren, A, Cremers, HR, Hofmann, D, Straube, T, Doruyter, AGG, Lochner, C, Peterburs, J, Van Tol, M-J, Gur, RE, Kaczkurkin, AN, Larsen, B, Satterthwaite, TD, Filippi, CA, Gold, AL, Harrewijn, A, Zugman, A, Bülow, R, Grabe, HJ, Völzke, H, Wittfeld, K, Böhnlein, J, Dohm, K, Kugel, H, Schrammen, E, Zwanzger, P, Leehr, EJ, Sindermann, L, Ball, TM, Fonzo, GA, Paulus, MP, Simmons, A, Stein, MB, Klumpp, H, Phan, KL, Furmark, T, Månsson, KNT, Manzouri, A, Avery, SN, Blackford, JU, Clauss, JA, Feola, B, Harper, JC, Sylvester, CM, Lueken, U, Veltman, DJ, Winkler, AM, Jahanshad, N, Pine, DS, Thompson, PM, Stein, DJ, and Van der Wee, NJA
Abstract: There is limited convergence in neuroimaging investigations into volumes of subcortical brain regions in social anxiety disorder (SAD). The inconsistent findings may arise from variations in methodological approaches across studies, including sample selection based on age and clinical characteristics. The ENIGMA-Anxiety Working Group initiated a global mega-analysis to determine whether differences in subcortical volumes can be detected in adults and adolescents with SAD relative to healthy controls. Volumetric data from 37 international samples with 1115 SAD patients and 2775 controls were obtained from ENIGMA-standardized protocols for image segmentation and quality assurance. Linear mixed-effects analyses were adjusted for comparisons across seven subcortical regions in each hemisphere using family-wise error (FWE)-correction. Mixed-effects d effect sizes were calculated. In the full sample, SAD patients showed smaller bilateral putamen volume than controls (left: d = -0.077, pFWE = 0.037; right: d = -0.104, pFWE = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = -0.034, pFWE = 0.045). Smaller bilateral putamen volumes (left: d = -0.141, pFWE < 0.001; right: d = -0.158, pFWE < 0.001) and larger bilateral pallidum volumes (left: d = 0.129, pFWE = 0.006; right: d = 0.099, pFWE = 0.046) were detected in adult SAD patients relative to controls, but no volumetric differences were apparent in adolescent SAD patients relative to controls. Comorbid anxiety disorders and age of SAD onset were additional determinants of SAD-related volumetric differences in subcortical regions. To conclude, subtle volumetric alterations in subcortical regions in SAD were detected. Heterogeneity in age and clinical characteristics may partly explain inconsistencies in previous findings. The association between alterations in subcortical volumes and SAD illness progression deserves further investigation, especially from adolescence into adulthood.
Published: 2023

4. Reproducibility in the absence of selective reporting: An illustration from large-scale brain asymmetry research

Author: Kong, XZ, Francks, C, Mathias, SR, Guadalupe, T, Abé, C, Agartz, I, Akudjedu, TN, Aleman, A, Alhusaini, S, Allen, NB, Ames, D, Andreassen, OA, Vasquez, AA, Armstrong, NJ, Asherson, P, Bergo, F, Bastin, ME, Batalla, A, Bauer, J, Baune, BT, Baur-Streubel, R, Biederman, J, Blaine, SK, Boedhoe, P, Bøen, E, Bose, A, Bralten, J, Brandeis, D, Brem, S, Brodaty, H, Yüksel, D, Brooks, SJ, Buitelaar, J, Bürger, C, Bülow, R, Calhoun, V, Calvo, A, Canales-Rodríguez, EJ, Cannon, DM, Caparelli, EC, Castellanos, FX, Cendes, F, Chaim-Avancini, TM, Chantiluke, K, Chen, QL, Chen, X, Cheng, Y, Christakou, A, Clark, VP, Coghill, D, Connolly, CG, Conzelmann, A, Córdova-Palomera, A, Cousijn, J, Crow, T, Cubillo, A, Dannlowski, U, de Bruttopilo, SA, de Zeeuw, P, Deary, IJ, Demeter, DV, Di Martino, A, Dickie, EW, Dietsche, B, Doan, NT, Doherty, CP, Doyle, A, Durston, S, Earl, E, Ehrlich, S, Ekman, CJ, Elvsåshagen, T, Epstein, JN, Fair, DA, Faraone, SV, Fernández, G, Flint, C, Filho, GB, Förster, K, Fouche, JP, Foxe, JJ, Frodl, T, Fuentes-Claramonte, P, Fullerton, JM, Garavan, H, do Santos Garcia, D, Gotlib, IH, Goudriaan, AE, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Gurholt, T, Haavik, J, Hahn, T, Hansell, NK, Harris, MA, Hartman, CA, del Carmen Valdés Hernández, M, Kong, XZ, Francks, C, Mathias, SR, Guadalupe, T, Abé, C, Agartz, I, Akudjedu, TN, Aleman, A, Alhusaini, S, Allen, NB, Ames, D, Andreassen, OA, Vasquez, AA, Armstrong, NJ, Asherson, P, Bergo, F, Bastin, ME, Batalla, A, Bauer, J, Baune, BT, Baur-Streubel, R, Biederman, J, Blaine, SK, Boedhoe, P, Bøen, E, Bose, A, Bralten, J, Brandeis, D, Brem, S, Brodaty, H, Yüksel, D, Brooks, SJ, Buitelaar, J, Bürger, C, Bülow, R, Calhoun, V, Calvo, A, Canales-Rodríguez, EJ, Cannon, DM, Caparelli, EC, Castellanos, FX, Cendes, F, Chaim-Avancini, TM, Chantiluke, K, Chen, QL, Chen, X, Cheng, Y, Christakou, A, Clark, VP, Coghill, D, Connolly, CG, Conzelmann, A, Córdova-Palomera, A, Cousijn, J, Crow, T, Cubillo, A, Dannlowski, U, de Bruttopilo, SA, de Zeeuw, P, Deary, IJ, Demeter, DV, Di Martino, A, Dickie, EW, Dietsche, B, Doan, NT, Doherty, CP, Doyle, A, Durston, S, Earl, E, Ehrlich, S, Ekman, CJ, Elvsåshagen, T, Epstein, JN, Fair, DA, Faraone, SV, Fernández, G, Flint, C, Filho, GB, Förster, K, Fouche, JP, Foxe, JJ, Frodl, T, Fuentes-Claramonte, P, Fullerton, JM, Garavan, H, do Santos Garcia, D, Gotlib, IH, Goudriaan, AE, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Gurholt, T, Haavik, J, Hahn, T, Hansell, NK, Harris, MA, Hartman, CA, and del Carmen Valdés Hernández, M
Published: 2022

5. Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years

Author: Dima, D, Modabbernia, A, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, Ortiz-Garcia De la Foz, V, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, Van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Williams, SCR, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, Frangou, S, Dima, D, Modabbernia, A, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, Ortiz-Garcia De la Foz, V, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, Van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Williams, SCR, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, and Frangou, S
Abstract: Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.
Published: 2022

6. Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years

Author: Frangou, S, Modabbernia, A, Williams, SCR, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, de la Foz, VO-G, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, Dima, D, Frangou, S, Modabbernia, A, Williams, SCR, Papachristou, E, Doucet, GE, Agartz, I, Aghajani, M, Akudjedu, TN, Albajes-Eizagirre, A, Alnaes, D, Alpert, K, Andersson, M, Andreasen, NC, Andreassen, OA, Asherson, P, Banaschewski, T, Bargallo, N, Baumeister, S, Baur-Streubel, R, Bertolino, A, Bonvino, A, Boomsma, D, Borgwardt, S, Bourque, J, Brandeis, D, Breier, A, Brodaty, H, Brouwer, RM, Buitelaar, JK, Busatto, GF, Buckner, RL, Calhoun, V, Canales-Rodriguez, EJ, Cannon, DM, Caseras, X, Castellanos, FX, Cervenka, S, Chaim-Avancini, TM, Ching, CRK, Chubar, V, Clark, VP, Conrod, P, Conzelmann, A, Crespo-Facorro, B, Crivello, F, Crone, EA, Dale, AM, Davey, C, de Geus, EJC, de Haan, L, de Zubicaray, G, den Braber, A, Dickie, EW, Di Giorgio, A, Nhat, TD, Dorum, ES, Ehrlich, S, Erk, S, Espeseth, T, Fatouros-Bergman, H, Fisher, SE, Fouche, J-P, Franke, B, Frodl, T, Fuentes-Claramonte, P, Glahn, DC, Gotlib, IH, Grabe, H-J, Grimm, O, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Gur, RE, Gur, RC, Harrison, BJ, Hartman, CA, Hatton, SN, Heinz, A, Heslenfeld, DJ, Hibar, DP, Hickie, IB, Ho, B-C, Hoekstra, PJ, Hohmann, S, Holmes, AJ, Hoogman, M, Hosten, N, Howells, FM, Pol, HEH, Huyser, C, Jahanshad, N, James, A, Jernigan, TL, Jiang, J, Jonsson, EG, Joska, JA, Kahn, R, Kalnin, A, Kanai, R, Klein, M, Klyushnik, TP, Koenders, L, Koops, S, Kraemer, B, Kuntsi, J, Lagopoulos, J, Lazaro, L, Lebedeva, I, Lee, WH, Lesch, K-P, Lochner, C, Machielsen, MWJ, Maingault, S, Martin, NG, Martinez-Zalacain, I, Mataix-Cols, D, Mazoyer, B, McDonald, C, McDonald, BC, McIntosh, AM, McMahon, KL, McPhilemy, G, Menchon, JM, Medland, SE, Meyer-Lindenberg, A, Naaijen, J, Najt, P, Nakao, T, Nordvik, JE, Nyberg, L, Oosterlaan, J, de la Foz, VO-G, Paloyelis, Y, Pauli, P, Pergola, G, Pomarol-Clotet, E, Portella, MJ, Potkin, SG, Radua, J, Reif, A, Rinker, DA, Roffman, JL, Rosa, PGP, Sacchet, MD, Sachdev, PS, Salvador, R, Sanchez-Juan, P, Sarro, S, Satterthwaite, TD, Saykin, AJ, Serpa, MH, Schmaal, L, Schnell, K, Schumann, G, Sim, K, Smoller, JW, Sommer, I, Soriano-Mas, C, Stein, DJ, Strike, LT, Swagerman, SC, Tamnes, CK, Temmingh, HS, Thomopoulos, S, Tomyshev, AS, Tordesillas-Gutierrez, D, Trollor, JN, Turner, JA, Uhlmann, A, van den Heuvel, OA, van den Meer, D, van der Wee, NJA, van Haren, NEM, van't Ent, D, van Erp, TGM, Veer, IM, Veltman, DJ, Voineskos, A, Voelzke, H, Walter, H, Walton, E, Wang, L, Wang, Y, Wassink, TH, Weber, B, Wen, W, West, JD, Westlye, LT, Whalley, H, Wierenga, LM, Wittfeld, K, Wolf, DH, Worker, A, Wright, MJ, Yang, K, Yoncheva, Y, Zanetti, M, Ziegler, GC, Thompson, PM, and Dima, D
Abstract: Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.
Published: 2022

7. ENIGMA-anxiety working group: Rationale for and organization of large-scale neuroimaging studies of anxiety disorders.

Author: Bas-Hoogendam, JM, Groenewold, NA, Aghajani, M, Freitag, GF, Harrewijn, A, Hilbert, K, Jahanshad, N, Thomopoulos, SI, Thompson, PM, Veltman, DJ, Winkler, AM, Lueken, U, Pine, DS, van der Wee, NJA, Stein, DJ, ENIGMA-Anxiety Working Group, Bas-Hoogendam, JM, Groenewold, NA, Aghajani, M, Freitag, GF, Harrewijn, A, Hilbert, K, Jahanshad, N, Thomopoulos, SI, Thompson, PM, Veltman, DJ, Winkler, AM, Lueken, U, Pine, DS, van der Wee, NJA, Stein, DJ, and ENIGMA-Anxiety Working Group
Abstract: Anxiety disorders are highly prevalent and disabling but seem particularly tractable to investigation with translational neuroscience methodologies. Neuroimaging has informed our understanding of the neurobiology of anxiety disorders, but research has been limited by small sample sizes and low statistical power, as well as heterogenous imaging methodology. The ENIGMA-Anxiety Working Group has brought together researchers from around the world, in a harmonized and coordinated effort to address these challenges and generate more robust and reproducible findings. This paper elaborates on the concepts and methods informing the work of the working group to date, and describes the initial approach of the four subgroups studying generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobia. At present, the ENIGMA-Anxiety database contains information about more than 100 unique samples, from 16 countries and 59 institutes. Future directions include examining additional imaging modalities, integrating imaging and genetic data, and collaborating with other ENIGMA working groups. The ENIGMA consortium creates synergy at the intersection of global mental health and clinical neuroscience, and the ENIGMA-Anxiety Working Group extends the promise of this approach to neuroimaging research on anxiety disorders.
Published: 2022

8. Subcortical shape alterations in major depressive disorder: Findings from the ENIGMA major depressive disorder working group

Author: Ho, TC, Gutman, B, Pozzi, E, Grabe, HJ, Hosten, N, Wittfeld, K, Voelzke, H, Baune, B, Dannlowski, U, Foerster, K, Grotegerd, D, Redlich, R, Jansen, A, Kircher, T, Krug, A, Meinert, S, Nenadic, I, Opel, N, Dinga, R, Veltman, DJ, Schnell, K, Veer, I, Walter, H, Gotlib, IH, Sacchet, MD, Aleman, A, Groenewold, NA, Stein, DJ, Li, M, Walter, M, Ching, CRK, Jahanshad, N, Ragothaman, A, Isaev, D, Zavaliangos-Petropulu, A, Thompson, PM, Saemann, PG, Schmaal, L, Ho, TC, Gutman, B, Pozzi, E, Grabe, HJ, Hosten, N, Wittfeld, K, Voelzke, H, Baune, B, Dannlowski, U, Foerster, K, Grotegerd, D, Redlich, R, Jansen, A, Kircher, T, Krug, A, Meinert, S, Nenadic, I, Opel, N, Dinga, R, Veltman, DJ, Schnell, K, Veer, I, Walter, H, Gotlib, IH, Sacchet, MD, Aleman, A, Groenewold, NA, Stein, DJ, Li, M, Walter, M, Ching, CRK, Jahanshad, N, Ragothaman, A, Isaev, D, Zavaliangos-Petropulu, A, Thompson, PM, Saemann, PG, and Schmaal, L
Abstract: Alterations in regional subcortical brain volumes have been investigated as part of the efforts of an international consortium, ENIGMA, to identify reliable neural correlates of major depressive disorder (MDD). Given that subcortical structures are comprised of distinct subfields, we sought to build significantly from prior work by precisely mapping localized MDD-related differences in subcortical regions using shape analysis. In this meta-analysis of subcortical shape from the ENIGMA-MDD working group, we compared 1,781 patients with MDD and 2,953 healthy controls (CTL) on individual measures of shape metrics (thickness and surface area) on the surface of seven bilateral subcortical structures: nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Harmonized data processing and statistical analyses were conducted locally at each site, and findings were aggregated by meta-analysis. Relative to CTL, patients with adolescent-onset MDD (≤ 21 years) had lower thickness and surface area of the subiculum, cornu ammonis (CA) 1 of the hippocampus and basolateral amygdala (Cohen's d = -0.164 to -0.180). Relative to first-episode MDD, recurrent MDD patients had lower thickness and surface area in the CA1 of the hippocampus and the basolateral amygdala (Cohen's d = -0.173 to -0.184). Our results suggest that previously reported MDD-associated volumetric differences may be localized to specific subfields of these structures that have been shown to be sensitive to the effects of stress, with important implications for mapping treatments to patients based on specific neural targets and key clinical features.
Published: 2022

9. Brain charts for the human lifespan (vol 604, pg 525, 2022)

Author: Bethlehem, RAI, Seidlitz, J, White, SR, Vogel, JW, Anderson, KM, Adamson, C, Adler, S, Alexopoulos, GS, Anagnostou, E, Areces-Gonzalez, A, Astle, DE, Auyeung, B, Ayub, M, Bae, J, Ball, G, Baron-Cohen, S, Beare, R, Bedford, SA, Benegal, V, Beyer, F, Blangero, J, Blesa Cabez, M, Boardman, JP, Borzage, M, Bosch-Bayard, JF, Bourke, N, Calhoun, VD, Chakravarty, MM, Chen, C, Chertavian, C, Chetelat, G, Chong, YS, Cole, JH, Corvin, A, Costantino, M, Courchesne, E, Crivello, F, Cropley, VL, Crosbie, J, Crossley, N, Delarue, M, Delorme, R, Desrivieres, S, Devenyi, GA, Di Biase, MA, Dolan, R, Donald, KA, Donohoe, G, Dunlop, K, Edwards, AD, Elison, JT, Ellis, CT, Elman, JA, Eyler, L, Fair, DA, Feczko, E, Fletcher, PC, Fonagy, P, Franz, CE, Galan-Garcia, L, Gholipour, A, Giedd, J, Gilmore, JH, Glahn, DC, Goodyer, IM, Grant, PE, Groenewold, NA, Gunning, FM, Gur, RE, Gur, RC, Hammill, CF, Hansson, O, Hedden, T, Heinz, A, Henson, RN, Heuer, K, Hoare, J, Holla, B, Holmes, AJ, Holt, R, Huang, H, Im, K, Ipser, J, Jack, CR, Jackowski, AP, Jia, T, Johnson, KA, Jones, PB, Jones, DT, Kahn, RS, Karlsson, H, Karlsson, L, Kawashima, R, Kelley, EA, Kern, S, Kim, KW, Kitzbichler, MG, Kremen, WS, Lalonde, F, Landeau, B, Lee, S, Lerch, J, Lewis, JD, Li, J, Liao, W, Liston, C, Lombardo, MV, Lv, J, Lynch, C, Mallard, TT, Marcelis, M, Markello, RD, Mathias, SR, Mazoyer, B, McGuire, P, Meaney, MJ, Mechelli, A, Medic, N, Misic, B, Morgan, SE, Mothersill, D, Nigg, J, Ong, MQW, Ortinau, C, Ossenkoppele, R, Ouyang, M, Palaniyappan, L, Paly, L, Pan, PM, Pantelis, C, Park, MM, Paus, T, Pausova, Z, Paz-Linares, D, Pichet Binette, A, Pierce, K, Qian, X, Qiu, J, Qiu, A, Raznahan, A, Rittman, T, Rodrigue, A, Rollins, CK, Romero-Garcia, R, Ronan, L, Rosenberg, MD, Rowitch, DH, Salum, GA, Satterthwaite, TD, Schaare, HL, Schachar, RJ, Schultz, AP, Schumann, G, Scholl, M, Sharp, D, Shinohara, RT, Skoog, I, Smyser, CD, Sperling, RA, Stein, DJ, Stolicyn, A, Suckling, J, Sullivan, G, Taki, Y, Thyreau, B, Toro, R, Traut, N, Tsvetanov, KA, Turk-Browne, NB, Tuulari, JJ, Tzourio, C, Vachon-Presseau, E, Valdes-Sosa, MJ, Valdes-Sosa, PA, Valk, SL, van Amelsvoort, T, Vandekar, SN, Vasung, L, Victoria, LW, Villeneuve, S, Villringer, A, Vertes, PE, Wagstyl, K, Wang, YS, Warfield, SK, Warrier, V, Westman, E, Westwater, ML, Whalley, HC, Witte, AV, Yang, N, Yeo, B, Yun, H, Zalesky, A, Zar, HJ, Zettergren, A, Zhou, JH, Ziauddeen, H, Zugman, A, Zuo, XN, Bullmore, ET, Alexander-Bloch, AF, Bethlehem, RAI, Seidlitz, J, White, SR, Vogel, JW, Anderson, KM, Adamson, C, Adler, S, Alexopoulos, GS, Anagnostou, E, Areces-Gonzalez, A, Astle, DE, Auyeung, B, Ayub, M, Bae, J, Ball, G, Baron-Cohen, S, Beare, R, Bedford, SA, Benegal, V, Beyer, F, Blangero, J, Blesa Cabez, M, Boardman, JP, Borzage, M, Bosch-Bayard, JF, Bourke, N, Calhoun, VD, Chakravarty, MM, Chen, C, Chertavian, C, Chetelat, G, Chong, YS, Cole, JH, Corvin, A, Costantino, M, Courchesne, E, Crivello, F, Cropley, VL, Crosbie, J, Crossley, N, Delarue, M, Delorme, R, Desrivieres, S, Devenyi, GA, Di Biase, MA, Dolan, R, Donald, KA, Donohoe, G, Dunlop, K, Edwards, AD, Elison, JT, Ellis, CT, Elman, JA, Eyler, L, Fair, DA, Feczko, E, Fletcher, PC, Fonagy, P, Franz, CE, Galan-Garcia, L, Gholipour, A, Giedd, J, Gilmore, JH, Glahn, DC, Goodyer, IM, Grant, PE, Groenewold, NA, Gunning, FM, Gur, RE, Gur, RC, Hammill, CF, Hansson, O, Hedden, T, Heinz, A, Henson, RN, Heuer, K, Hoare, J, Holla, B, Holmes, AJ, Holt, R, Huang, H, Im, K, Ipser, J, Jack, CR, Jackowski, AP, Jia, T, Johnson, KA, Jones, PB, Jones, DT, Kahn, RS, Karlsson, H, Karlsson, L, Kawashima, R, Kelley, EA, Kern, S, Kim, KW, Kitzbichler, MG, Kremen, WS, Lalonde, F, Landeau, B, Lee, S, Lerch, J, Lewis, JD, Li, J, Liao, W, Liston, C, Lombardo, MV, Lv, J, Lynch, C, Mallard, TT, Marcelis, M, Markello, RD, Mathias, SR, Mazoyer, B, McGuire, P, Meaney, MJ, Mechelli, A, Medic, N, Misic, B, Morgan, SE, Mothersill, D, Nigg, J, Ong, MQW, Ortinau, C, Ossenkoppele, R, Ouyang, M, Palaniyappan, L, Paly, L, Pan, PM, Pantelis, C, Park, MM, Paus, T, Pausova, Z, Paz-Linares, D, Pichet Binette, A, Pierce, K, Qian, X, Qiu, J, Qiu, A, Raznahan, A, Rittman, T, Rodrigue, A, Rollins, CK, Romero-Garcia, R, Ronan, L, Rosenberg, MD, Rowitch, DH, Salum, GA, Satterthwaite, TD, Schaare, HL, Schachar, RJ, Schultz, AP, Schumann, G, Scholl, M, Sharp, D, Shinohara, RT, Skoog, I, Smyser, CD, Sperling, RA, Stein, DJ, Stolicyn, A, Suckling, J, Sullivan, G, Taki, Y, Thyreau, B, Toro, R, Traut, N, Tsvetanov, KA, Turk-Browne, NB, Tuulari, JJ, Tzourio, C, Vachon-Presseau, E, Valdes-Sosa, MJ, Valdes-Sosa, PA, Valk, SL, van Amelsvoort, T, Vandekar, SN, Vasung, L, Victoria, LW, Villeneuve, S, Villringer, A, Vertes, PE, Wagstyl, K, Wang, YS, Warfield, SK, Warrier, V, Westman, E, Westwater, ML, Whalley, HC, Witte, AV, Yang, N, Yeo, B, Yun, H, Zalesky, A, Zar, HJ, Zettergren, A, Zhou, JH, Ziauddeen, H, Zugman, A, Zuo, XN, Bullmore, ET, and Alexander-Bloch, AF
Published: 2022

10. A large-scale ENIGMA multisite replication study of brain age in depression

Author: Han, LKM, Dinga, R, Leenings, R, Hahn, T, Cole, JH, Aftanas, LI, Amod, AR, Besteher, B, Colle, R, Corruble, E, Couvy-Duchesne, B, Danilenko, KV, Fuentes-Claramonte, P, Gonul, AS, Gotlib, IH, Goya-Maldonado, R, Groenewold, NA, Hamilton, P, Ichikawa, N, Ipser, JC, Itai, E, Koopowitz, S-M, Li, M, Okada, G, Okamoto, Y, Churikova, OS, Osipov, EA, Penninx, BWJH, Pomarol-Clotet, E, Rodríguez-Cano, E, Sacchet, MD, Shinzato, H, Sim, K, Stein, DJ, Uyar-Demir, A, Veltman, DJ, Schmaal, L, Han, LKM, Dinga, R, Leenings, R, Hahn, T, Cole, JH, Aftanas, LI, Amod, AR, Besteher, B, Colle, R, Corruble, E, Couvy-Duchesne, B, Danilenko, KV, Fuentes-Claramonte, P, Gonul, AS, Gotlib, IH, Goya-Maldonado, R, Groenewold, NA, Hamilton, P, Ichikawa, N, Ipser, JC, Itai, E, Koopowitz, S-M, Li, M, Okada, G, Okamoto, Y, Churikova, OS, Osipov, EA, Penninx, BWJH, Pomarol-Clotet, E, Rodríguez-Cano, E, Sacchet, MD, Shinzato, H, Sim, K, Stein, DJ, Uyar-Demir, A, Veltman, DJ, and Schmaal, L
Abstract: Background: Several studies have evaluated whether depressed persons have older appearing brains than their nondepressed peers. However, the estimated neuroimaging-derived “brain age gap” has varied from study to study, likely driven by differences in training and testing sample (size), age range, and used modality/features. To validate our previously developed ENIGMA brain age model and the identified brain age gap, we aim to replicate the presence and effect size estimate previously found in the largest study in depression to date (N = 2126 controls & N = 2675 cases; +1.08 years [SE 0.22], Cohen’s d = 0.14, 95% CI: 0.08–0.20), in independent cohorts that were not part of the original study. Methods: A previously trained brain age model (www.photon-ai.com/enigma_brainage) based on 77 FreeSurfer brain regions of interest was used to obtain unbiased brain age predictions in 751 controls and 766 persons with depression (18–75 years) from 13 new cohorts collected from 20 different scanners. Meta-regressions were used to examine potential moderating effects of basic cohort characteristics (e.g., clinical and scan technical) on the brain age gap. Results: Our ENIGMA MDD brain age model generalized reasonably well to controls from the new cohorts (predicted age vs. age: r = 0.73, R2 = 0.47, MAE = 7.50 years), although the performance varied from cohort to cohort. In these new cohorts, on average, depressed persons showed a significantly higher brain age gap of +1 year (SE 0.35) (Cohen’s d = 0.15, 95% CI: 0.05–0.25) compared with controls, highly similar to our previous finding. Significant moderating effects of FreeSurfer version 6.0 (d = 0.41, p = 0.007) and Philips scanner vendor (d = 0.50, p < 0.0001) were found, leading to more positive effect size estimates. Conclusions: This study further validates our previously developed ENIGMA brain age algorithm. Importantly, we replicated the brain age gap in depression with a comparable effect size. Thus, two large-scale indep
Published: 2022

11. The Role of Educational Attainment and Brain Morphology in Major Depressive Disorder: Findings From the ENIGMA Major Depressive Disorder Consortium

Author: Whittle, S, Rakesh, D, Schmaal, L, Veltman, DJ, Thompson, PM, Singh, A, Gonul, AS, Aleman, A, Demir, AU, Krug, A, Mwangi, B, Kramer, B, Baune, BT, Stein, DJ, Grotegerd, D, Pomarol-Clotet, E, Rodriguez-Cano, E, Melloni, E, Benedetti, F, Stein, F, Grabe, HJ, Volzke, H, Gotlib, IH, Nenadic, I, Soares, JC, Repple, J, Sim, K, Brosch, K, Wittfeld, K, Berger, K, Hermesdorf, M, Portella, MJ, Sacchet, MD, Wu, M-J, Opel, N, Groenewold, NA, Gruber, O, Fuentes-Claramonte, P, Salvador, R, Goya-Maldonado, R, Sarro, S, Poletti, S, Meinert, SL, Kircher, T, Dannlowski, U, Pozzi, E, Whittle, S, Rakesh, D, Schmaal, L, Veltman, DJ, Thompson, PM, Singh, A, Gonul, AS, Aleman, A, Demir, AU, Krug, A, Mwangi, B, Kramer, B, Baune, BT, Stein, DJ, Grotegerd, D, Pomarol-Clotet, E, Rodriguez-Cano, E, Melloni, E, Benedetti, F, Stein, F, Grabe, HJ, Volzke, H, Gotlib, IH, Nenadic, I, Soares, JC, Repple, J, Sim, K, Brosch, K, Wittfeld, K, Berger, K, Hermesdorf, M, Portella, MJ, Sacchet, MD, Wu, M-J, Opel, N, Groenewold, NA, Gruber, O, Fuentes-Claramonte, P, Salvador, R, Goya-Maldonado, R, Sarro, S, Poletti, S, Meinert, SL, Kircher, T, Dannlowski, U, and Pozzi, E
Abstract: Brain structural abnormalities and low educational attainment are consistently associated with major depressive disorder (MDD), yet there has been little research investigating the complex interaction of these factors. Brain structural alterations may represent a vulnerability or differential susceptibility marker, and in the context of low educational attainment, predict MDD. We tested this moderation model in a large multisite sample of 1958 adults with MDD and 2921 controls (aged 18 to 86) from the ENIGMA MDD working group. Using generalized linear mixed models and within-sample split-half replication, we tested whether brain structure interacted with educational attainment to predict MDD status. Analyses revealed that cortical thickness in a number of occipital, parietal, and frontal regions significantly interacted with education to predict MDD. For the majority of regions, models suggested a differential susceptibility effect, whereby thicker cortex was more likely to predict MDD in individuals with low educational attainment, but less likely to predict MDD in individuals with high educational attainment. Findings suggest that greater thickness of brain regions subserving visuomotor and social-cognitive functions confers susceptibility to MDD, dependent on level of educational attainment. Longitudinal work, however, is ultimately needed to establish whether cortical thickness represents a preexisting susceptibility marker. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Published: 2022

12. Virtual Ontogeny of Cortical Growth Preceding Mental Illness

Author: Patel, Y, Shin, J, Abe, C, Agartz, I, Alloza, C, Alnaes, D, Ambrogi, S, Antonucci, LA, Arango, C, Arolt, V, Auzias, G, Ayesa-Arriola, R, Banaj, N, Banaschewski, T, Bandeira, C, Basgoze, Z, Cupertino, RB, Bau, CHD, Bauer, J, Baumeister, S, Bernardoni, F, Bertolino, A, del Mar Bonnin, C, Brandeis, D, Brem, S, Bruggemann, J, Bulow, R, Bustillo, JR, Calderoni, S, Calvo, R, Canales-Rodriguez, EJ, Cannon, DM, Carmona, S, Carr, VJ, Catts, SV, Chenji, S, Chew, QH, Coghill, D, Connolly, CG, Conzelmann, A, Craven, AR, Crespo-Facorro, B, Cullen, K, Dahl, A, Dannlowski, U, Davey, CG, Deruelle, C, Diaz-Caneja, CM, Dohm, K, Ehrlich, S, Epstein, J, Erwin-Grabner, T, Eyler, LT, Fedor, J, Fitzgerald, J, Foran, W, Ford, JM, Fortea, L, Fuentes-Claramonte, P, Fullerton, J, Furlong, L, Gallagher, L, Gao, B, Gao, S, Goikolea, JM, Gotlib, I, Goya-Maldonado, R, Grabe, HJ, Green, M, Grevet, EH, Groenewold, NA, Grotegerd, D, Gruber, O, Haavik, J, Hahn, T, Harrison, BJ, Heindel, W, Henskens, F, Heslenfeld, DJ, Hilland, E, Hoekstra, PJ, Hohmann, S, Holz, N, Howells, FM, Ipser, JC, Jahanshad, N, Jakobi, B, Jansen, A, Janssen, J, Jonassen, R, Kaiser, A, Kaleda, V, Karantonis, J, King, JA, Kircher, T, Kochunov, P, Koopowitz, S-M, Landen, M, Landro, NI, Lawrie, S, Lebedeva, I, Luna, B, Lundervold, AJ, MacMaster, FP, Maglanoc, LA, Mathalon, DH, McDonald, C, McIntosh, A, Meinert, S, Michie, PT, Mitchell, P, Moreno-Alcazar, A, Mowry, B, Muratori, F, Nabulsi, L, Nenadic, I, Tuura, RO, Oosterlaan, J, Overs, B, Pantelis, C, Parellada, M, Pariente, JC, Pauli, P, Pergola, G, Piarulli, FM, Picon, F, Piras, F, Pomarol-Clotet, E, Pretus, C, Quide, Y, Radua, J, Ramos-Quiroga, JA, Rasser, PE, Reif, A, Retico, A, Roberts, G, Rossell, S, Rovaris, DL, Rubia, K, Sacchet, M, Salavert, J, Salvador, R, Sarro, S, Sawa, A, Schall, U, Scott, R, Selvaggi, P, Silk, T, Sim, K, Skoch, A, Spalletta, G, Spaniel, F, Stein, DJ, Steinstrater, O, Stolicyn, A, Takayanagi, Y, Tamm, L, Tavares, M, Teumer, A, Thiel, K, Thomopoulos, SI, Tomecek, D, Tomyshev, AS, Tordesillas-Gutierrez, D, Tosetti, M, Uhlmann, A, Van Rheenen, T, Vazquez-Bourgon, J, Vernooij, MW, Vieta, E, Vilarroya, O, Weickert, C, Weickert, T, Westlye, LT, Whalley, H, Willinger, D, Winter, A, Wittfeld, K, Yang, TT, Yoncheva, Y, Zijlmans, JL, Hoogman, M, Franke, B, van Rooij, D, Buitelaar, J, Ching, CRK, Andreassen, OA, Pozzi, E, Veltman, D, Schmaal, L, van Erp, TGM, Turner, J, Castellanos, FX, Pausova, Z, Thompson, P, Paus, T, Patel, Y, Shin, J, Abe, C, Agartz, I, Alloza, C, Alnaes, D, Ambrogi, S, Antonucci, LA, Arango, C, Arolt, V, Auzias, G, Ayesa-Arriola, R, Banaj, N, Banaschewski, T, Bandeira, C, Basgoze, Z, Cupertino, RB, Bau, CHD, Bauer, J, Baumeister, S, Bernardoni, F, Bertolino, A, del Mar Bonnin, C, Brandeis, D, Brem, S, Bruggemann, J, Bulow, R, Bustillo, JR, Calderoni, S, Calvo, R, Canales-Rodriguez, EJ, Cannon, DM, Carmona, S, Carr, VJ, Catts, SV, Chenji, S, Chew, QH, Coghill, D, Connolly, CG, Conzelmann, A, Craven, AR, Crespo-Facorro, B, Cullen, K, Dahl, A, Dannlowski, U, Davey, CG, Deruelle, C, Diaz-Caneja, CM, Dohm, K, Ehrlich, S, Epstein, J, Erwin-Grabner, T, Eyler, LT, Fedor, J, Fitzgerald, J, Foran, W, Ford, JM, Fortea, L, Fuentes-Claramonte, P, Fullerton, J, Furlong, L, Gallagher, L, Gao, B, Gao, S, Goikolea, JM, Gotlib, I, Goya-Maldonado, R, Grabe, HJ, Green, M, Grevet, EH, Groenewold, NA, Grotegerd, D, Gruber, O, Haavik, J, Hahn, T, Harrison, BJ, Heindel, W, Henskens, F, Heslenfeld, DJ, Hilland, E, Hoekstra, PJ, Hohmann, S, Holz, N, Howells, FM, Ipser, JC, Jahanshad, N, Jakobi, B, Jansen, A, Janssen, J, Jonassen, R, Kaiser, A, Kaleda, V, Karantonis, J, King, JA, Kircher, T, Kochunov, P, Koopowitz, S-M, Landen, M, Landro, NI, Lawrie, S, Lebedeva, I, Luna, B, Lundervold, AJ, MacMaster, FP, Maglanoc, LA, Mathalon, DH, McDonald, C, McIntosh, A, Meinert, S, Michie, PT, Mitchell, P, Moreno-Alcazar, A, Mowry, B, Muratori, F, Nabulsi, L, Nenadic, I, Tuura, RO, Oosterlaan, J, Overs, B, Pantelis, C, Parellada, M, Pariente, JC, Pauli, P, Pergola, G, Piarulli, FM, Picon, F, Piras, F, Pomarol-Clotet, E, Pretus, C, Quide, Y, Radua, J, Ramos-Quiroga, JA, Rasser, PE, Reif, A, Retico, A, Roberts, G, Rossell, S, Rovaris, DL, Rubia, K, Sacchet, M, Salavert, J, Salvador, R, Sarro, S, Sawa, A, Schall, U, Scott, R, Selvaggi, P, Silk, T, Sim, K, Skoch, A, Spalletta, G, Spaniel, F, Stein, DJ, Steinstrater, O, Stolicyn, A, Takayanagi, Y, Tamm, L, Tavares, M, Teumer, A, Thiel, K, Thomopoulos, SI, Tomecek, D, Tomyshev, AS, Tordesillas-Gutierrez, D, Tosetti, M, Uhlmann, A, Van Rheenen, T, Vazquez-Bourgon, J, Vernooij, MW, Vieta, E, Vilarroya, O, Weickert, C, Weickert, T, Westlye, LT, Whalley, H, Willinger, D, Winter, A, Wittfeld, K, Yang, TT, Yoncheva, Y, Zijlmans, JL, Hoogman, M, Franke, B, van Rooij, D, Buitelaar, J, Ching, CRK, Andreassen, OA, Pozzi, E, Veltman, D, Schmaal, L, van Erp, TGM, Turner, J, Castellanos, FX, Pausova, Z, Thompson, P, and Paus, T
Abstract: BACKGROUND: Morphology of the human cerebral cortex differs across psychiatric disorders, with neurobiology and developmental origins mostly undetermined. Deviations in the tangential growth of the cerebral cortex during pre/perinatal periods may be reflected in individual variations in cortical surface area later in life. METHODS: Interregional profiles of group differences in surface area between cases and controls were generated using T1-weighted magnetic resonance imaging from 27,359 individuals including those with attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia, and high general psychopathology (through the Child Behavior Checklist). Similarity of interregional profiles of group differences in surface area and prenatal cell-specific gene expression was assessed. RESULTS: Across the 11 cortical regions, group differences in cortical area for attention-deficit/hyperactivity disorder, schizophrenia, and Child Behavior Checklist were dominant in multimodal association cortices. The same interregional profiles were also associated with interregional profiles of (prenatal) gene expression specific to proliferative cells, namely radial glia and intermediate progenitor cells (greater expression, larger difference), as well as differentiated cells, namely excitatory neurons and endothelial and mural cells (greater expression, smaller difference). Finally, these cell types were implicated in known pre/perinatal risk factors for psychosis. Genes coexpressed with radial glia were enriched with genes implicated in congenital abnormalities, birth weight, hypoxia, and starvation. Genes coexpressed with endothelial and mural genes were enriched with genes associated with maternal hypertension and preterm birth. CONCLUSIONS: Our findings support a neurodevelopmental model of vulnerability to mental illness whereby prenatal risk factors acting through cell-specific processes lead to deviations from t
Published: 2022

13. Virtual histology of cortical thickness and shared neurobiology in 6 psychiatric disorders

Author: Patel, Y, Parker, N, Shin, J, Howard, D, French, L, Thomopoulos, SI, Pozzi, E, Abe, Y, Abé, C, Anticevic, A, Alda, M, Aleman, A, Alloza, C, Alonso-Lana, S, Ameis, SH, Anagnostou, E, McIntosh, AA, Arango, C, Arnold, PD, Asherson, P, Assogna, F, Auzias, G, Ayesa-Arriola, R, Bakker, G, Banaj, N, Banaschewski, T, Bandeira, CE, Baranov, A, Bargalló, N, Bau, CHD, Baumeister, S, Baune, BT, Bellgrove, MA, Benedetti, F, Bertolino, A, Boedhoe, PSW, Boks, M, Bollettini, I, Del Mar Bonnin, C, Borgers, T, Borgwardt, S, Brandeis, D, Brennan, BP, Bruggemann, JM, Bülow, R, Busatto, GF, Calderoni, S, Calhoun, VD, Calvo, R, Canales-Rodríguez, EJ, Cannon, DM, Carr, VJ, Cascella, N, Cercignani, M, Chaim-Avancini, TM, Christakou, A, Coghill, D, Conzelmann, A, Crespo-Facorro, B, Cubillo, AI, Cullen, KR, Cupertino, RB, Daly, E, Dannlowski, U, Davey, CG, Denys, D, Deruelle, C, Di Giorgio, A, Dickie, EW, Dima, D, Dohm, K, Ehrlich, S, Ely, BA, Erwin-Grabner, T, Ethofer, T, Fair, DA, Fallgatter, AJ, Faraone, SV, Fatjó-Vilas, M, Fedor, JM, Fitzgerald, KD, Ford, JM, Frodl, T, Fu, CHY, Fullerton, JM, Gabel, MC, Glahn, DC, Roberts, G, Gogberashvili, T, Goikolea, JM, Gotlib, IH, Goya-Maldonado, R, Grabe, HJ, Green, MJ, Grevet, EH, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Guerrero-Pedraza, A, Silk, Timothy, Patel, Y, Parker, N, Shin, J, Howard, D, French, L, Thomopoulos, SI, Pozzi, E, Abe, Y, Abé, C, Anticevic, A, Alda, M, Aleman, A, Alloza, C, Alonso-Lana, S, Ameis, SH, Anagnostou, E, McIntosh, AA, Arango, C, Arnold, PD, Asherson, P, Assogna, F, Auzias, G, Ayesa-Arriola, R, Bakker, G, Banaj, N, Banaschewski, T, Bandeira, CE, Baranov, A, Bargalló, N, Bau, CHD, Baumeister, S, Baune, BT, Bellgrove, MA, Benedetti, F, Bertolino, A, Boedhoe, PSW, Boks, M, Bollettini, I, Del Mar Bonnin, C, Borgers, T, Borgwardt, S, Brandeis, D, Brennan, BP, Bruggemann, JM, Bülow, R, Busatto, GF, Calderoni, S, Calhoun, VD, Calvo, R, Canales-Rodríguez, EJ, Cannon, DM, Carr, VJ, Cascella, N, Cercignani, M, Chaim-Avancini, TM, Christakou, A, Coghill, D, Conzelmann, A, Crespo-Facorro, B, Cubillo, AI, Cullen, KR, Cupertino, RB, Daly, E, Dannlowski, U, Davey, CG, Denys, D, Deruelle, C, Di Giorgio, A, Dickie, EW, Dima, D, Dohm, K, Ehrlich, S, Ely, BA, Erwin-Grabner, T, Ethofer, T, Fair, DA, Fallgatter, AJ, Faraone, SV, Fatjó-Vilas, M, Fedor, JM, Fitzgerald, KD, Ford, JM, Frodl, T, Fu, CHY, Fullerton, JM, Gabel, MC, Glahn, DC, Roberts, G, Gogberashvili, T, Goikolea, JM, Gotlib, IH, Goya-Maldonado, R, Grabe, HJ, Green, MJ, Grevet, EH, Groenewold, NA, Grotegerd, D, Gruber, O, Gruner, P, Guerrero-Pedraza, A, and Silk, Timothy
Published: 2021

14. 1q21.1 distal copy number variants are associated with cerebral and cognitive alterations in humans

Author: Sonderby, IE, van der Meer, D, Moreau, C, Kaufmann, T, Walters, GB, Ellegaard, M, Abdellaoui, A, Ames, D, Amunts, K, Andersson, M, Armstrong, NJ, Bernard, M, Blackburn, NB, Blangero, J, Boomsma, DI, Brodaty, H, Brouwer, RM, Buelow, R, Boen, R, Cahn, W, Calhoun, VD, Caspers, S, Ching, CRK, Cichon, S, Ciufolini, S, Crespo-Facorro, B, Curran, JE, Dale, AM, Dalvie, S, Dazzan, P, de Geus, EJC, de Zubicaray, GI, de Zwarte, SMC, Desrivieres, S, Doherty, JL, Donohoe, G, Draganski, B, Ehrlich, S, Eising, E, Espeseth, T, Fejgin, K, Fisher, SE, Fladby, T, Frei, O, Frouin, V, Fukunaga, M, Gareau, T, Ge, T, Glahn, DC, Grabe, HJ, Groenewold, NA, Gustafsson, O, Haavik, J, Haberg, AK, Hall, J, Hashimoto, R, Hehir-Kwa, JY, Hibar, DP, Hillegers, MHJ, Hoffmann, P, Holleran, L, Holmes, AJ, Homuth, G, Hottenga, J-J, Hulshoff Pol, HE, Ikeda, M, Jahanshad, N, Jockwitz, C, Johansson, S, Joensson, EG, Jorgensen, NR, Kikuchi, M, Knowles, EEM, Kumar, K, Le Hellard, S, Leu, C, Linden, DEJ, Liu, J, Lundervold, A, Lundervold, AJ, Maillard, AM, Martin, NG, Martin-Brevet, S, Mather, KA, Mathias, SR, McMahon, KL, McRae, AF, Medland, SE, Meyer-Lindenberg, A, Moberget, T, Modenato, C, Sanchez, JM, Morris, DW, Muehleisen, TW, Murray, RM, Nielsen, J, Nordvik, JE, Nyberg, L, Loohuis, LMO, Ophoff, RA, Owen, MJ, Paus, T, Pausova, Z, Peralta, JM, Pike, GB, Prieto, C, Quinlan, EB, Reinbold, CS, Marques, TR, Rucker, JJH, Sachdev, PS, Sando, SB, Schofield, PR, Schork, AJ, Schumann, G, Shin, J, Shumskaya, E, Silva, AI, Sisodiya, SM, Steen, VM, Stein, DJ, Strike, LT, Suzuki, IK, Tamnes, CK, Teumer, A, Thalamuthu, A, Tordesillas-Gutierrez, D, Uhlmann, A, Ulfarsson, MO, van 't Ent, D, van den Bree, MBM, Vanderhaeghen, P, Vassos, E, Wen, W, Wittfeld, K, Wright, MJ, Agartz, I, Djurovic, S, Westlye, LT, Stefansson, H, Stefansson, K, Jacquemont, S, Thompson, PM, Andreassen, OA, Sonderby, IE, van der Meer, D, Moreau, C, Kaufmann, T, Walters, GB, Ellegaard, M, Abdellaoui, A, Ames, D, Amunts, K, Andersson, M, Armstrong, NJ, Bernard, M, Blackburn, NB, Blangero, J, Boomsma, DI, Brodaty, H, Brouwer, RM, Buelow, R, Boen, R, Cahn, W, Calhoun, VD, Caspers, S, Ching, CRK, Cichon, S, Ciufolini, S, Crespo-Facorro, B, Curran, JE, Dale, AM, Dalvie, S, Dazzan, P, de Geus, EJC, de Zubicaray, GI, de Zwarte, SMC, Desrivieres, S, Doherty, JL, Donohoe, G, Draganski, B, Ehrlich, S, Eising, E, Espeseth, T, Fejgin, K, Fisher, SE, Fladby, T, Frei, O, Frouin, V, Fukunaga, M, Gareau, T, Ge, T, Glahn, DC, Grabe, HJ, Groenewold, NA, Gustafsson, O, Haavik, J, Haberg, AK, Hall, J, Hashimoto, R, Hehir-Kwa, JY, Hibar, DP, Hillegers, MHJ, Hoffmann, P, Holleran, L, Holmes, AJ, Homuth, G, Hottenga, J-J, Hulshoff Pol, HE, Ikeda, M, Jahanshad, N, Jockwitz, C, Johansson, S, Joensson, EG, Jorgensen, NR, Kikuchi, M, Knowles, EEM, Kumar, K, Le Hellard, S, Leu, C, Linden, DEJ, Liu, J, Lundervold, A, Lundervold, AJ, Maillard, AM, Martin, NG, Martin-Brevet, S, Mather, KA, Mathias, SR, McMahon, KL, McRae, AF, Medland, SE, Meyer-Lindenberg, A, Moberget, T, Modenato, C, Sanchez, JM, Morris, DW, Muehleisen, TW, Murray, RM, Nielsen, J, Nordvik, JE, Nyberg, L, Loohuis, LMO, Ophoff, RA, Owen, MJ, Paus, T, Pausova, Z, Peralta, JM, Pike, GB, Prieto, C, Quinlan, EB, Reinbold, CS, Marques, TR, Rucker, JJH, Sachdev, PS, Sando, SB, Schofield, PR, Schork, AJ, Schumann, G, Shin, J, Shumskaya, E, Silva, AI, Sisodiya, SM, Steen, VM, Stein, DJ, Strike, LT, Suzuki, IK, Tamnes, CK, Teumer, A, Thalamuthu, A, Tordesillas-Gutierrez, D, Uhlmann, A, Ulfarsson, MO, van 't Ent, D, van den Bree, MBM, Vanderhaeghen, P, Vassos, E, Wen, W, Wittfeld, K, Wright, MJ, Agartz, I, Djurovic, S, Westlye, LT, Stefansson, H, Stefansson, K, Jacquemont, S, Thompson, PM, and Andreassen, OA
Abstract: Low-frequency 1q21.1 distal deletion and duplication copy number variant (CNV) carriers are predisposed to multiple neurodevelopmental disorders, including schizophrenia, autism and intellectual disability. Human carriers display a high prevalence of micro- and macrocephaly in deletion and duplication carriers, respectively. The underlying brain structural diversity remains largely unknown. We systematically called CNVs in 38 cohorts from the large-scale ENIGMA-CNV collaboration and the UK Biobank and identified 28 1q21.1 distal deletion and 22 duplication carriers and 37,088 non-carriers (48% male) derived from 15 distinct magnetic resonance imaging scanner sites. With standardized methods, we compared subcortical and cortical brain measures (all) and cognitive performance (UK Biobank only) between carrier groups also testing for mediation of brain structure on cognition. We identified positive dosage effects of copy number on intracranial volume (ICV) and total cortical surface area, with the largest effects in frontal and cingulate cortices, and negative dosage effects on caudate and hippocampal volumes. The carriers displayed distinct cognitive deficit profiles in cognitive tasks from the UK Biobank with intermediate decreases in duplication carriers and somewhat larger in deletion carriers-the latter potentially mediated by ICV or cortical surface area. These results shed light on pathobiological mechanisms of neurodevelopmental disorders, by demonstrating gene dose effect on specific brain structures and effect on cognitive function.
Published: 2021

15. Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders (May, 2020, 10.1038/s41380-020-0774-9)

Author: Opel, N, Thalamuthu, A, Milaneschi, Y, Grotegerd, D, Flint, C, Leenings, R, Goltermann, J, Richter, M, Hahn, T, Woditsch, G, Berger, K, Hermesdorf, M, McIntosh, A, Whalley, HC, Harris, MA, MacMaster, FP, Walter, H, Veer, IM, Frodl, T, Carballedo, A, Krug, A, Nenadic, I, Kircher, T, Aleman, A, Groenewold, NA, Stein, DJ, Soares, JC, Zunta-Soares, GB, Mwangi, B, Wu, M-J, Walter, M, Li, M, Harrison, BJ, Davey, CG, Cullen, KR, Klimes-Dougan, B, Mueller, BA, Samann, PG, Penninx, B, Nawijn, L, Veltman, DJ, Aftanas, L, Brak, IV, Filimonova, EA, Osipov, EA, Reneman, L, Schrantee, A, Grabe, HJ, van der Auwera, S, Wittfeld, K, Hosten, N, Volzke, H, Sim, K, Gotlib, IH, Sacchet, MD, Lagopoulos, J, Hatton, SN, Hickie, I, Pozzi, E, Thompson, PM, Jahanshad, N, Schmaal, L, Baune, BT, Dannlowski, U, Opel, N, Thalamuthu, A, Milaneschi, Y, Grotegerd, D, Flint, C, Leenings, R, Goltermann, J, Richter, M, Hahn, T, Woditsch, G, Berger, K, Hermesdorf, M, McIntosh, A, Whalley, HC, Harris, MA, MacMaster, FP, Walter, H, Veer, IM, Frodl, T, Carballedo, A, Krug, A, Nenadic, I, Kircher, T, Aleman, A, Groenewold, NA, Stein, DJ, Soares, JC, Zunta-Soares, GB, Mwangi, B, Wu, M-J, Walter, M, Li, M, Harrison, BJ, Davey, CG, Cullen, KR, Klimes-Dougan, B, Mueller, BA, Samann, PG, Penninx, B, Nawijn, L, Veltman, DJ, Aftanas, L, Brak, IV, Filimonova, EA, Osipov, EA, Reneman, L, Schrantee, A, Grabe, HJ, van der Auwera, S, Wittfeld, K, Hosten, N, Volzke, H, Sim, K, Gotlib, IH, Sacchet, MD, Lagopoulos, J, Hatton, SN, Hickie, I, Pozzi, E, Thompson, PM, Jahanshad, N, Schmaal, L, Baune, BT, and Dannlowski, U
Published: 2021

16. Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders Evidence through univariate and multivariate mega-analysis including 6420 participants from the ENIGMA MDD working group

Author: Opel, N, Thalamuthu, A, Milaneschi, Y, Grotegerd, D, Flint, C, Leenings, R, Goltermann, J, Richter, M, Hahn, T, Woditsch, G, Berger, K, Hermesdorf, M, McIntosh, A, Whalley, HC, Harris, MA, MacMaster, FP, Walter, H, Veer, IM, Frodl, T, Carballedo, A, Krug, A, Nenadic, I, Kircher, T, Aleman, A, Groenewold, NA, Stein, DJ, Soares, JC, Zunta-Soares, GB, Mwangi, B, Wu, M-J, Walter, M, Li, M, Harrison, BJ, Davey, CG, Cullen, KR, Klimes-Dougan, B, Mueller, BA, Saemann, PG, Penninx, B, Nawijn, L, Veltman, DJ, Aftanas, L, Brak, I, Filimonova, EA, Osipov, EA, Reneman, L, Schrantee, A, Grabe, HJ, Van der Auwera, S, Wittfeld, K, Hosten, N, Voelzke, H, Sim, K, Gotlib, IH, Sacchet, MD, Lagopoulos, J, Hatton, SN, Hickie, I, Pozzi, E, Thompson, PM, Jahanshad, N, Schmaal, L, Baune, BT, Dannlowski, U, Opel, N, Thalamuthu, A, Milaneschi, Y, Grotegerd, D, Flint, C, Leenings, R, Goltermann, J, Richter, M, Hahn, T, Woditsch, G, Berger, K, Hermesdorf, M, McIntosh, A, Whalley, HC, Harris, MA, MacMaster, FP, Walter, H, Veer, IM, Frodl, T, Carballedo, A, Krug, A, Nenadic, I, Kircher, T, Aleman, A, Groenewold, NA, Stein, DJ, Soares, JC, Zunta-Soares, GB, Mwangi, B, Wu, M-J, Walter, M, Li, M, Harrison, BJ, Davey, CG, Cullen, KR, Klimes-Dougan, B, Mueller, BA, Saemann, PG, Penninx, B, Nawijn, L, Veltman, DJ, Aftanas, L, Brak, I, Filimonova, EA, Osipov, EA, Reneman, L, Schrantee, A, Grabe, HJ, Van der Auwera, S, Wittfeld, K, Hosten, N, Voelzke, H, Sim, K, Gotlib, IH, Sacchet, MD, Lagopoulos, J, Hatton, SN, Hickie, I, Pozzi, E, Thompson, PM, Jahanshad, N, Schmaal, L, Baune, BT, and Dannlowski, U
Abstract: Emerging evidence suggests that obesity impacts brain physiology at multiple levels. Here we aimed to clarify the relationship between obesity and brain structure using structural MRI (n = 6420) and genetic data (n = 3907) from the ENIGMA Major Depressive Disorder (MDD) working group. Obesity (BMI > 30) was significantly associated with cortical and subcortical abnormalities in both mass-univariate and multivariate pattern recognition analyses independent of MDD diagnosis. The most pronounced effects were found for associations between obesity and lower temporo-frontal cortical thickness (maximum Cohen´s d (left fusiform gyrus) = -0.33). The observed regional distribution and effect size of cortical thickness reductions in obesity revealed considerable similarities with corresponding patterns of lower cortical thickness in previously published studies of neuropsychiatric disorders. A higher polygenic risk score for obesity significantly correlated with lower occipital surface area. In addition, a significant age-by-obesity interaction on cortical thickness emerged driven by lower thickness in older participants. Our findings suggest a neurobiological interaction between obesity and brain structure under physiological and pathological brain conditions.
Published: 2021

17. Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group

Author: Han, LKM, Dinga, R, Hahn, T, Ching, CRK, Eyler, LT, Aftanas, L, Aghajani, M, Aleman, A, Baune, BT, Berger, K, Brak, I, Busatto Filho, G, Carballedo, A, Connolly, CG, Couvy-Duchesne, B, Cullen, KR, Dannlowski, U, Davey, CG, Dima, D, Duran, FLS, Enneking, V, Filimonova, E, Frenzel, S, Frodl, T, Fu, CHY, Godlewska, BR, Gotlib, IH, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Hall, GB, Harrison, BJ, Hatton, SN, Hermesdorf, M, Hickie, IB, Ho, TC, Hosten, N, Jansen, A, Kaehler, C, Kircher, T, Klimes-Dougan, B, Kraemer, B, Krug, A, Lagopoulos, J, Leenings, R, MacMaster, FP, MacQueen, G, McIntosh, A, McLellan, Q, McMahon, KL, Medland, SE, Mueller, BA, Mwangi, B, Osipov, E, Portella, MJ, Pozzi, E, Reneman, L, Repple, J, Rosa, PGP, Sacchet, MD, Saemann, PG, Schnell, K, Schrantee, A, Simulionyte, E, Soares, JC, Sommer, J, Stein, DJ, Steinstraeter, O, Strike, LT, Thomopoulos, SI, van Tol, M-J, Veer, IM, Vermeiren, RRJM, Walter, H, van der Wee, NJA, van der Werff, SJA, Whalley, H, Winter, NR, Wittfeld, K, Wright, MJ, Wu, M-J, Voelzke, H, Yang, TT, Zannias, V, de Zubicaray, GI, Zunta-Soares, GB, Abe, C, Alda, M, Andreassen, OA, Boen, E, Bonnin, CM, Canales-Rodriguez, EJ, Cannon, D, Caseras, X, Chaim-Avancini, TM, Elvsashagen, T, Favre, P, Foley, SF, Fullerton, JM, Goikolea, JM, Haarman, BCM, Hajek, T, Henry, C, Houenou, J, Howells, FM, Ingvar, M, Kuplicki, R, Lafer, B, Landen, M, Machado-Vieira, R, Malt, UF, McDonald, C, Mitchell, PB, Nabulsi, L, Otaduy, MCG, Overs, BJ, Polosan, M, Pomarol-Clotet, E, Radua, J, Rive, MM, Roberts, G, Ruhe, HG, Salvador, R, Sarro, S, Satterthwaite, TD, Savitz, J, Schene, AH, Schofield, PR, Serpa, MH, Sim, K, Soeiro-de-Souza, MG, Sutherland, AN, Temmingh, HS, Timmons, GM, Uhlmann, A, Vieta, E, Wolf, DH, Zanetti, MV, Jahanshad, N, Thompson, PM, Veltman, DJ, Penninx, BWJH, Marquand, AF, Cole, JH, Schmaal, L, Han, LKM, Dinga, R, Hahn, T, Ching, CRK, Eyler, LT, Aftanas, L, Aghajani, M, Aleman, A, Baune, BT, Berger, K, Brak, I, Busatto Filho, G, Carballedo, A, Connolly, CG, Couvy-Duchesne, B, Cullen, KR, Dannlowski, U, Davey, CG, Dima, D, Duran, FLS, Enneking, V, Filimonova, E, Frenzel, S, Frodl, T, Fu, CHY, Godlewska, BR, Gotlib, IH, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Hall, GB, Harrison, BJ, Hatton, SN, Hermesdorf, M, Hickie, IB, Ho, TC, Hosten, N, Jansen, A, Kaehler, C, Kircher, T, Klimes-Dougan, B, Kraemer, B, Krug, A, Lagopoulos, J, Leenings, R, MacMaster, FP, MacQueen, G, McIntosh, A, McLellan, Q, McMahon, KL, Medland, SE, Mueller, BA, Mwangi, B, Osipov, E, Portella, MJ, Pozzi, E, Reneman, L, Repple, J, Rosa, PGP, Sacchet, MD, Saemann, PG, Schnell, K, Schrantee, A, Simulionyte, E, Soares, JC, Sommer, J, Stein, DJ, Steinstraeter, O, Strike, LT, Thomopoulos, SI, van Tol, M-J, Veer, IM, Vermeiren, RRJM, Walter, H, van der Wee, NJA, van der Werff, SJA, Whalley, H, Winter, NR, Wittfeld, K, Wright, MJ, Wu, M-J, Voelzke, H, Yang, TT, Zannias, V, de Zubicaray, GI, Zunta-Soares, GB, Abe, C, Alda, M, Andreassen, OA, Boen, E, Bonnin, CM, Canales-Rodriguez, EJ, Cannon, D, Caseras, X, Chaim-Avancini, TM, Elvsashagen, T, Favre, P, Foley, SF, Fullerton, JM, Goikolea, JM, Haarman, BCM, Hajek, T, Henry, C, Houenou, J, Howells, FM, Ingvar, M, Kuplicki, R, Lafer, B, Landen, M, Machado-Vieira, R, Malt, UF, McDonald, C, Mitchell, PB, Nabulsi, L, Otaduy, MCG, Overs, BJ, Polosan, M, Pomarol-Clotet, E, Radua, J, Rive, MM, Roberts, G, Ruhe, HG, Salvador, R, Sarro, S, Satterthwaite, TD, Savitz, J, Schene, AH, Schofield, PR, Serpa, MH, Sim, K, Soeiro-de-Souza, MG, Sutherland, AN, Temmingh, HS, Timmons, GM, Uhlmann, A, Vieta, E, Wolf, DH, Zanetti, MV, Jahanshad, N, Thompson, PM, Veltman, DJ, Penninx, BWJH, Marquand, AF, Cole, JH, and Schmaal, L
Abstract: Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.
Published: 2021

18. ENIGMA-Sleep: Challenges, opportunities, and the road map

Author: Tahmasian, M, Aleman, A, Andreassen, OA, Arab, Z, Baillet, M, Benedetti, F, Bresser, T, Bright, J, Chee, MWL, Chylinski, D, Cheng, W, Deantoni, M, Dresler, M, Eickhoff, SB, Eickhoff, CR, Elvsashagen, T, Feng, J, Foster-Dingley, JC, Ganjgahi, H, Grabe, HJ, Groenewold, NA, Ho, TC, Hong, SB, Houenou, J, Irungu, B, Jahanshad, N, Khazaie, H, Kim, H, Koshmanova, E, Kocevska, D, Kochunov, P, Lakbila-Kamal, O, Leerssen, J, Li, M, Luik, A, Muto, V, Narbutas, J, Nilsonne, G, O'Callaghan, VS, Olsen, A, Osorio, RS, Poletti, S, Poudel, G, Reesen, JE, Reneman, L, Reyt, M, Riemann, D, Rosenzweig, I, Rostampour, M, Saberi, A, Schiel, J, Schmidt, C, Schrantee, A, Sciberras, E, Silk, TJ, Sim, K, Smevik, H, Soares, JC, Spiegelhalder, K, Stein, DJ, Talwar, P, Tamm, S, Teresi, GL, Valk, SL, Van Someren, E, Vandewalle, G, Van Egroo, M, Volzke, H, Walter, M, Wassing, R, Weber, FD, Weihs, A, Westlye, LT, Wright, MJ, Wu, M-J, Zak, N, Zarei, M, Tahmasian, M, Aleman, A, Andreassen, OA, Arab, Z, Baillet, M, Benedetti, F, Bresser, T, Bright, J, Chee, MWL, Chylinski, D, Cheng, W, Deantoni, M, Dresler, M, Eickhoff, SB, Eickhoff, CR, Elvsashagen, T, Feng, J, Foster-Dingley, JC, Ganjgahi, H, Grabe, HJ, Groenewold, NA, Ho, TC, Hong, SB, Houenou, J, Irungu, B, Jahanshad, N, Khazaie, H, Kim, H, Koshmanova, E, Kocevska, D, Kochunov, P, Lakbila-Kamal, O, Leerssen, J, Li, M, Luik, A, Muto, V, Narbutas, J, Nilsonne, G, O'Callaghan, VS, Olsen, A, Osorio, RS, Poletti, S, Poudel, G, Reesen, JE, Reneman, L, Reyt, M, Riemann, D, Rosenzweig, I, Rostampour, M, Saberi, A, Schiel, J, Schmidt, C, Schrantee, A, Sciberras, E, Silk, TJ, Sim, K, Smevik, H, Soares, JC, Spiegelhalder, K, Stein, DJ, Talwar, P, Tamm, S, Teresi, GL, Valk, SL, Van Someren, E, Vandewalle, G, Van Egroo, M, Volzke, H, Walter, M, Wassing, R, Weber, FD, Weihs, A, Westlye, LT, Wright, MJ, Wu, M-J, Zak, N, and Zarei, M
Abstract: Neuroimaging and genetics studies have advanced our understanding of the neurobiology of sleep and its disorders. However, individual studies usually have limitations to identifying consistent and reproducible effects, including modest sample sizes, heterogeneous clinical characteristics and varied methodologies. These issues call for a large-scale multi-centre effort in sleep research, in order to increase the number of samples, and harmonize the methods of data collection, preprocessing and analysis using pre-registered well-established protocols. The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium provides a powerful collaborative framework for combining datasets across individual sites. Recently, we have launched the ENIGMA-Sleep working group with the collaboration of several institutes from 15 countries to perform large-scale worldwide neuroimaging and genetics studies for better understanding the neurobiology of impaired sleep quality in population-based healthy individuals, the neural consequences of sleep deprivation, pathophysiology of sleep disorders, as well as neural correlates of sleep disturbances across various neuropsychiatric disorders. In this introductory review, we describe the details of our currently available datasets and our ongoing projects in the ENIGMA-Sleep group, and discuss both the potential challenges and opportunities of a collaborative initiative in sleep medicine.
Published: 2021

19. The genetic architecture of the human cerebral cortex

Author: Grasby, KL, Jahanshad, N, Painter, JN, Colodro-Conde, L, Bralten, J, Hibar, DP, Lind, PA, Pizzagalli, F, Ching, CRK, McMahon, MAB, Shatokhina, N, Zsembik, LCP, Thomopoulos, SI, Zhu, AH, Strike, LT, Agartz, I, Alhusaini, S, Almeida, MAA, Alnæs, D, Amlien, IK, Andersson, M, Ard, T, Armstrong, NJ, Ashley-Koch, A, Atkins, JR, Bernard, M, Brouwer, RM, Buimer, EEL, Bülow, R, Bürger, C, Cannon, DM, Chakravarty, M, Chen, Q, Cheung, JW, Couvy-Duchesne, B, Dale, AM, Dalvie, S, de Araujo, TK, de Zubicaray, GI, de Zwarte, SMC, den Braber, A, Doan, NT, Dohm, K, Ehrlich, S, Engelbrecht, HR, Erk, S, Fan, CC, Fedko, IO, Foley, SF, Ford, JM, Fukunaga, M, Garrett, ME, Ge, T, Giddaluru, S, Goldman, AL, Green, MJ, Groenewold, NA, Grotegerd, D, Gurholt, TP, Gutman, BA, Hansell, NK, Harris, MA, Harrison, MB, Haswell, CC, Hauser, M, Herms, S, Heslenfeld, DJ, Ho, NF, Hoehn, D, Hoffmann, P, Holleran, L, Hoogman, M, Hottenga, JJ, Ikeda, M, Janowitz, D, Jansen, IE, Jia, T, Jockwitz, C, Kanai, R, Karama, S, Kasperaviciute, D, Kaufmann, T, Kelly, S, Kikuchi, M, Klein, M, Knapp, M, Knodt, AR, Krämer, B, Lam, M, Lancaster, TM, Lee, PH, Lett, TA, Lewis, LB, Lopes-Cendes, I, Luciano, M, Macciardi, F, Marquand, AF, Mathias, SR, Melzer, TR, Milaneschi, Y, Grasby, KL, Jahanshad, N, Painter, JN, Colodro-Conde, L, Bralten, J, Hibar, DP, Lind, PA, Pizzagalli, F, Ching, CRK, McMahon, MAB, Shatokhina, N, Zsembik, LCP, Thomopoulos, SI, Zhu, AH, Strike, LT, Agartz, I, Alhusaini, S, Almeida, MAA, Alnæs, D, Amlien, IK, Andersson, M, Ard, T, Armstrong, NJ, Ashley-Koch, A, Atkins, JR, Bernard, M, Brouwer, RM, Buimer, EEL, Bülow, R, Bürger, C, Cannon, DM, Chakravarty, M, Chen, Q, Cheung, JW, Couvy-Duchesne, B, Dale, AM, Dalvie, S, de Araujo, TK, de Zubicaray, GI, de Zwarte, SMC, den Braber, A, Doan, NT, Dohm, K, Ehrlich, S, Engelbrecht, HR, Erk, S, Fan, CC, Fedko, IO, Foley, SF, Ford, JM, Fukunaga, M, Garrett, ME, Ge, T, Giddaluru, S, Goldman, AL, Green, MJ, Groenewold, NA, Grotegerd, D, Gurholt, TP, Gutman, BA, Hansell, NK, Harris, MA, Harrison, MB, Haswell, CC, Hauser, M, Herms, S, Heslenfeld, DJ, Ho, NF, Hoehn, D, Hoffmann, P, Holleran, L, Hoogman, M, Hottenga, JJ, Ikeda, M, Janowitz, D, Jansen, IE, Jia, T, Jockwitz, C, Kanai, R, Karama, S, Kasperaviciute, D, Kaufmann, T, Kelly, S, Kikuchi, M, Klein, M, Knapp, M, Knodt, AR, Krämer, B, Lam, M, Lancaster, TM, Lee, PH, Lett, TA, Lewis, LB, Lopes-Cendes, I, Luciano, M, Macciardi, F, Marquand, AF, Mathias, SR, Melzer, TR, and Milaneschi, Y
Abstract: The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.
Published: 2020

20. ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

Author: Thompson, PM, Jahanshad, N, Ching, CRK, Salminen, LE, Thomopoulos, SI, Bright, J, Baune, BT, Bertolin, S, Bralten, J, Bruin, WB, Buelow, R, Chen, J, Chye, Y, Dannlowski, U, de Kovel, CGF, Donohoe, G, Eyler, LT, Faraone, SV, Favre, P, Filippi, CA, Frodl, T, Garijo, D, Gil, Y, Grabe, HJ, Grasby, KL, Hajek, T, Han, LKM, Hatton, SN, Hilbert, K, Ho, TC, Holleran, L, Homuth, G, Hosten, N, Houenou, J, Ivanov, I, Jia, T, Kelly, S, Klein, M, Kwon, JS, Laansma, MA, Leerssen, J, Lueken, U, Nunes, A, Neill, JO, Opel, N, Piras, F, Postema, MC, Pozzi, E, Shatokhina, N, Soriano-Mas, C, Spalletta, G, Sun, D, Teumer, A, Tilot, AK, Tozzi, L, van der Merwe, C, Van Someren, EJW, van Wingen, GA, Voelzke, H, Walton, E, Wang, L, Winkler, AM, Wittfeld, K, Wright, MJ, Yun, J-Y, Zhang, G, Zhang-James, Y, Adhikari, BM, Agartz, I, Aghajani, M, Aleman, A, Althoff, RR, Altmann, A, Andreassen, OA, Baron, DA, Bartnik-Olson, BL, Bas-Hoogendam, J, Baskin-Sommers, AR, Bearden, CE, Berner, LA, Boedhoe, PSW, Brouwer, RM, Buitelaar, JK, Caeyenberghs, K, Cecil, CAM, Cohen, RA, Cole, JH, Conrod, PJ, De Brito, SA, de Zwarte, SMC, Dennis, EL, Desrivieres, S, Dima, D, Ehrlich, S, Esopenko, C, Fairchild, G, Fisher, SE, Fouche, J-P, Francks, C, Frangou, S, Franke, B, Garavan, HP, Glahn, DC, Groenewold, NA, Gurholt, TP, Gutman, BA, Hahn, T, Harding, IH, Hernaus, D, Hibar, DP, Hillary, FG, Hoogman, M, Pol, HE, Jalbrzikowski, M, Karkashadze, GA, Klapwijk, ET, Knickmeyer, RC, Kochunov, P, Koerte, IK, Kong, X-Z, Liew, S-L, Lin, AP, Logue, MW, Luders, E, Macciardi, F, Mackey, S, Mayer, AR, McDonald, CR, McMahon, AB, Medland, SE, Modinos, G, Morey, RA, Mueller, SC, Mukherjee, P, Namazova-Baranova, L, Nir, TM, Olsen, A, Paschou, P, Pine, DS, Pizzagalli, F, Renteria, ME, Rohrer, JD, Saemann, PG, Schmaal, L, Schumann, G, Shiroishi, MS, Sisodiya, SM, Smit, DJA, Sonderby, IE, Stein, DJ, Stein, JL, Tahmasian, M, Tate, DF, Turner, JA, van den Heuvel, OA, van der Wee, NJA, van der Werf, YD, van Erp, TGM, van Haren, NEM, van Rooij, D, van Velzen, LS, Veer, IM, Veltman, DJ, Villalon-Reina, JE, Walter, H, Whelan, CD, Wilde, EA, Zarei, M, Zelman, V, Thompson, PM, Jahanshad, N, Ching, CRK, Salminen, LE, Thomopoulos, SI, Bright, J, Baune, BT, Bertolin, S, Bralten, J, Bruin, WB, Buelow, R, Chen, J, Chye, Y, Dannlowski, U, de Kovel, CGF, Donohoe, G, Eyler, LT, Faraone, SV, Favre, P, Filippi, CA, Frodl, T, Garijo, D, Gil, Y, Grabe, HJ, Grasby, KL, Hajek, T, Han, LKM, Hatton, SN, Hilbert, K, Ho, TC, Holleran, L, Homuth, G, Hosten, N, Houenou, J, Ivanov, I, Jia, T, Kelly, S, Klein, M, Kwon, JS, Laansma, MA, Leerssen, J, Lueken, U, Nunes, A, Neill, JO, Opel, N, Piras, F, Postema, MC, Pozzi, E, Shatokhina, N, Soriano-Mas, C, Spalletta, G, Sun, D, Teumer, A, Tilot, AK, Tozzi, L, van der Merwe, C, Van Someren, EJW, van Wingen, GA, Voelzke, H, Walton, E, Wang, L, Winkler, AM, Wittfeld, K, Wright, MJ, Yun, J-Y, Zhang, G, Zhang-James, Y, Adhikari, BM, Agartz, I, Aghajani, M, Aleman, A, Althoff, RR, Altmann, A, Andreassen, OA, Baron, DA, Bartnik-Olson, BL, Bas-Hoogendam, J, Baskin-Sommers, AR, Bearden, CE, Berner, LA, Boedhoe, PSW, Brouwer, RM, Buitelaar, JK, Caeyenberghs, K, Cecil, CAM, Cohen, RA, Cole, JH, Conrod, PJ, De Brito, SA, de Zwarte, SMC, Dennis, EL, Desrivieres, S, Dima, D, Ehrlich, S, Esopenko, C, Fairchild, G, Fisher, SE, Fouche, J-P, Francks, C, Frangou, S, Franke, B, Garavan, HP, Glahn, DC, Groenewold, NA, Gurholt, TP, Gutman, BA, Hahn, T, Harding, IH, Hernaus, D, Hibar, DP, Hillary, FG, Hoogman, M, Pol, HE, Jalbrzikowski, M, Karkashadze, GA, Klapwijk, ET, Knickmeyer, RC, Kochunov, P, Koerte, IK, Kong, X-Z, Liew, S-L, Lin, AP, Logue, MW, Luders, E, Macciardi, F, Mackey, S, Mayer, AR, McDonald, CR, McMahon, AB, Medland, SE, Modinos, G, Morey, RA, Mueller, SC, Mukherjee, P, Namazova-Baranova, L, Nir, TM, Olsen, A, Paschou, P, Pine, DS, Pizzagalli, F, Renteria, ME, Rohrer, JD, Saemann, PG, Schmaal, L, Schumann, G, Shiroishi, MS, Sisodiya, SM, Smit, DJA, Sonderby, IE, Stein, DJ, Stein, JL, Tahmasian, M, Tate, DF, Turner, JA, van den Heuvel, OA, van der Wee, NJA, van der Werf, YD, van Erp, TGM, van Haren, NEM, van Rooij, D, van Velzen, LS, Veer, IM, Veltman, DJ, Villalon-Reina, JE, Walter, H, Whelan, CD, Wilde, EA, Zarei, M, and Zelman, V
Abstract: This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.
Published: 2020

21. ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing

Author: Schmaal, L, Pozzi, E, Ho, TC, van Velzen, LS, Veer, IM, Opel, N, Van Someren, EJW, Han, LKM, Aftanas, L, Aleman, A, Baune, BT, Berger, K, Blanken, TF, Capitao, L, Couvy-Duchesne, B, Cullen, KR, Dannlowski, U, Davey, C, Erwin-Grabner, T, Evans, J, Frodl, T, Fu, CHY, Godlewska, B, Gotlib, IH, Goya-Maldonado, R, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Gutman, BA, Hall, GB, Harrison, BJ, Hatton, SN, Hermesdorf, M, Hickie, IB, Hilland, E, Irungu, B, Jonassen, R, Kelly, S, Kircher, T, Klimes-Dougan, B, Krug, A, Landro, NI, Lagopoulos, J, Leerssen, J, Li, M, Linden, DEJ, MacMaster, FP, McIntosh, AM, Mehler, DMA, Nenadic, I, Penninx, BWJH, Portella, MJ, Reneman, L, Renteria, ME, Sacchet, MD, Saemann, PG, Schrantee, A, Sim, K, Soares, JC, Stein, DJ, Tozzi, L, van Der Wee, NJA, van Tol, M-J, Vermeiren, R, Vives-Gilabert, Y, Walter, H, Walter, M, Whalley, HC, Wittfeld, K, Whittle, S, Wright, MJ, Yang, TT, Zarate, C, Thomopoulos, SI, Jahanshad, N, Thompson, PM, Veltman, DJ, Schmaal, L, Pozzi, E, Ho, TC, van Velzen, LS, Veer, IM, Opel, N, Van Someren, EJW, Han, LKM, Aftanas, L, Aleman, A, Baune, BT, Berger, K, Blanken, TF, Capitao, L, Couvy-Duchesne, B, Cullen, KR, Dannlowski, U, Davey, C, Erwin-Grabner, T, Evans, J, Frodl, T, Fu, CHY, Godlewska, B, Gotlib, IH, Goya-Maldonado, R, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Gutman, BA, Hall, GB, Harrison, BJ, Hatton, SN, Hermesdorf, M, Hickie, IB, Hilland, E, Irungu, B, Jonassen, R, Kelly, S, Kircher, T, Klimes-Dougan, B, Krug, A, Landro, NI, Lagopoulos, J, Leerssen, J, Li, M, Linden, DEJ, MacMaster, FP, McIntosh, AM, Mehler, DMA, Nenadic, I, Penninx, BWJH, Portella, MJ, Reneman, L, Renteria, ME, Sacchet, MD, Saemann, PG, Schrantee, A, Sim, K, Soares, JC, Stein, DJ, Tozzi, L, van Der Wee, NJA, van Tol, M-J, Vermeiren, R, Vives-Gilabert, Y, Walter, H, Walter, M, Whalley, HC, Wittfeld, K, Whittle, S, Wright, MJ, Yang, TT, Zarate, C, Thomopoulos, SI, Jahanshad, N, Thompson, PM, and Veltman, DJ
Abstract: A key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates of major depressive disorder (MDD). Identifying measurable indicators of brain processes associated with MDD could facilitate the detection of individuals at risk, and the development of novel treatments, the monitoring of treatment effects, and predicting who might benefit most from treatments that target specific brain mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies and a lack of reproducible findings have hindered progress. Here, we discuss the work of the ENIGMA Major Depressive Disorder (MDD) Consortium, which was established to address issues of poor replication, unreliable results, and overestimation of effect sizes in previous studies. The ENIGMA MDD Consortium currently includes data from 45 MDD study cohorts from 14 countries across six continents. The primary aim of ENIGMA MDD is to identify structural and functional brain alterations associated with MDD that can be reliably detected and replicated across cohorts worldwide. A secondary goal is to investigate how demographic, genetic, clinical, psychological, and environmental factors affect these associations. In this review, we summarize findings of the ENIGMA MDD disease working group to date and discuss future directions. We also highlight the challenges and benefits of large-scale data sharing for mental health research.
Published: 2020

22. Interactive impact of childhood maltreatment, depression, and age on cortical brain structure: mega-analytic findings from a large multi-site cohort

Author: Tozzi, L, Garczarek, L, Janowitz, D, Stein, DJ, Wittfeld, K, Dobrowolny, H, Lagopoulos, J, Hatton, SN, Hickie, IB, Carballedo, A, Brooks, SJ, Vuletic, D, Uhlmann, A, Veer, IM, Walter, H, Buelow, R, Voelzke, H, Klinger-Koenig, J, Schnell, K, Schoepf, D, Grotegerd, D, Opel, N, Dannlowski, U, Kugel, H, Schramm, E, Konrad, C, Kircher, T, Jueksel, DI, Nenadic, I, Krug, A, Hahn, T, Steinstraeter, O, Redlich, R, Zaremba, D, Zurowski, B, Fu, CHY, Dima, D, Cole, J, Grabe, HJ, Connolly, CG, Yang, TT, Ho, TC, Lewinn, KZ, Li, M, Groenewold, NA, Salminen, LE, Walter, M, Simmons, AN, van Erp, TGM, Jahanshad, N, Baune, BT, van der Wee, NJA, van Tol, M-J, Penninx, BWJH, Hibar, DP, Thompson, PM, Veltman, DJ, Schmaal, L, Frodl, T, Tozzi, L, Garczarek, L, Janowitz, D, Stein, DJ, Wittfeld, K, Dobrowolny, H, Lagopoulos, J, Hatton, SN, Hickie, IB, Carballedo, A, Brooks, SJ, Vuletic, D, Uhlmann, A, Veer, IM, Walter, H, Buelow, R, Voelzke, H, Klinger-Koenig, J, Schnell, K, Schoepf, D, Grotegerd, D, Opel, N, Dannlowski, U, Kugel, H, Schramm, E, Konrad, C, Kircher, T, Jueksel, DI, Nenadic, I, Krug, A, Hahn, T, Steinstraeter, O, Redlich, R, Zaremba, D, Zurowski, B, Fu, CHY, Dima, D, Cole, J, Grabe, HJ, Connolly, CG, Yang, TT, Ho, TC, Lewinn, KZ, Li, M, Groenewold, NA, Salminen, LE, Walter, M, Simmons, AN, van Erp, TGM, Jahanshad, N, Baune, BT, van der Wee, NJA, van Tol, M-J, Penninx, BWJH, Hibar, DP, Thompson, PM, Veltman, DJ, Schmaal, L, and Frodl, T
Abstract: BACKGROUND: Childhood maltreatment (CM) plays an important role in the development of major depressive disorder (MDD). The aim of this study was to examine whether CM severity and type are associated with MDD-related brain alterations, and how they interact with sex and age. METHODS: Within the ENIGMA-MDD network, severity and subtypes of CM using the Childhood Trauma Questionnaire were assessed and structural magnetic resonance imaging data from patients with MDD and healthy controls were analyzed in a mega-analysis comprising a total of 3872 participants aged between 13 and 89 years. Cortical thickness and surface area were extracted at each site using FreeSurfer. RESULTS: CM severity was associated with reduced cortical thickness in the banks of the superior temporal sulcus and supramarginal gyrus as well as with reduced surface area of the middle temporal lobe. Participants reporting both childhood neglect and abuse had a lower cortical thickness in the inferior parietal lobe, middle temporal lobe, and precuneus compared to participants not exposed to CM. In males only, regardless of diagnosis, CM severity was associated with higher cortical thickness of the rostral anterior cingulate cortex. Finally, a significant interaction between CM and age in predicting thickness was seen across several prefrontal, temporal, and temporo-parietal regions. CONCLUSIONS: Severity and type of CM may impact cortical thickness and surface area. Importantly, CM may influence age-dependent brain maturation, particularly in regions related to the default mode network, perception, and theory of mind.
Published: 2020

23. White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group

Author: van Velzen, LS, Kelly, S, Isaev, D, Aleman, A, Aftanas, L, Bauer, J, Baune, BT, Brak, I, Carballedo, A, Connolly, CG, Couvy-Duchesne, B, Cullen, KR, Danilenko, K, Dannlowski, U, Enneking, V, Filimonova, E, Foerster, K, Frodl, T, Gotlib, IH, Groenewold, NA, Grotegerd, D, Harris, MA, Hatton, SN, Hawkins, EL, Hickie, IB, Ho, TC, Jansen, A, Kircher, T, Klimes-Dougan, B, Kochunov, P, Krug, A, Lagopoulos, J, Lee, R, Lett, TA, Li, M, MacMaster, FP, Martin, NG, McIntosh, AM, McLellan, Q, Meinert, S, Nenadic, I, Osipov, E, Penninx, BWJH, Portella, MJ, Repple, J, Roos, A, Sacchet, MD, Saemann, PG, Schnell, K, Shen, X, Sim, K, Stein, DJ, van Tol, M-J, Tomyshev, AS, Tozzi, L, Veer, IM, Vermeiren, R, Vives-Gilabert, Y, Walter, H, Walter, M, van der Wee, NJA, van der Werff, SJA, Schreiner, MW, Whalley, HC, Wright, MJ, Yang, TT, Zhu, A, Veltman, DJ, Thompson, PM, Jahanshad, N, Schmaal, L, van Velzen, LS, Kelly, S, Isaev, D, Aleman, A, Aftanas, L, Bauer, J, Baune, BT, Brak, I, Carballedo, A, Connolly, CG, Couvy-Duchesne, B, Cullen, KR, Danilenko, K, Dannlowski, U, Enneking, V, Filimonova, E, Foerster, K, Frodl, T, Gotlib, IH, Groenewold, NA, Grotegerd, D, Harris, MA, Hatton, SN, Hawkins, EL, Hickie, IB, Ho, TC, Jansen, A, Kircher, T, Klimes-Dougan, B, Kochunov, P, Krug, A, Lagopoulos, J, Lee, R, Lett, TA, Li, M, MacMaster, FP, Martin, NG, McIntosh, AM, McLellan, Q, Meinert, S, Nenadic, I, Osipov, E, Penninx, BWJH, Portella, MJ, Repple, J, Roos, A, Sacchet, MD, Saemann, PG, Schnell, K, Shen, X, Sim, K, Stein, DJ, van Tol, M-J, Tomyshev, AS, Tozzi, L, Veer, IM, Vermeiren, R, Vives-Gilabert, Y, Walter, H, Walter, M, van der Wee, NJA, van der Werff, SJA, Schreiner, MW, Whalley, HC, Wright, MJ, Yang, TT, Zhu, A, Veltman, DJ, Thompson, PM, Jahanshad, N, and Schmaal, L
Abstract: Alterations in white matter (WM) microstructure have been implicated in the pathophysiology of major depressive disorder (MDD). However, previous findings have been inconsistent, partially due to low statistical power and the heterogeneity of depression. In the largest multi-site study to date, we examined WM anisotropy and diffusivity in 1305 MDD patients and 1602 healthy controls (age range 12-88 years) from 20 samples worldwide, which included both adults and adolescents, within the MDD Working Group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium. Processing of diffusion tensor imaging (DTI) data and statistical analyses were harmonized across sites and effects were meta-analyzed across studies. We observed subtle, but widespread, lower fractional anisotropy (FA) in adult MDD patients compared with controls in 16 out of 25 WM tracts of interest (Cohen's d between 0.12 and 0.26). The largest differences were observed in the corpus callosum and corona radiata. Widespread higher radial diffusivity (RD) was also observed (all Cohen's d between 0.12 and 0.18). Findings appeared to be driven by patients with recurrent MDD and an adult age of onset of depression. White matter microstructural differences in a smaller sample of adolescent MDD patients and controls did not survive correction for multiple testing. In this coordinated and harmonized multisite DTI study, we showed subtle, but widespread differences in WM microstructure in adult MDD, which may suggest structural disconnectivity in MDD.
Published: 2020

24. Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA consortium

Author: Kong, XZ, Mathias, SR, Guadalupe, T, Abé, C, Agartz, I, Akudjedu, TN, Aleman, A, Alhusaini, S, Allen, NB, Ames, D, Andreassen, OA, Vasquez, AA, Armstrong, NJ, Bergo, F, Bastin, ME, Batalla, A, Bauer, J, Baune, BT, Baur-Streubel, R, Biederman, J, Blaine, SK, Boedhoe, P, Bøen, E, Bose, A, Bralten, J, Brandeis, D, Brem, S, Brodaty, H, Yüksel, D, Brooks, SJ, Buitelaar, J, Bürger, C, Bülow, R, Calhoun, V, Calvo, A, Canales-Rodríguez, EJ, Canive, JM, Cannon, DM, Caparelli, EC, Castellanos, FX, Cavalleri, GL, Cendes, F, Chaim-Avancini, TM, Chantiluke, K, Chen, QL, Chen, X, Cheng, Y, Christakou, A, Clark, VP, Coghill, D, Connolly, CG, Conzelmann, A, Córdova-Palomera, A, Cousijn, J, Crow, T, Cubillo, A, Dale, A, Dannlowski, U, De Bruttopilo, SA, De Zeeuw, P, Deary, IJ, Delanty, N, Demeter, DV, Di Martino, A, Dickie, EW, Dietsche, B, Doan, NT, Doherty, CP, Doyle, A, Durston, S, Earl, E, Ehrlich, S, Ekman, CJ, Elvsåshagen, T, Epstein, JN, Fair, DA, Faraone, SV, Fernández, G, Filho, GB, Förster, K, Fouche, JP, Foxe, JJ, Frodl, T, Fuentes-Claramonte, P, Fullerton, J, Garavan, H, Garcia, DDS, Gotlib, IH, Goudriaan, AE, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Gurholt, T, Haavik, J, Hahn, T, Hansell, NK, Harris, MA, Hartman, CA, Hernández, MDCV, Kong, XZ, Mathias, SR, Guadalupe, T, Abé, C, Agartz, I, Akudjedu, TN, Aleman, A, Alhusaini, S, Allen, NB, Ames, D, Andreassen, OA, Vasquez, AA, Armstrong, NJ, Bergo, F, Bastin, ME, Batalla, A, Bauer, J, Baune, BT, Baur-Streubel, R, Biederman, J, Blaine, SK, Boedhoe, P, Bøen, E, Bose, A, Bralten, J, Brandeis, D, Brem, S, Brodaty, H, Yüksel, D, Brooks, SJ, Buitelaar, J, Bürger, C, Bülow, R, Calhoun, V, Calvo, A, Canales-Rodríguez, EJ, Canive, JM, Cannon, DM, Caparelli, EC, Castellanos, FX, Cavalleri, GL, Cendes, F, Chaim-Avancini, TM, Chantiluke, K, Chen, QL, Chen, X, Cheng, Y, Christakou, A, Clark, VP, Coghill, D, Connolly, CG, Conzelmann, A, Córdova-Palomera, A, Cousijn, J, Crow, T, Cubillo, A, Dale, A, Dannlowski, U, De Bruttopilo, SA, De Zeeuw, P, Deary, IJ, Delanty, N, Demeter, DV, Di Martino, A, Dickie, EW, Dietsche, B, Doan, NT, Doherty, CP, Doyle, A, Durston, S, Earl, E, Ehrlich, S, Ekman, CJ, Elvsåshagen, T, Epstein, JN, Fair, DA, Faraone, SV, Fernández, G, Filho, GB, Förster, K, Fouche, JP, Foxe, JJ, Frodl, T, Fuentes-Claramonte, P, Fullerton, J, Garavan, H, Garcia, DDS, Gotlib, IH, Goudriaan, AE, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Gurholt, T, Haavik, J, Hahn, T, Hansell, NK, Harris, MA, Hartman, CA, and Hernández, MDCV
Abstract: Hemispheric asymmetry is a cardinal feature of human brain organization. Altered brain asymmetry has also been linked to some cognitive and neuropsychiatric disorders. Here, the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium presents the largest-ever analysis of cerebral cortical asymmetry and its variability across individuals. Cortical thickness and surface area were assessed in MRI scans of 17,141 healthy individuals from 99 datasets worldwide. Results revealed widespread asymmetries at both hemispheric and regional levels, with a generally thicker cortex but smaller surface area in the left hemisphere relative to the right. Regionally, asymmetries of cortical thickness and/or surface area were found in the inferior frontal gyrus, transverse temporal gyrus, parahippocampal gyrus, and entorhinal cortex. These regions are involved in lateralized functions, including language and visuospatial processing. In addition to population-level asymmetries, variability in brain asymmetry was related to sex, age, and intracranial volume. Interestingly, we did not find significant associations between asymmetries and handedness. Finally, with two independent pedigree datasets (n = 1,443 and 1,113, respectively), we found several asymmetries showing significant, replicable heritability. The structural asymmetries identified and their variabilities and heritability provide a reference resource for future studies on the genetic basis of brain asymmetry and altered laterality in cognitive, neurological, and psychiatric disorders.
Published: 2018

25. Classification of major depressive disorder via multi-site weighted LASSO model

Author: Zhu, D, Riedel, BC, Jahanshad, N, Groenewold, NA, Stein, DJ, Gotlib, IH, Sacchet, MD, Dima, D, Cole, JH, Fu, CHY, Walter, H, Veer, IM, Frodl, T, Schmaal, L, Veltman, DJ, Thompson, PM, Zhu, D, Riedel, BC, Jahanshad, N, Groenewold, NA, Stein, DJ, Gotlib, IH, Sacchet, MD, Dima, D, Cole, JH, Fu, CHY, Walter, H, Veer, IM, Frodl, T, Schmaal, L, Veltman, DJ, and Thompson, PM
Abstract: Large-scale collaborative analysis of brain imaging data, in psychiatry and neurology, offers a new source of statistical power to discover features that boost accuracy in disease classification, differential diagnosis, and outcome prediction. However, due to data privacy regulations or limited accessibility to large datasets across the world, it is challenging to efficiently integrate distributed information. Here we propose a novel classification framework through multi-site weighted LASSO: each site performs an iterative weighted LASSO for feature selection separately. Within each iteration, the classification result and the selected features are collected to update the weighting parameters for each feature. This new weight is used to guide the LASSO process at the next iteration. Only the features that help to improve the classification accuracy are preserved. In tests on data from five sites (299 patients with major depressive disorder (MDD) and 258 normal controls), our method boosted classification accuracy for MDD by 4.9% on average. This result shows the potential of the proposed new strategy as an effective and practical collaborative platform for machine learning on large scale distributed imaging and biobank data.
Published: 2017

26. Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries.

Author: García-Marín LM, Campos AI, Diaz-Torres S, Rabinowitz JA, Ceja Z, Mitchell BL, Grasby KL, Thorp JG, Agartz I, Alhusaini S, Ames D, Amouyel P, Andreassen OA, Arfanakis K, Arias-Vasquez A, Armstrong NJ, Athanasiu L, Bastin ME, Beiser AS, Bennett DA, Bis JC, Boks MPM, Boomsma DI, Brodaty H, Brouwer RM, Buitelaar JK, Burkhardt R, Cahn W, Calhoun VD, Carmichael OT, Chakravarty M, Chen Q, Ching CRK, Cichon S, Crespo-Facorro B, Crivello F, Dale AM, Smith GD, de Geus EJC, De Jager PL, de Zubicaray GI, Debette S, DeCarli C, Depondt C, Desrivières S, Djurovic S, Ehrlich S, Erk S, Espeseth T, Fernández G, Filippi I, Fisher SE, Fleischman DA, Fletcher E, Fornage M, Forstner AJ, Francks C, Franke B, Ge T, Goldman AL, Grabe HJ, Green RC, Grimm O, Groenewold NA, Gruber O, Gudnason V, Håberg AK, Haukvik UK, Heinz A, Hibar DP, Hilal S, Himali JJ, Ho BC, Hoehn DF, Hoekstra PJ, Hofer E, Hoffmann W, Holmes AJ, Homuth G, Hosten N, Ikram MK, Ipser JC, Jack CR Jr, Jahanshad N, Jönsson EG, Kahn RS, Kanai R, Klein M, Knol MJ, Launer LJ, Lawrie SM, Hellard SL, Lee PH, Lemaître H, Li S, Liewald DCM, Lin H, Longstreth WT Jr, Lopez OL, Luciano M, Maillard P, Marquand AF, Martin NG, Martinot JL, Mather KA, Mattay VS, McMahon KL, Mecocci P, Melle I, Meyer-Lindenberg A, Mirza-Schreiber N, Milaneschi Y, Mosley TH, Mühleisen TW, Müller-Myhsok B, Maniega SM, Nauck M, Nho K, Niessen WJ, Nöthen MM, Nyquist PA, Oosterlaan J, Pandolfo M, Paus T, Pausova Z, Penninx BWJH, Pike GB, Psaty BM, Pütz B, Reppermund S, Rietschel MD, Risacher SL, Romanczuk-Seiferth N, Romero-Garcia R, Roshchupkin GV, Rotter JI, Sachdev PS, Sämann PG, Saremi A, Sargurupremraj M, Saykin AJ, Schmaal L, Schmidt H, Schmidt R, Schofield PR, Scholz M, Schumann G, Schwarz E, Shen L, Shin J, Sisodiya SM, Smith AV, Smoller JW, Soininen HS, Steen VM, Stein DJ, Stein JL, Thomopoulos SI, Toga AW, Tordesillas-Gutiérrez D, Trollor JN, Valdes-Hernandez MC, van T Ent D, van Bokhoven H, van der Meer D, van der Wee NJA, Vázquez-Bourgon J, Veltman DJ, Vernooij MW, Villringer A, Vinke LN, Völzke H, Walter H, Wardlaw JM, Weinberger DR, Weiner MW, Wen W, Westlye LT, Westman E, White T, Witte AV, Wolf C, Yang J, Zwiers MP, Ikram MA, Seshadri S, Thompson PM, Satizabal CL, Medland SE, and Rentería ME
Subjects: Humans, Female, Male, Organ Size genetics, Attention Deficit Disorder with Hyperactivity genetics, Attention Deficit Disorder with Hyperactivity pathology, Parkinson Disease genetics, Parkinson Disease pathology, Polymorphism, Single Nucleotide, Genomics methods, Adult, Genetic Predisposition to Disease, Middle Aged, White People genetics, Genome-Wide Association Study, Multifactorial Inheritance genetics, Brain pathology
Abstract: Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. Here we performed genome-wide association studies meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signaling and brain aging-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse ancestries. We observed causal genetic effects of brain volumes with Parkinson's disease and attention-deficit/hyperactivity disorder. Findings implicate specific gene expression patterns in brain development and genetic variants in comorbid neuropsychiatric disorders, which could point to a brain substrate and region of action for risk genes implicated in brain diseases., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)
Published: 2024
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27. Cortical and Subcortical Brain Alterations in Specific Phobia and Its Animal and Blood-Injection-Injury Subtypes: A Mega-Analysis From the ENIGMA Anxiety Working Group.

Author: Hilbert K, Boeken OJ, Langhammer T, Groenewold NA, Bas-Hoogendam JM, Aghajani M, Zugman A, Åhs F, Arolt V, Beesdo-Baum K, Björkstrand J, Blackford JU, Blanco-Hinojo L, Böhnlein J, Bülow R, Cano M, Cardoner N, Caseras X, Dannlowski U, Domschke K, Fehm L, Feola B, Fredrikson M, Goossens L, Grabe HJ, Grotegerd D, Gur RE, Hamm AO, Harrewijn A, Heinig I, Herrmann MJ, Hofmann D, Jackowski AP, Jansen A, Kaczkurkin AN, Kindt M, Kingsley EN, Kircher T, Klahn AL, Koelkebeck K, Krug A, Kugel H, Larsen B, Leehr EJ, Leonhardt L, Lotze M, Margraf J, Michałowski J, Muehlhan M, Nenadić I, Pan PM, Pauli P, Peñate W, Pittig A, Plag J, Pujol J, Richter J, Rivero FL, Salum GA, Satterthwaite TD, Schäfer A, Schäfer J, Schienle A, Schneider S, Schrammen E, Schruers K, Schulz SM, Seidl E, Stark RM, Stein F, Straube B, Straube T, Ströhle A, Suchan B, Thomopoulos SI, Ventura-Bort C, Visser R, Völzke H, Wabnegger A, Wannemüller A, Wendt J, Wiemer J, Wittchen HU, Wittfeld K, Wright B, Yang Y, Zilverstand A, Zwanzger P, Veltman DJ, Winkler AM, Pine DS, Jahanshad N, Thompson PM, Stein DJ, Van der Wee NJA, and Lueken U
Subjects: Humans, Adult, Female, Male, Child, Adolescent, Young Adult, Middle Aged, Brain pathology, Brain diagnostic imaging, Aged, Child, Preschool, Aged, 80 and over, Cerebral Cortex pathology, Cerebral Cortex diagnostic imaging, Animals, Case-Control Studies, Phobic Disorders pathology, Magnetic Resonance Imaging
Abstract: Objective: Specific phobia is a common anxiety disorder, but the literature on associated brain structure alterations exhibits substantial gaps. The ENIGMA Anxiety Working Group examined brain structure differences between individuals with specific phobias and healthy control subjects as well as between the animal and blood-injection-injury (BII) subtypes of specific phobia. Additionally, the authors investigated associations of brain structure with symptom severity and age (youths vs. adults)., Methods: Data sets from 31 original studies were combined to create a final sample with 1,452 participants with phobia and 2,991 healthy participants (62.7% female; ages 5-90). Imaging processing and quality control were performed using established ENIGMA protocols. Subcortical volumes as well as cortical surface area and thickness were examined in a preregistered analysis., Results: Compared with the healthy control group, the phobia group showed mostly smaller subcortical volumes, mixed surface differences, and larger cortical thickness across a substantial number of regions. The phobia subgroups also showed differences, including, as hypothesized, larger medial orbitofrontal cortex thickness in BII phobia (N=182) compared with animal phobia (N=739). All findings were driven by adult participants; no significant results were observed in children and adolescents., Conclusions: Brain alterations associated with specific phobia exceeded those of other anxiety disorders in comparable analyses in extent and effect size and were not limited to reductions in brain structure. Moreover, phenomenological differences between phobia subgroups were reflected in diverging neural underpinnings, including brain areas related to fear processing and higher cognitive processes. The findings implicate brain structure alterations in specific phobia, although subcortical alterations in particular may also relate to broader internalizing psychopathology.
Published: 2024
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28. Subcortical Brain Volumes and Neurocognitive Function in Children With Perinatal HIV Exposure: A Population-Based Cohort Study in South Africa.

Author: Wedderburn CJ, Yeung S, Groenewold NA, Rehman AM, Subramoney S, Fouche JP, Joshi SH, Narr KL, Hoffman N, Roos A, Gibb DM, Zar HJ, Stein DJ, and Donald KA
Abstract: Background: Children who are HIV-exposed and uninfected (HEU) are at risk for early neurodevelopmental impairment. Smaller basal ganglia nuclei have been reported in neonates who are HEU compared to HIV-unexposed (HU); however, neuroimaging studies outside infancy are scarce. We examined subcortical brain structures and associations with neurocognition in children who are HEU., Methods: This neuroimaging study was nested within the Drakenstein Child Health Study birth cohort in South Africa. We compared (T1-weighted) magnetic resonance imaging-derived subcortical brain volumes between children who were HEU (n = 70) and HU (n = 92) at age 2-3 years using linear regression. Brain volumes were correlated with neurodevelopmental outcomes measured with the Bayley Scales of Infant and Toddler Development III., Results: Compared to HU children, on average children who were HEU had 3% lower subcortical grey matter volumes. Analyses of individual structures found smaller volume of the putamen nucleus in the basal ganglia (-5% difference, P = .016) and the hippocampus (-3% difference, P = .044), which held on adjustment for potential confounders ( P < .05). Maternal viremia and lower CD4 count in pregnancy were associated with smaller child putamen volumes. Children who were HEU had lower language scores than HU; putamen and hippocampus volumes were positively correlated with language outcomes., Conclusions: Overall, children who are HEU had a pattern of smaller subcortical volumes in the basal ganglia and hippocampal regions compared to HU children, which correlated with language function. Findings suggest that optimizing maternal perinatal HIV care is important for child brain development. Further studies are needed to investigate underlying mechanisms and long-term outcomes., Competing Interests: Potential conflicts of interest. D. J. S. has received research grants and/or consultancy honoraria from Discovery Vitality, Johnson & Johnson, Kanna, L’Oreal, Lundbeck, Orion, Sanofi, Servier, Takeda, and Vistagen. All other authors report no potential conflicts., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)
Published: 2024
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29. Beyond the Global Brain Differences: Intraindividual Variability Differences in 1q21.1 Distal and 15q11.2 BP1-BP2 Deletion Carriers.

Author: Boen R, Kaufmann T, van der Meer D, Frei O, Agartz I, Ames D, Andersson M, Armstrong NJ, Artiges E, Atkins JR, Bauer J, Benedetti F, Boomsma DI, Brodaty H, Brosch K, Buckner RL, Cairns MJ, Calhoun V, Caspers S, Cichon S, Corvin AP, Crespo-Facorro B, Dannlowski U, David FS, de Geus EJC, de Zubicaray GI, Desrivières S, Doherty JL, Donohoe G, Ehrlich S, Eising E, Espeseth T, Fisher SE, Forstner AJ, Fortaner-Uyà L, Frouin V, Fukunaga M, Ge T, Glahn DC, Goltermann J, Grabe HJ, Green MJ, Groenewold NA, Grotegerd D, Grøntvedt GR, Hahn T, Hashimoto R, Hehir-Kwa JY, Henskens FA, Holmes AJ, Håberg AK, Haavik J, Jacquemont S, Jansen A, Jockwitz C, Jönsson EG, Kikuchi M, Kircher T, Kumar K, Le Hellard S, Leu C, Linden DE, Liu J, Loughnan R, Mather KA, McMahon KL, McRae AF, Medland SE, Meinert S, Moreau CA, Morris DW, Mowry BJ, Mühleisen TW, Nenadić I, Nöthen MM, Nyberg L, Ophoff RA, Owen MJ, Pantelis C, Paolini M, Paus T, Pausova Z, Persson K, Quidé Y, Marques TR, Sachdev PS, Sando SB, Schall U, Scott RJ, Selbæk G, Shumskaya E, Silva AI, Sisodiya SM, Stein F, Stein DJ, Straube B, Streit F, Strike LT, Teumer A, Teutenberg L, Thalamuthu A, Tooney PA, Tordesillas-Gutierrez D, Trollor JN, van 't Ent D, van den Bree MBM, van Haren NEM, Vázquez-Bourgon J, Völzke H, Wen W, Wittfeld K, Ching CRK, Westlye LT, Thompson PM, Bearden CE, Selmer KK, Alnæs D, Andreassen OA, and Sønderby IE
Subjects: Humans, Brain diagnostic imaging, Magnetic Resonance Imaging, Chromosomes, Human, Pair 15, DNA Copy Number Variations, Chromosome Deletion, Abnormalities, Multiple
Abstract: Background: Carriers of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants exhibit regional and global brain differences compared with noncarriers. However, interpreting regional differences is challenging if a global difference drives the regional brain differences. Intraindividual variability measures can be used to test for regional differences beyond global differences in brain structure., Methods: Magnetic resonance imaging data were used to obtain regional brain values for 1q21.1 distal deletion (n = 30) and duplication (n = 27) and 15q11.2 BP1-BP2 deletion (n = 170) and duplication (n = 243) carriers and matched noncarriers (n = 2350). Regional intra-deviation scores, i.e., the standardized difference between an individual's regional difference and global difference, were used to test for regional differences that diverge from the global difference., Results: For the 1q21.1 distal deletion carriers, cortical surface area for regions in the medial visual cortex, posterior cingulate, and temporal pole differed less and regions in the prefrontal and superior temporal cortex differed more than the global difference in cortical surface area. For the 15q11.2 BP1-BP2 deletion carriers, cortical thickness in regions in the medial visual cortex, auditory cortex, and temporal pole differed less and the prefrontal and somatosensory cortex differed more than the global difference in cortical thickness., Conclusions: We find evidence for regional effects beyond differences in global brain measures in 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants. The results provide new insight into brain profiling of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants, with the potential to increase understanding of the mechanisms involved in altered neurodevelopment., (Copyright © 2023. Published by Elsevier Inc.)
Published: 2024
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30. A global multicohort study to map subcortical brain development and cognition in infancy and early childhood.

Author: Alex AM, Aguate F, Botteron K, Buss C, Chong YS, Dager SR, Donald KA, Entringer S, Fair DA, Fortier MV, Gaab N, Gilmore JH, Girault JB, Graham AM, Groenewold NA, Hazlett H, Lin W, Meaney MJ, Piven J, Qiu A, Rasmussen JM, Roos A, Schultz RT, Skeide MA, Stein DJ, Styner M, Thompson PM, Turesky TK, Wadhwa PD, Zar HJ, Zöllei L, de Los Campos G, and Knickmeyer RC
Subjects: Male, Female, Humans, Infant, Newborn, Child, Preschool, Child, Cognition, Brain diagnostic imaging, Neuroimaging, Magnetic Resonance Imaging, Premature Birth
Abstract: The human brain grows quickly during infancy and early childhood, but factors influencing brain maturation in this period remain poorly understood. To address this gap, we harmonized data from eight diverse cohorts, creating one of the largest pediatric neuroimaging datasets to date focused on birth to 6 years of age. We mapped the developmental trajectory of intracranial and subcortical volumes in ∼2,000 children and studied how sociodemographic factors and adverse birth outcomes influence brain structure and cognition. The amygdala was the first subcortical volume to mature, whereas the thalamus exhibited protracted development. Males had larger brain volumes than females, and children born preterm or with low birthweight showed catch-up growth with age. Socioeconomic factors exerted region- and time-specific effects. Regarding cognition, males scored lower than females; preterm birth affected all developmental areas tested, and socioeconomic factors affected visual reception and receptive language. Brain-cognition correlations revealed region-specific associations., (© 2023. The Author(s).)
Published: 2024
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31. Complex PTSD as mediator of psychosis after childhood trauma.

Author: Groenewold NA
Subjects: Humans, Adverse Childhood Experiences, Stress Disorders, Post-Traumatic, Psychotic Disorders
Abstract: Competing Interests: I report grants from the US National Institutes of Health (R01MH131022), Start-up Emerging Research Award from the University of Cape Town, and the Carnegie Developing Emerging Academic Leaders Award from the Carnegie Corporation New York, NY, USA.
Published: 2023
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32. Genetic Influences on the Developing Young Brain and Risk for Neuropsychiatric Disorders.

Author: Alex AM, Buss C, Davis EP, Campos GL, Donald KA, Fair DA, Gaab N, Gao W, Gilmore JH, Girault JB, Grewen K, Groenewold NA, Hankin BL, Ipser J, Kapoor S, Kim P, Lin W, Luo S, Norton ES, O'Connor TG, Piven J, Qiu A, Rasmussen JM, Skeide MA, Stein DJ, Styner MA, Thompson PM, Wakschlag L, and Knickmeyer R
Subjects: Female, Pregnancy, Child, Preschool, Humans, Neuroimaging methods, Phenotype, Brain diagnostic imaging, Mental Disorders genetics
Abstract: Imaging genetics provides an opportunity to discern associations between genetic variants and brain imaging phenotypes. Historically, the field has focused on adults and adolescents; very few imaging genetics studies have focused on brain development in infancy and early childhood (from birth to age 6 years). This is an important knowledge gap because developmental changes in the brain during the prenatal and early postnatal period are regulated by dynamic gene expression patterns that likely play an important role in establishing an individual's risk for later psychiatric illness and neurodevelopmental disabilities. In this review, we summarize findings from imaging genetics studies spanning from early infancy to early childhood, with a focus on studies examining genetic risk for neuropsychiatric disorders. We also introduce the Organization for Imaging Genomics in Infancy (ORIGINs), a working group of the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium, which was established to facilitate large-scale imaging genetics studies in infancy and early childhood., (Copyright © 2023 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)
Published: 2023
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33. Antenatal maternal intimate partner violence exposure is associated with sex-specific alterations in brain structure among young infants: Evidence from a South African birth cohort.

Author: Hiscox LV, Fairchild G, Donald KA, Groenewold NA, Koen N, Roos A, Narr KL, Lawrence M, Hoffman N, Wedderburn CJ, Barnett W, Zar HJ, Stein DJ, and Halligan SL
Subjects: Male, Child, Infant, Pregnancy, Humans, Female, Infant, Newborn, South Africa, Mothers psychology, Brain, Birth Cohort, Intimate Partner Violence psychology
Abstract: Maternal psychological distress during pregnancy has been linked to adverse outcomes in children with evidence of sex-specific effects on brain development. Here, we investigated whether in utero exposure to intimate partner violence (IPV), a particularly severe maternal stressor, is associated with brain structure in young infants from a South African birth cohort. Exposure to IPV during pregnancy was measured in 143 mothers at 28-32 weeks' gestation and infants underwent structural and diffusion magnetic resonance imaging (mean age 3 weeks). Subcortical volumetric estimates were compared between IPV-exposed (n = 63; 52% female) and unexposed infants (n = 80; 48% female), with white matter microstructure also examined in a subsample (IPV-exposed, n = 28, 54% female; unexposed infants, n = 42, 40% female). In confound adjusted analyses, maternal IPV exposure was associated with sexually dimorphic effects in brain volumes: IPV exposure predicted a larger caudate nucleus among males but not females, and smaller amygdala among females but not males. Diffusivity alterations within white matter tracts of interest were evident in males, but not females exposed to IPV. Results were robust to the removal of mother-infant pairs with pregnancy complications. Further research is required to understand how these early alterations are linked to the sex-bias in neuropsychiatric outcomes later observed in IPV-exposed children., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Published: 2023
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34. Maternal perinatal depression and child brain structure at 2-3 years in a South African birth cohort study.

Author: Pellowski JA, Wedderburn CJ, Groenewold NA, Roos A, Subramoney S, Hoffman N, Fouche JP, Joshi SH, Woods RP, Narr KL, Zar HJ, Donald KA, and Stein DJ
Subjects: Pregnancy, Humans, Female, Child, Depression diagnostic imaging, Depression complications, Cohort Studies, South Africa, Brain diagnostic imaging, Brain pathology, Depression, Postpartum diagnostic imaging, Pregnancy Complications
Abstract: Maternal perinatal depression is associated with risk of adverse child developmental outcomes and differences in offspring brain structure. Evidence from low- and middle-income countries is lacking as is an investigation of antenatal, postnatal, and persistent depression in the same sample. In a South African birth cohort, we investigated the effect of antenatal and postpartum maternal depressive symptoms on offspring brain structure at 2-3 years of age. Magnetic resonance imaging was performed, extracting cortical thickness and surface areas in frontal cortex regions of interest and subcortical volumes using FreeSurfer software. Maternal depressive symptoms were measured using the Edinburgh Postpartum Depression Scale and the Beck Depression Inventory II antenatally and at 6-10 weeks, 6 months, 12 months, and 18 months postpartum and analyzed dichotomously and continuously. Linear regressions were used controlling for child age, sex, intracranial volume, maternal education, age, smoking, alcohol use and HIV. 146 children were included with 38 (37%) exposed to depressive symptoms antenatally and 44 (35%) exposed postnatally. Of these, 16 (13%) were exposed to both. Postpartum, but not antenatal, depressive symptoms were associated with smaller amygdala volumes in children (B = -74.73, p = 0.01). Persistent maternal depressive symptoms across pregnancy and postpartum were also independently associated with smaller amygdala volumes (B = -78.61, p = 0.047). Differences in amygdala volumes among children exposed to postnatal as well as persistent maternal depressive symptomatology underscore the importance of identifying women at-risk for depression during the entire perinatal period., (© 2023. The Author(s).)
Published: 2023
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35. Volume of subcortical brain regions in social anxiety disorder: mega-analytic results from 37 samples in the ENIGMA-Anxiety Working Group.

Author: Groenewold NA, Bas-Hoogendam JM, Amod AR, Laansma MA, Van Velzen LS, Aghajani M, Hilbert K, Oh H, Salas R, Jackowski AP, Pan PM, Salum GA, Blair JR, Blair KS, Hirsch J, Pantazatos SP, Schneier FR, Talati A, Roelofs K, Volman I, Blanco-Hinojo L, Cardoner N, Pujol J, Beesdo-Baum K, Ching CRK, Thomopoulos SI, Jansen A, Kircher T, Krug A, Nenadić I, Stein F, Dannlowski U, Grotegerd D, Lemke H, Meinert S, Winter A, Erb M, Kreifelts B, Gong Q, Lui S, Zhu F, Mwangi B, Soares JC, Wu MJ, Bayram A, Canli M, Tükel R, Westenberg PM, Heeren A, Cremers HR, Hofmann D, Straube T, Doruyter AGG, Lochner C, Peterburs J, Van Tol MJ, Gur RE, Kaczkurkin AN, Larsen B, Satterthwaite TD, Filippi CA, Gold AL, Harrewijn A, Zugman A, Bülow R, Grabe HJ, Völzke H, Wittfeld K, Böhnlein J, Dohm K, Kugel H, Schrammen E, Zwanzger P, Leehr EJ, Sindermann L, Ball TM, Fonzo GA, Paulus MP, Simmons A, Stein MB, Klumpp H, Phan KL, Furmark T, Månsson KNT, Manzouri A, Avery SN, Blackford JU, Clauss JA, Feola B, Harper JC, Sylvester CM, Lueken U, Veltman DJ, Winkler AM, Jahanshad N, Pine DS, Thompson PM, Stein DJ, and Van der Wee NJA
Subjects: Adult, Adolescent, Humans, Magnetic Resonance Imaging methods, Brain, Anxiety, Neuroimaging methods, Phobia, Social
Abstract: There is limited convergence in neuroimaging investigations into volumes of subcortical brain regions in social anxiety disorder (SAD). The inconsistent findings may arise from variations in methodological approaches across studies, including sample selection based on age and clinical characteristics. The ENIGMA-Anxiety Working Group initiated a global mega-analysis to determine whether differences in subcortical volumes can be detected in adults and adolescents with SAD relative to healthy controls. Volumetric data from 37 international samples with 1115 SAD patients and 2775 controls were obtained from ENIGMA-standardized protocols for image segmentation and quality assurance. Linear mixed-effects analyses were adjusted for comparisons across seven subcortical regions in each hemisphere using family-wise error (FWE)-correction. Mixed-effects d effect sizes were calculated. In the full sample, SAD patients showed smaller bilateral putamen volume than controls (left: d = -0.077, p FWE = 0.037; right: d = -0.104, p FWE = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = -0.034, p FWE = 0.045). Smaller bilateral putamen volumes (left: d = -0.141, p FWE < 0.001; right: d = -0.158, p FWE < 0.001) and larger bilateral pallidum volumes (left: d = 0.129, p FWE = 0.006; right: d = 0.099, p FWE = 0.046) were detected in adult SAD patients relative to controls, but no volumetric differences were apparent in adolescent SAD patients relative to controls. Comorbid anxiety disorders and age of SAD onset were additional determinants of SAD-related volumetric differences in subcortical regions. To conclude, subtle volumetric alterations in subcortical regions in SAD were detected. Heterogeneity in age and clinical characteristics may partly explain inconsistencies in previous findings. The association between alterations in subcortical volumes and SAD illness progression deserves further investigation, especially from adolescence into adulthood., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)
Published: 2023
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36. Advanced brain ageing in adult psychopathology: A systematic review and meta-analysis of structural MRI studies.

Author: Blake KV, Ntwatwa Z, Kaufmann T, Stein DJ, Ipser JC, and Groenewold NA
Subjects: Humans, Adult, Brain diagnostic imaging, Brain pathology, Magnetic Resonance Imaging methods, Aging, Psychotic Disorders complications, Depressive Disorder, Major complications
Abstract: Evidence suggests that psychopathology is associated with an advanced brain ageing process, typically mapped using machine learning models that predict an individual's age based on structural neuroimaging data. The brain predicted age difference (brain-PAD) captures the deviation of brain age from chronological age. Substantial heterogeneity between studies has introduced uncertainty regarding the magnitude of the brain-PAD in adult psychopathology. The present meta-analysis aimed to quantify structural MRI-based brain-PAD in adult psychotic and mood disorders, while addressing possible sources of heterogeneity related to diagnosis subtypes, segmentation method, age and sex. Clinical factors influencing brain ageing in axis 1 psychiatric disorders were systematically reviewed. Thirty-three studies were included for review. A random-effects meta-analysis revealed a brain-PAD of +3.12 (standard error = 0.49) years in psychotic disorders (n = 16 studies), +2.04 (0.10) years in bipolar disorder (n = 5), and +0.90 (0.20) years in major depression (n = 7). An exploratory meta-analysis found a brain-PAD of +1.57 (0.67) in first episode psychosis (n = 4), which was smaller than that observed in psychosis and schizophrenia (n = 10, +3.87 (0.61)). Patient mean age significantly explained heterogeneity in effect size estimates in psychotic disorders, but not mood disorders. The systematic review determined that clinical factors, such as higher symptom severity, may be associated with a larger brain-PAD in psychopathology. In conclusion, larger structural MRI-based brain-PAD was confirmed in adult psychopathology. Preliminary evidence was obtained that brain ageing is greater in those with prolonged duration of psychotic disorders. Accentuated brain ageing may underlie the cognitive difficulties experienced by some patients, and may be progressive in nature., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Published: 2023
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37. Shape analysis of subcortical structures in obsessive-compulsive disorder and the relationship with comorbid anxiety, depression, and medication use: A meta-analysis by the OCD Brain Imaging Consortium.

Author: Fouche JP, Groenewold NA, Sevenoaks T, Heany S, Lochner C, Alonso P, Batistuzzo MC, Cardoner N, Ching CRK, de Wit SJ, Gutman B, Hoexter MQ, Jahanshad N, Kim M, Kwon JS, Mataix-Cols D, Menchon JM, Miguel EC, Nakamae T, Phillips ML, Pujol J, Sakai Y, Yun JY, Soriano-Mas C, Thompson PM, Yamada K, Veltman DJ, van den Heuvel OA, and Stein DJ
Subjects: Anxiety diagnostic imaging, Anxiety epidemiology, Brain diagnostic imaging, Comorbidity, Humans, Magnetic Resonance Imaging methods, Neuroimaging, Depression diagnostic imaging, Depression epidemiology, Obsessive-Compulsive Disorder diagnostic imaging, Obsessive-Compulsive Disorder epidemiology
Abstract: Objective: Neuroimaging studies of obsessive-compulsive disorder (OCD) patients have highlighted the important role of deep gray matter structures. Less work has however focused on subcortical shape in OCD patients., Methods: Here we pooled brain MRI scans from 412 OCD patients and 368 controls to perform a meta-analysis utilizing the ENIGMA-Shape protocol. In addition, we investigated modulating effects of medication status, comorbid anxiety or depression, and disease duration on subcortical shape., Results: There was no significant difference in shape thickness or surface area between OCD patients and healthy controls. For the subgroup analyses, OCD patients with comorbid depression or anxiety had lower thickness of the hippocampus and caudate nucleus and higher thickness of the putamen and pallidum compared to controls. OCD patients with comorbid depression had lower shape surface area in the thalamus, caudate nucleus, putamen, hippocampus, and nucleus accumbens and higher shape surface area in the pallidum. OCD patients with comorbid anxiety had lower shape surface area in the putamen and the left caudate nucleus and higher shape surface area in the pallidum and the right caudate nucleus. Further, OCD patients on medication had lower shape thickness of the putamen, thalamus, and hippocampus and higher thickness of the pallidum and caudate nucleus, as well as lower shape surface area in the hippocampus and amygdala and higher surface area in the putamen, pallidum, and caudate nucleus compared to controls. There were no significant differences between OCD patients without co-morbid anxiety and/or depression and healthy controls on shape measures. In addition, there were also no significant differences between OCD patients not using medication and healthy controls., Conclusions: The findings here are partly consistent with prior work on brain volumes in OCD, insofar as they emphasize that alterations in subcortical brain morphology are associated with comorbidity and medication status. Further work is needed to understand the biological processes contributing to subcortical shape., (© 2022 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)
Published: 2022
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38. Publisher Correction: Brain charts for the human lifespan.

Author: Bethlehem RAI, Seidlitz J, White SR, Vogel JW, Anderson KM, Adamson C, Adler S, Alexopoulos GS, Anagnostou E, Areces-Gonzalez A, Astle DE, Auyeung B, Ayub M, Bae J, Ball G, Baron-Cohen S, Beare R, Bedford SA, Benegal V, Beyer F, Blangero J, Blesa Cábez M, Boardman JP, Borzage M, Bosch-Bayard JF, Bourke N, Calhoun VD, Chakravarty MM, Chen C, Chertavian C, Chetelat G, Chong YS, Cole JH, Corvin A, Costantino M, Courchesne E, Crivello F, Cropley VL, Crosbie J, Crossley N, Delarue M, Delorme R, Desrivieres S, Devenyi GA, Di Biase MA, Dolan R, Donald KA, Donohoe G, Dunlop K, Edwards AD, Elison JT, Ellis CT, Elman JA, Eyler L, Fair DA, Feczko E, Fletcher PC, Fonagy P, Franz CE, Galan-Garcia L, Gholipour A, Giedd J, Gilmore JH, Glahn DC, Goodyer IM, Grant PE, Groenewold NA, Gunning FM, Gur RE, Gur RC, Hammill CF, Hansson O, Hedden T, Heinz A, Henson RN, Heuer K, Hoare J, Holla B, Holmes AJ, Holt R, Huang H, Im K, Ipser J, Jack CR Jr, Jackowski AP, Jia T, Johnson KA, Jones PB, Jones DT, Kahn RS, Karlsson H, Karlsson L, Kawashima R, Kelley EA, Kern S, Kim KW, Kitzbichler MG, Kremen WS, Lalonde F, Landeau B, Lee S, Lerch J, Lewis JD, Li J, Liao W, Liston C, Lombardo MV, Lv J, Lynch C, Mallard TT, Marcelis M, Markello RD, Mathias SR, Mazoyer B, McGuire P, Meaney MJ, Mechelli A, Medic N, Misic B, Morgan SE, Mothersill D, Nigg J, Ong MQW, Ortinau C, Ossenkoppele R, Ouyang M, Palaniyappan L, Paly L, Pan PM, Pantelis C, Park MM, Paus T, Pausova Z, Paz-Linares D, Pichet Binette A, Pierce K, Qian X, Qiu J, Qiu A, Raznahan A, Rittman T, Rodrigue A, Rollins CK, Romero-Garcia R, Ronan L, Rosenberg MD, Rowitch DH, Salum GA, Satterthwaite TD, Schaare HL, Schachar RJ, Schultz AP, Schumann G, Schöll M, Sharp D, Shinohara RT, Skoog I, Smyser CD, Sperling RA, Stein DJ, Stolicyn A, Suckling J, Sullivan G, Taki Y, Thyreau B, Toro R, Traut N, Tsvetanov KA, Turk-Browne NB, Tuulari JJ, Tzourio C, Vachon-Presseau É, Valdes-Sosa MJ, Valdes-Sosa PA, Valk SL, van Amelsvoort T, Vandekar SN, Vasung L, Victoria LW, Villeneuve S, Villringer A, Vértes PE, Wagstyl K, Wang YS, Warfield SK, Warrier V, Westman E, Westwater ML, Whalley HC, Witte AV, Yang N, Yeo B, Yun H, Zalesky A, Zar HJ, Zettergren A, Zhou JH, Ziauddeen H, Zugman A, Zuo XN, Bullmore ET, and Alexander-Bloch AF
Published: 2022
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39. Newborn differential DNA methylation and subcortical brain volumes as early signs of severe neurodevelopmental delay in a South African Birth Cohort Study.

Author: Hüls A, Wedderburn CJ, Groenewold NA, Gladish N, Jones MJ, Koen N, MacIsaac JL, Lin DTS, Ramadori KE, Epstein MP, Donald KA, Kobor MS, Zar HJ, and Stein DJ
Subjects: Infant, Newborn, Humans, Cohort Studies, South Africa, Brain pathology, DNA Methylation, Birth Cohort
Abstract: Objectives: Early detection of neurodevelopmental delay is crucial for intervention and treatment strategies. We analysed associations between newborn DNA methylation (DNAm), neonatal magnetic resonance imaging (MRI) neuroimaging data, and neurodevelopment., Methods: Neurodevelopment was assessed in 161 children from the South African Drakenstein Child Health Study at 2 years of age using the Bayley Scales of Infant and Toddler Development III. We performed an epigenome-wide association study of neurodevelopmental delay using DNAm from cord blood. Subsequently, we analysed if associations between DNAm and neurodevelopmental delay were mediated by altered neonatal brain volumes (subset of 51 children)., Results: Differential DNAm at SPTBN4 (cg26971411, Δ beta = -0.024, p -value = 3.28 × 10 -08 ), and two intergenic regions (chromosome 11: cg00490349, Δ beta = -0.036, p -value = 3.02 × 10 -08 ; chromosome 17: cg15660740, Δ beta = -0.078, p -value = 6.49 × 10 -08 ) were significantly associated with severe neurodevelopmental delay. While these associations were not mediated by neonatal brain volume, neonatal caudate volumes were independently associated with neurodevelopmental delay, particularly in language ( Δ caudate volume = 165.30 mm 3 , p = 0.0443) and motor ( Δ caudate volume = 365.36 mm 3 , p -value = 0.0082) domains., Conclusions: Differential DNAm from cord blood and increased neonatal caudate volumes were independently associated with severe neurodevelopmental delay at 2 years of age. These findings suggest that neurobiological signals for severe developmental delay may be detectable in very early life.
Published: 2022
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40. Structural Brain Correlates of Childhood Inhibited Temperament: An ENIGMA-Anxiety Mega-analysis.

Author: Bas-Hoogendam JM, Bernstein R, Benson BE, Buss KA, Gunther KE, Pérez-Edgar K, Salum GA, Jackowski AP, Bressan RA, Zugman A, Degnan KA, Filippi CA, Fox NA, Henderson HA, Tang A, Zeytinoglu S, Harrewijn A, Hillegers MHJ, White T, van IJzendoorn MH, Schwartz CE, Felicione JM, DeYoung KA, Shackman AJ, Smith JF, Tillman RM, van den Berg YHM, Cillessen AHN, Roelofs K, Tyborowska A, Hill SY, Battaglia M, Tettamanti M, Dougherty LR, Jin J, Klein DN, Leung HC, Avery SN, Blackford JU, Clauss JA, Hayden EP, Liu P, Vandermeer MRJ, Goldsmith HH, Planalp EM, Nichols TE, Thompson PM, Westenberg PM, van der Wee NJA, Groenewold NA, Stein DJ, Winkler AM, and Pine DS
Subjects: Anxiety Disorders, Brain physiology, Child, Preschool, Humans, Retrospective Studies, Anxiety psychology, Temperament physiology
Abstract: Temperament involves stable behavioral and emotional tendencies that differ between individuals, which can be first observed in infancy or early childhood and relate to behavior in many contexts and over many years. 1 One of the most rigorously characterized temperament classifications relates to the tendency of individuals to avoid the unfamiliar and to withdraw from unfamiliar people, objects, and unexpected events. This temperament is referred to as behavioral inhibition or inhibited temperament (IT). 2 IT is a moderately heritable trait 1 that can be measured in multiple species. 3 In humans, levels of IT can be quantified from the first year of life through direct behavioral observations or reports by caregivers or teachers. Similar approaches as well as self-report questionnaires on current and/or retrospective levels of IT 1 can be used later in life., (Copyright © 2022 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.)
Published: 2022
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41. Virtual Ontogeny of Cortical Growth Preceding Mental Illness.

Author: Patel Y, Shin J, Abé C, Agartz I, Alloza C, Alnæs D, Ambrogi S, Antonucci LA, Arango C, Arolt V, Auzias G, Ayesa-Arriola R, Banaj N, Banaschewski T, Bandeira C, Başgöze Z, Cupertino RB, Bau CHD, Bauer J, Baumeister S, Bernardoni F, Bertolino A, Bonnin CDM, Brandeis D, Brem S, Bruggemann J, Bülow R, Bustillo JR, Calderoni S, Calvo R, Canales-Rodríguez EJ, Cannon DM, Carmona S, Carr VJ, Catts SV, Chenji S, Chew QH, Coghill D, Connolly CG, Conzelmann A, Craven AR, Crespo-Facorro B, Cullen K, Dahl A, Dannlowski U, Davey CG, Deruelle C, Díaz-Caneja CM, Dohm K, Ehrlich S, Epstein J, Erwin-Grabner T, Eyler LT, Fedor J, Fitzgerald J, Foran W, Ford JM, Fortea L, Fuentes-Claramonte P, Fullerton J, Furlong L, Gallagher L, Gao B, Gao S, Goikolea JM, Gotlib I, Goya-Maldonado R, Grabe HJ, Green M, Grevet EH, Groenewold NA, Grotegerd D, Gruber O, Haavik J, Hahn T, Harrison BJ, Heindel W, Henskens F, Heslenfeld DJ, Hilland E, Hoekstra PJ, Hohmann S, Holz N, Howells FM, Ipser JC, Jahanshad N, Jakobi B, Jansen A, Janssen J, Jonassen R, Kaiser A, Kaleda V, Karantonis J, King JA, Kircher T, Kochunov P, Koopowitz SM, Landén M, Landrø NI, Lawrie S, Lebedeva I, Luna B, Lundervold AJ, MacMaster FP, Maglanoc LA, Mathalon DH, McDonald C, McIntosh A, Meinert S, Michie PT, Mitchell P, Moreno-Alcázar A, Mowry B, Muratori F, Nabulsi L, Nenadić I, O'Gorman Tuura R, Oosterlaan J, Overs B, Pantelis C, Parellada M, Pariente JC, Pauli P, Pergola G, Piarulli FM, Picon F, Piras F, Pomarol-Clotet E, Pretus C, Quidé Y, Radua J, Ramos-Quiroga JA, Rasser PE, Reif A, Retico A, Roberts G, Rossell S, Rovaris DL, Rubia K, Sacchet M, Salavert J, Salvador R, Sarró S, Sawa A, Schall U, Scott R, Selvaggi P, Silk T, Sim K, Skoch A, Spalletta G, Spaniel F, Stein DJ, Steinsträter O, Stolicyn A, Takayanagi Y, Tamm L, Tavares M, Teumer A, Thiel K, Thomopoulos SI, Tomecek D, Tomyshev AS, Tordesillas-Gutiérrez D, Tosetti M, Uhlmann A, Van Rheenen T, Vazquez-Bourgón J, Vernooij MW, Vieta E, Vilarroya O, Weickert C, Weickert T, Westlye LT, Whalley H, Willinger D, Winter A, Wittfeld K, Yang TT, Yoncheva Y, Zijlmans JL, Hoogman M, Franke B, van Rooij D, Buitelaar J, Ching CRK, Andreassen OA, Pozzi E, Veltman D, Schmaal L, van Erp TGM, Turner J, Castellanos FX, Pausova Z, Thompson P, and Paus T
Subjects: Cerebral Cortex, Child, Female, Humans, Infant, Newborn, Magnetic Resonance Imaging methods, Pregnancy, Attention Deficit Disorder with Hyperactivity, Autism Spectrum Disorder genetics, Autism Spectrum Disorder pathology, Bipolar Disorder, Depressive Disorder, Major pathology, Premature Birth pathology
Abstract: Background: Morphology of the human cerebral cortex differs across psychiatric disorders, with neurobiology and developmental origins mostly undetermined. Deviations in the tangential growth of the cerebral cortex during pre/perinatal periods may be reflected in individual variations in cortical surface area later in life., Methods: Interregional profiles of group differences in surface area between cases and controls were generated using T1-weighted magnetic resonance imaging from 27,359 individuals including those with attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia, and high general psychopathology (through the Child Behavior Checklist). Similarity of interregional profiles of group differences in surface area and prenatal cell-specific gene expression was assessed., Results: Across the 11 cortical regions, group differences in cortical area for attention-deficit/hyperactivity disorder, schizophrenia, and Child Behavior Checklist were dominant in multimodal association cortices. The same interregional profiles were also associated with interregional profiles of (prenatal) gene expression specific to proliferative cells, namely radial glia and intermediate progenitor cells (greater expression, larger difference), as well as differentiated cells, namely excitatory neurons and endothelial and mural cells (greater expression, smaller difference). Finally, these cell types were implicated in known pre/perinatal risk factors for psychosis. Genes coexpressed with radial glia were enriched with genes implicated in congenital abnormalities, birth weight, hypoxia, and starvation. Genes coexpressed with endothelial and mural genes were enriched with genes associated with maternal hypertension and preterm birth., Conclusions: Our findings support a neurodevelopmental model of vulnerability to mental illness whereby prenatal risk factors acting through cell-specific processes lead to deviations from typical brain development during pregnancy., (Copyright © 2022 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)
Published: 2022
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42. The role of educational attainment and brain morphology in major depressive disorder: Findings from the ENIGMA major depressive disorder consortium.

Author: Whittle S, Rakesh D, Schmaal L, Veltman DJ, Thompson PM, Singh A, Gonul AS, Aleman A, Uyar Demir A, Krug A, Mwangi B, Krämer B, Baune BT, Stein DJ, Grotegerd D, Pomarol-Clotet E, Rodríguez-Cano E, Melloni E, Benedetti F, Stein F, Grabe HJ, Völzke H, Gotlib IH, Nenadić I, Soares JC, Repple J, Sim K, Brosch K, Wittfeld K, Berger K, Hermesdorf M, Portella MJ, Sacchet MD, Wu MJ, Opel N, Groenewold NA, Gruber O, Fuentes-Claramonte P, Salvador R, Goya-Maldonado R, Sarró S, Poletti S, Meinert SL, Kircher T, Dannlowski U, and Pozzi E
Subjects: Adult, Brain diagnostic imaging, Educational Status, Frontal Lobe, Humans, Magnetic Resonance Imaging, Depressive Disorder, Major diagnostic imaging
Abstract: Brain structural abnormalities and low educational attainment are consistently associated with major depressive disorder (MDD), yet there has been little research investigating the complex interaction of these factors. Brain structural alterations may represent a vulnerability or differential susceptibility marker, and in the context of low educational attainment, predict MDD. We tested this moderation model in a large multisite sample of 1958 adults with MDD and 2921 controls (aged 18 to 86) from the ENIGMA MDD working group. Using generalized linear mixed models and within-sample split-half replication, we tested whether brain structure interacted with educational attainment to predict MDD status. Analyses revealed that cortical thickness in a number of occipital, parietal, and frontal regions significantly interacted with education to predict MDD. For the majority of regions, models suggested a differential susceptibility effect, whereby thicker cortex was more likely to predict MDD in individuals with low educational attainment, but less likely to predict MDD in individuals with high educational attainment. Findings suggest that greater thickness of brain regions subserving visuomotor and social-cognitive functions confers susceptibility to MDD, dependent on level of educational attainment. Longitudinal work, however, is ultimately needed to establish whether cortical thickness represents a preexisting susceptibility marker. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Published: 2022
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43. Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years.

Author: Frangou S, Modabbernia A, Williams SCR, Papachristou E, Doucet GE, Agartz I, Aghajani M, Akudjedu TN, Albajes-Eizagirre A, Alnaes D, Alpert KI, Andersson M, Andreasen NC, Andreassen OA, Asherson P, Banaschewski T, Bargallo N, Baumeister S, Baur-Streubel R, Bertolino A, Bonvino A, Boomsma DI, Borgwardt S, Bourque J, Brandeis D, Breier A, Brodaty H, Brouwer RM, Buitelaar JK, Busatto GF, Buckner RL, Calhoun V, Canales-Rodríguez EJ, Cannon DM, Caseras X, Castellanos FX, Cervenka S, Chaim-Avancini TM, Ching CRK, Chubar V, Clark VP, Conrod P, Conzelmann A, Crespo-Facorro B, Crivello F, Crone EA, Dale AM, Dannlowski U, Davey C, de Geus EJC, de Haan L, de Zubicaray GI, den Braber A, Dickie EW, Di Giorgio A, Doan NT, Dørum ES, Ehrlich S, Erk S, Espeseth T, Fatouros-Bergman H, Fisher SE, Fouche JP, Franke B, Frodl T, Fuentes-Claramonte P, Glahn DC, Gotlib IH, Grabe HJ, Grimm O, Groenewold NA, Grotegerd D, Gruber O, Gruner P, Gur RE, Gur RC, Hahn T, Harrison BJ, Hartman CA, Hatton SN, Heinz A, Heslenfeld DJ, Hibar DP, Hickie IB, Ho BC, Hoekstra PJ, Hohmann S, Holmes AJ, Hoogman M, Hosten N, Howells FM, Hulshoff Pol HE, Huyser C, Jahanshad N, James A, Jernigan TL, Jiang J, Jönsson EG, Joska JA, Kahn R, Kalnin A, Kanai R, Klein M, Klyushnik TP, Koenders L, Koops S, Krämer B, Kuntsi J, Lagopoulos J, Lázaro L, Lebedeva I, Lee WH, Lesch KP, Lochner C, Machielsen MWJ, Maingault S, Martin NG, Martínez-Zalacaín I, Mataix-Cols D, Mazoyer B, McDonald C, McDonald BC, McIntosh AM, McMahon KL, McPhilemy G, Meinert S, Menchón JM, Medland SE, Meyer-Lindenberg A, Naaijen J, Najt P, Nakao T, Nordvik JE, Nyberg L, Oosterlaan J, de la Foz VO, Paloyelis Y, Pauli P, Pergola G, Pomarol-Clotet E, Portella MJ, Potkin SG, Radua J, Reif A, Rinker DA, Roffman JL, Rosa PGP, Sacchet MD, Sachdev PS, Salvador R, Sánchez-Juan P, Sarró S, Satterthwaite TD, Saykin AJ, Serpa MH, Schmaal L, Schnell K, Schumann G, Sim K, Smoller JW, Sommer I, Soriano-Mas C, Stein DJ, Strike LT, Swagerman SC, Tamnes CK, Temmingh HS, Thomopoulos SI, Tomyshev AS, Tordesillas-Gutiérrez D, Trollor JN, Turner JA, Uhlmann A, van den Heuvel OA, van den Meer D, van der Wee NJA, van Haren NEM, van 't Ent D, van Erp TGM, Veer IM, Veltman DJ, Voineskos A, Völzke H, Walter H, Walton E, Wang L, Wang Y, Wassink TH, Weber B, Wen W, West JD, Westlye LT, Whalley H, Wierenga LM, Wittfeld K, Wolf DH, Worker A, Wright MJ, Yang K, Yoncheva Y, Zanetti MV, Ziegler GC, Thompson PM, and Dima D
Subjects: Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Female, Humans, Male, Middle Aged, Young Adult, Cross-Sectional Studies, Cerebral Cortex anatomy & histology, Cerebral Cortex diagnostic imaging, Human Development physiology, Neuroimaging
Abstract: Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)
Published: 2022
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44. Subcortical shape alterations in major depressive disorder: Findings from the ENIGMA major depressive disorder working group.

Author: Ho TC, Gutman B, Pozzi E, Grabe HJ, Hosten N, Wittfeld K, Völzke H, Baune B, Dannlowski U, Förster K, Grotegerd D, Redlich R, Jansen A, Kircher T, Krug A, Meinert S, Nenadic I, Opel N, Dinga R, Veltman DJ, Schnell K, Veer I, Walter H, Gotlib IH, Sacchet MD, Aleman A, Groenewold NA, Stein DJ, Li M, Walter M, Ching CRK, Jahanshad N, Ragothaman A, Isaev D, Zavaliangos-Petropulu A, Thompson PM, Sämann PG, and Schmaal L
Subjects: Amygdala diagnostic imaging, Corpus Striatum diagnostic imaging, Depressive Disorder, Major diagnostic imaging, Hippocampus diagnostic imaging, Humans, Multicenter Studies as Topic, Thalamus diagnostic imaging, Amygdala pathology, Corpus Striatum pathology, Depressive Disorder, Major pathology, Hippocampus pathology, Neuroimaging, Thalamus pathology
Abstract: Alterations in regional subcortical brain volumes have been investigated as part of the efforts of an international consortium, ENIGMA, to identify reliable neural correlates of major depressive disorder (MDD). Given that subcortical structures are comprised of distinct subfields, we sought to build significantly from prior work by precisely mapping localized MDD-related differences in subcortical regions using shape analysis. In this meta-analysis of subcortical shape from the ENIGMA-MDD working group, we compared 1,781 patients with MDD and 2,953 healthy controls (CTL) on individual measures of shape metrics (thickness and surface area) on the surface of seven bilateral subcortical structures: nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Harmonized data processing and statistical analyses were conducted locally at each site, and findings were aggregated by meta-analysis. Relative to CTL, patients with adolescent-onset MDD (≤ 21 years) had lower thickness and surface area of the subiculum, cornu ammonis (CA) 1 of the hippocampus and basolateral amygdala (Cohen's d = -0.164 to -0.180). Relative to first-episode MDD, recurrent MDD patients had lower thickness and surface area in the CA1 of the hippocampus and the basolateral amygdala (Cohen's d = -0.173 to -0.184). Our results suggest that previously reported MDD-associated volumetric differences may be localized to specific subfields of these structures that have been shown to be sensitive to the effects of stress, with important implications for mapping treatments to patients based on specific neural targets and key clinical features., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.)
Published: 2022
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45. Subcortical brain volumes in young infants exposed to antenatal maternal depression: Findings from a South African birth cohort.

Author: Groenewold NA, Wedderburn CJ, Pellowski JA, Fouché JP, Michalak L, Roos A, Woods RP, Narr KL, Zar HJ, Donald KA, and Stein DJ
Subjects: Infant, Infant, Newborn, Adolescent, Child, Female, Humans, Pregnancy, Brain diagnostic imaging, Caudate Nucleus, Amygdala diagnostic imaging, Magnetic Resonance Imaging methods, Birth Cohort, Depressive Disorder
Abstract: Background: Several studies have reported enlarged amygdala and smaller hippocampus volumes in children and adolescents exposed to maternal depression. It is unclear whether similar volumetric differences are detectable in the infants' first weeks of life, following exposure in utero. We investigated subcortical volumes in 2-to-6 week old infants exposed to antenatal maternal depression (AMD) from a South African birth cohort., Methods: AMD was measured with the Beck Depression Inventory 2nd edition (BDI-II) at 28-32 weeks gestation. T2-weighted structural images were acquired during natural sleep on a 3T Siemens Allegra scanner. Subcortical regions were segmented based on the University of North Carolina neonatal brain atlas. Volumetric estimates were compared between AMD-exposed (BDI-II ⩾ 20) and unexposed (BDI-II < 14) infants, adjusted for age, sex and total intracranial volume using analysis of covariance., Results: Larger volumes were observed in AMD-exposed (N = 49) compared to unexposed infants (N = 75) for the right amygdala (1.93% difference, p = 0.039) and bilateral caudate nucleus (left: 5.79% difference, p = 0.001; right: 6.09% difference, p < 0.001). A significant AMD-by-sex interaction was found for the hippocampus (left: F(1,118) = 4.80, p = 0.030; right: F(1,118) = 5.16, p = 0.025), reflecting greater volume in AMD-exposed females (left: 5.09% difference, p = 0.001, right: 3.54% difference, p = 0.010), but not males., Conclusions: Volumetric differences in subcortical regions can be detected in AMD-exposed infants soon after birth, suggesting structural changes may occur in utero. Female infants might exhibit volumetric changes that are not observed in male infants. The potential mechanisms underlying these early volumetric differences, and their significance for long-term child mental health, require further investigation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)
Published: 2022
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46. Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years.

Author: Dima D, Modabbernia A, Papachristou E, Doucet GE, Agartz I, Aghajani M, Akudjedu TN, Albajes-Eizagirre A, Alnaes D, Alpert KI, Andersson M, Andreasen NC, Andreassen OA, Asherson P, Banaschewski T, Bargallo N, Baumeister S, Baur-Streubel R, Bertolino A, Bonvino A, Boomsma DI, Borgwardt S, Bourque J, Brandeis D, Breier A, Brodaty H, Brouwer RM, Buitelaar JK, Busatto GF, Buckner RL, Calhoun V, Canales-Rodríguez EJ, Cannon DM, Caseras X, Castellanos FX, Cervenka S, Chaim-Avancini TM, Ching CRK, Chubar V, Clark VP, Conrod P, Conzelmann A, Crespo-Facorro B, Crivello F, Crone EA, Dannlowski U, Dale AM, Davey C, de Geus EJC, de Haan L, de Zubicaray GI, den Braber A, Dickie EW, Di Giorgio A, Doan NT, Dørum ES, Ehrlich S, Erk S, Espeseth T, Fatouros-Bergman H, Fisher SE, Fouche JP, Franke B, Frodl T, Fuentes-Claramonte P, Glahn DC, Gotlib IH, Grabe HJ, Grimm O, Groenewold NA, Grotegerd D, Gruber O, Gruner P, Gur RE, Gur RC, Hahn T, Harrison BJ, Hartman CA, Hatton SN, Heinz A, Heslenfeld DJ, Hibar DP, Hickie IB, Ho BC, Hoekstra PJ, Hohmann S, Holmes AJ, Hoogman M, Hosten N, Howells FM, Hulshoff Pol HE, Huyser C, Jahanshad N, James A, Jernigan TL, Jiang J, Jönsson EG, Joska JA, Kahn R, Kalnin A, Kanai R, Klein M, Klyushnik TP, Koenders L, Koops S, Krämer B, Kuntsi J, Lagopoulos J, Lázaro L, Lebedeva I, Lee WH, Lesch KP, Lochner C, Machielsen MWJ, Maingault S, Martin NG, Martínez-Zalacaín I, Mataix-Cols D, Mazoyer B, McDonald C, McDonald BC, McIntosh AM, McMahon KL, McPhilemy G, Meinert S, Menchón JM, Medland SE, Meyer-Lindenberg A, Naaijen J, Najt P, Nakao T, Nordvik JE, Nyberg L, Oosterlaan J, de la Foz VO, Paloyelis Y, Pauli P, Pergola G, Pomarol-Clotet E, Portella MJ, Potkin SG, Radua J, Reif A, Rinker DA, Roffman JL, Rosa PGP, Sacchet MD, Sachdev PS, Salvador R, Sánchez-Juan P, Sarró S, Satterthwaite TD, Saykin AJ, Serpa MH, Schmaal L, Schnell K, Schumann G, Sim K, Smoller JW, Sommer I, Soriano-Mas C, Stein DJ, Strike LT, Swagerman SC, Tamnes CK, Temmingh HS, Thomopoulos SI, Tomyshev AS, Tordesillas-Gutiérrez D, Trollor JN, Turner JA, Uhlmann A, van den Heuvel OA, van den Meer D, van der Wee NJA, van Haren NEM, Van't Ent D, van Erp TGM, Veer IM, Veltman DJ, Voineskos A, Völzke H, Walter H, Walton E, Wang L, Wang Y, Wassink TH, Weber B, Wen W, West JD, Westlye LT, Whalley H, Wierenga LM, Williams SCR, Wittfeld K, Wolf DH, Worker A, Wright MJ, Yang K, Yoncheva Y, Zanetti MV, Ziegler GC, Thompson PM, and Frangou S
Subjects: Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Female, Humans, Male, Middle Aged, Young Adult, Amygdala anatomy & histology, Amygdala diagnostic imaging, Corpus Striatum anatomy & histology, Corpus Striatum diagnostic imaging, Hippocampus anatomy & histology, Hippocampus diagnostic imaging, Human Development physiology, Neuroimaging, Thalamus anatomy & histology, Thalamus diagnostic imaging
Abstract: Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)
Published: 2022
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47. Mega-analysis methods in ENIGMA: The experience of the generalized anxiety disorder working group.

Author: Zugman A, Harrewijn A, Cardinale EM, Zwiebel H, Freitag GF, Werwath KE, Bas-Hoogendam JM, Groenewold NA, Aghajani M, Hilbert K, Cardoner N, Porta-Casteràs D, Gosnell S, Salas R, Blair KS, Blair JR, Hammoud MZ, Milad M, Burkhouse K, Phan KL, Schroeder HK, Strawn JR, Beesdo-Baum K, Thomopoulos SI, Grabe HJ, Van der Auwera S, Wittfeld K, Nielsen JA, Buckner R, Smoller JW, Mwangi B, Soares JC, Wu MJ, Zunta-Soares GB, Jackowski AP, Pan PM, Salum GA, Assaf M, Diefenbach GJ, Brambilla P, Maggioni E, Hofmann D, Straube T, Andreescu C, Berta R, Tamburo E, Price R, Manfro GG, Critchley HD, Makovac E, Mancini M, Meeten F, Ottaviani C, Agosta F, Canu E, Cividini C, Filippi M, Kostić M, Munjiza A, Filippi CA, Leibenluft E, Alberton BAV, Balderston NL, Ernst M, Grillon C, Mujica-Parodi LR, van Nieuwenhuizen H, Fonzo GA, Paulus MP, Stein MB, Gur RE, Gur RC, Kaczkurkin AN, Larsen B, Satterthwaite TD, Harper J, Myers M, Perino MT, Yu Q, Sylvester CM, Veltman DJ, Lueken U, Van der Wee NJA, Stein DJ, Jahanshad N, Thompson PM, Pine DS, and Winkler AM
Subjects: Humans, Anxiety Disorders diagnostic imaging, Cerebral Cortex diagnostic imaging, Data Interpretation, Statistical, Meta-Analysis as Topic, Multicenter Studies as Topic methods, Multicenter Studies as Topic standards, Neuroimaging methods, Neuroimaging standards
Abstract: The ENIGMA group on Generalized Anxiety Disorder (ENIGMA-Anxiety/GAD) is part of a broader effort to investigate anxiety disorders using imaging and genetic data across multiple sites worldwide. The group is actively conducting a mega-analysis of a large number of brain structural scans. In this process, the group was confronted with many methodological challenges related to study planning and implementation, between-country transfer of subject-level data, quality control of a considerable amount of imaging data, and choices related to statistical methods and efficient use of resources. This report summarizes the background information and rationale for the various methodological decisions, as well as the approach taken to implement them. The goal is to document the approach and help guide other research groups working with large brain imaging data sets as they develop their own analytic pipelines for mega-analyses., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)
Published: 2022
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48. ENIGMA-anxiety working group: Rationale for and organization of large-scale neuroimaging studies of anxiety disorders.

Author: Bas-Hoogendam JM, Groenewold NA, Aghajani M, Freitag GF, Harrewijn A, Hilbert K, Jahanshad N, Thomopoulos SI, Thompson PM, Veltman DJ, Winkler AM, Lueken U, Pine DS, van der Wee NJA, and Stein DJ
Subjects: Humans, Multicenter Studies as Topic, Anxiety Disorders diagnostic imaging, Anxiety Disorders genetics, Anxiety Disorders pathology, Anxiety Disorders physiopathology, Limbic System diagnostic imaging, Limbic System pathology, Limbic System physiopathology, Neuroimaging, Prefrontal Cortex diagnostic imaging, Prefrontal Cortex pathology, Prefrontal Cortex physiopathology
Abstract: Anxiety disorders are highly prevalent and disabling but seem particularly tractable to investigation with translational neuroscience methodologies. Neuroimaging has informed our understanding of the neurobiology of anxiety disorders, but research has been limited by small sample sizes and low statistical power, as well as heterogenous imaging methodology. The ENIGMA-Anxiety Working Group has brought together researchers from around the world, in a harmonized and coordinated effort to address these challenges and generate more robust and reproducible findings. This paper elaborates on the concepts and methods informing the work of the working group to date, and describes the initial approach of the four subgroups studying generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobia. At present, the ENIGMA-Anxiety database contains information about more than 100 unique samples, from 16 countries and 59 institutes. Future directions include examining additional imaging modalities, integrating imaging and genetic data, and collaborating with other ENIGMA working groups. The ENIGMA consortium creates synergy at the intersection of global mental health and clinical neuroscience, and the ENIGMA-Anxiety Working Group extends the promise of this approach to neuroimaging research on anxiety disorders., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)
Published: 2022
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49. Early structural brain development in infants exposed to HIV and antiretroviral therapy in utero in a South African birth cohort.

Author: Wedderburn CJ, Groenewold NA, Roos A, Yeung S, Fouche JP, Rehman AM, Gibb DM, Narr KL, Zar HJ, Stein DJ, and Donald KA
Subjects: Birth Cohort, Brain diagnostic imaging, Breast Feeding, Female, Humans, Infant, Infant, Newborn, Male, Pregnancy, Prospective Studies, HIV Infections drug therapy, Pregnancy Complications, Infectious
Abstract: Introduction: There is a growing population of children who are HIV-exposed and uninfected (HEU) with the successful expansion of antiretroviral therapy (ART) use in pregnancy. Children who are HEU are at risk of delayed neurodevelopment; however, there is limited research on early brain growth and maturation. We aimed to investigate the effects of in utero exposure to HIV/ART on brain structure of infants who are HEU compared to HIV-unexposed (HU)., Methods: Magnetic resonance imaging using a T2-weighted sequence was undertaken in a subgroup of infants aged 2-6 weeks enrolled in the Drakenstein Child Health Study birth cohort, South Africa, between 2012 and 2015. Mother-child pairs received antenatal and postnatal HIV testing and ART per local guidelines. We compared subcortical and total grey matter volumes between HEU and HU groups using multivariable linear regression adjusting for infant age, sex, intracranial volume and socio-economic variables. We further assessed associations between brain volumes with maternal CD4 cell count and ART exposure., Results: One hundred forty-six infants (40 HEU; 106 HU) with high-resolution images were included in this analysis (mean age 3 weeks; 50.7% male). All infants who were HEU were exposed to ART (88% maternal triple ART). Infants who were HEU had smaller caudate volumes bilaterally (5.4% reduction, p < 0.05) compared to HU infants. There were no group differences in other subcortical volumes (all p > 0.2). Total grey matter volume was also reduced in infants who were HEU (2.1% reduction, p < 0.05). Exploratory analyses showed that low maternal CD4 cell count (<350 cells/mm 3 ) was associated with decreased infant grey matter volumes. There was no relationship between timing of ART exposure and grey matter volumes., Conclusions: Lower caudate and total grey matter volumes were found in infants who were HEU compared to HU in the first weeks of life, and maternal immunosuppression was associated with reduced volumes. These findings suggest that antenatal HIV exposure may impact early structural brain development and improved antenatal HIV management may have the potential to optimize neurodevelopmental outcomes of children who are HEU., (© 2022 The Authors. Journal of the International AIDS Society published by John Wiley & Sons Ltd on behalf of the International AIDS Society.)
Published: 2022
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50. Decreased reward circuit connectivity during reward anticipation in major depression.

Author: Geugies H, Groenewold NA, Meurs M, Doornbos B, de Klerk-Sluis JM, van Eijndhoven P, Roest AM, and Ruhé HG
Subjects: Humans, Reward, Magnetic Resonance Imaging, Neural Pathways diagnostic imaging, Depressive Disorder, Major drug therapy, Ventral Striatum diagnostic imaging
Abstract: An important symptom of major depressive disorder (MDD) is the inability to experience pleasure, possibly due to a dysfunction of the reward system. Despite promising insights regarding impaired reward-related processing in MDD, circuit-level abnormalities remain largely unexplored. Furthermore, whereas studies contrasting experimental conditions from incentive tasks have revealed important information about reward processing, temporal difference modeling of reward-related prediction error (PE) signals might give a more accurate representation of the reward system. We used a monetary incentive delay task during functional MRI scanning to explore PE-related striatal and ventral tegmental area (VTA) activation in response to anticipation and delivery of monetary rewards in 24 individuals with MDD versus 24 healthy controls (HCs). Furthermore, we investigated group differences in temporal difference related connectivity with a generalized psychophysiological interaction (gPPI) analysis with the VTA, ventral striatum (VS) and dorsal striatum (DS) as seeds during reward versus neutral, both in anticipation and delivery. Relative to HCs, MDD patients displayed a trend-level (p = 0.052) decrease in temporal difference-related activation in the VS during reward anticipation and delivery combined. Moreover, gPPI analyses revealed that during reward anticipation, MDD patients exhibited decreased functional connectivity between the VS and anterior cingulate cortex / medial prefrontal cortex, anterior cingulate gyrus, angular/middle orbital gyrus, left insula, superior/middle frontal gyrus (SFG/MFG) and precuneus/superior occipital gyrus/cerebellum compared to HC. Moreover, MDD patients showed decreased functional connectivity between the VTA and left insula compared to HC during reward anticipation. Exploratory analysis separating medication free patients from patients using antidepressant revealed that these decreased functional connectivity patterns were mainly apparent in the MDD group that used antidepressants. These results suggest that MDD is characterized by alterations in reward circuit connectivity rather than isolated activation impairments. These findings represent an important extension of the existing literature since improved understanding of neural pathways underlying depression-related reward dysfunctions, may help currently unmet diagnostic and therapeutic efforts., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)
Published: 2022
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