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

Author

Frangou, S. (Sophia), Modabbernia, A. (Amirhossein), Williams, S.C.R. (Steven C. R.), Papachristou, E. (Efstathios), Doucet, G.E. (Gaelle E.), Agartz, I. (Ingrid), Aghajani, M. (Moji), Akudjedu, T.N. (Theophilus N.), Albajes-Eizagirre, A. (Anton), Alnæs, D. (Dag), Alpert, K. (Kathryn), Andersson, M. (Micael), Andreasen, N.C. (Nancy C.), Andreassen, O.A. (Ole), Asherson, P. (Philip), Banaschewski, T. (Tobias), Bargallo, N. (Nuria), Baumeister, S. (Sarah), Baur-Streubel, R. (Ramona), Bertolino, A. (Alessandro), Bonvino, A. (Aurora), Boomsma, D.I. (Dorret I.), Borgwardt, S. (Stefan), Bourque, J. (Josiane), Brandeis, D. (Daniel), Breier, A. (Alan), Brodaty, H. (Henry), Brouwer, R.M. (Rachel), Buitelaar, J.K. (Jan K.), Busatto, G.F. (Geraldo F.), Buckner, M., Calhoun, V.D. (Vince), Canales-Rodríguez, E.J. (Erick J.), Cannon, D.M. (Dara M.), Caseras, X. (Xavier), Castellanos, F.X. (Francisco X.), Cervenka, S. (Simon), Chaim-Avancini, T.M. (Tiffany M.), Ching, C.R.K. (Christopher), Chubar, V. (Victoria), Clark, V.P. (Vincent P.), Conrod, P. (Patricia), Conzelmann, A. (Annette), Crespo-Facorro, B. (Benedicto), Crivello, F. (Fabrice), Crone, E.A. (Eveline), Dale, A.M. (Anders), Davey, C.G. (Christopher), Geus, E.J.C. (Eco) de, Haan, L. (Lieuwe) de, Zubicaray, G.I. (Greig) de, Braber, A. (Anouk) den, Dickie, E.W. (Erin W.), Di Giorgio, A. (Annabella), Doan, N.T. (Nhat Trung), Dørum, E.S. (Erlend S.), Ehrlich, S.M. (Stefan), Erk, S., Espeseth, T. (Thomas), Fatouros-Bergman, H. (Helena), Fisher, S.E. (Simon), Fouche, J.-P. (Jean-Paul), Franke, B. (Barbara), Frodl, T. (Thomas), Fuentes-Claramonte, P. (Paola), Glahn, D.C. (David), Gotlib, I.H. (Ian H.), Grabe, H.J. (Hans Jörgen), Grimm, O. (Oliver), Groenewold, N.A. (Nynke A.), Grotegerd, D. (Dominik), Gruber, O. (Oliver), Gruner, P. (Patricia), Gur, R.E. (Rachel E.), Gur, R.C. (Ruben C.), Harrison, B.J. (Ben J.), Hartman, C.A. (Catharine A.), Hatton, W., Heinz, A. (Andreas), Heslenfeld, D.J. (Dirk), Hibar, D.P. (Derrek P.), Hickie, I.B. (Ian), Ho, B.-C. (Beng-Choon), Hoekstra, P.J. (Pieter), Hohmann, S. (Sarah), Holmes, A.J. (Avram J.), Hoogman, M. (Martine), Hosten, N. (Norbert), Howells, F.M. (Fleur M.), Hulshoff Pol, H.E. (Hilleke E.), Huyser, J. (Jochanan), Jahanshad, N. (Neda), James, A., Jernigan, T.L. (Terry L.), Jiang, J. (Jiyang), Jönsson, E.G. (Erik G.), Joska, J.A. (John A.), Kahn, R. (Rene), Kalnin, A. (Andrew), Kanai, R. (Ryota), Klein, M. (Marieke), Klyushnik, T.P. (Tatyana P.), Koenders, L. (Laura), Koops, S. (Sanne), Krämer, B. (Bernd), Kuntsi, J. (Jonna), Lagopoulos, J. (Jim), Lázaro, L. (Luisa), Lebedeva, I. (Irina), Lee, W.H. (Won Hee), Lesch, K.-P. (Klaus-Peter), Lochner, C. (Christine), Machielsen, M.W.J. (Marise), Maingault, S. (Sophie), Martin, N.G. (Nicholas G.), Martínez-Zalacaín, I. (Ignacio), Mataix-Cols, D. (David), Mazoyer, B. (Bernard), McDonald, C. (Colm), McDonald, B.C. (Brenna C.), McIntosh, A.M. (Andrew), McMahon, K.L. (Katie L.), McPhilemy, G. (Genevieve), Menchón, J.M. (José M.), Medland, S.E. (Sarah), Meyer-Lindenberg, A. (Andreas), Naaijen, J. (Jilly), Najt, P. (Pablo), Nakao, T. (Tomohiro), Nordvik, J.E. (Jan E.), Nyberg, L. (Lisa), Oosterlaan, J. (Jaap), de la Foz, V.O.-G. (Víctor Ortiz-García), Paloyelis, Y. (Yannis), Pauli, P. (Paul), Pergola, G. (Giulio), Pomarol-Clotet, E. (Edith), Portella, M.J. (Maria J.), Potkin, S.G. (Steven G.), Radua, J. (Joaquim), Reif, A. (Andreas), Rinker, D.A. (Daniel A.), Roffman, J.L. (Joshua), Rosa, P.G.P. (Pedro G. P.), Sacchet, M.D. (Matthew D.), Sachdev, P.S. (Perminder), Salvador, R. (Raymond), Sánchez-Juan, P. (Pascual), Sarró, S. (Salvador), Satterthwaite, T.D. (Theodore), Saykin, A.J. (Andrew), Serpa, M.H. (Mauricio H.), Schmaal, L. (Lianne), Schnell, K. (Kerry), Schumann, G. (Gunter), Sim, K. (Kang), Smoller, J.W., Sommer, I. (Iris), Soriano-Mas, C. (Carles), Stein, D.J. (Dan J.), Strike, L.T. (Lachlan), Swagerman, S.C. (Suzanne C.), Tamnes, C.K. (Christian K.), Temmingh, H.S. (Henk S.), Thomopoulos, S.I. (Sophia I.), Tomyshev, A.S. (Alexander S.), Tordesillas-Gutierrez, D. (Diana), Trollor, J., Turner, J.A. (Jessica A.), Uhlmann, A. (Anne), Heuvel, O.A. (Odile A.), van den Meer, D. (Dennis), Wee, N.J. (Nic) van der, van Haren, N.E.M. (Neeltje E. M.), Ent, D. (Dennis) van 't, Erp, T.G.M. (Theo G.) van, Veer, I.M. (Ilya), Veltman, D.J. (Dick), Voineskos, A. (Aristotle), Völzke, H. (Henry), Walter, H. (Henrik), Walton, E. (Esther), Wang, L. (Lei), Wang, Y. (Yang), Wassink, A.M.J. (Annemarie), Weber, B. (Bernd), Wen, W. (Wei), West, J.D. (John D.), Westlye, L.T. (Lars), Whalley, H. (Heather), Wierenga, L.M. (Lara M.), Wittfeld, K. (Katharina), Wolf, D.H. (Daniel H.), Worker, A. (Amanda), Wright, M.J. (Margaret J.), Yang, K. (Kun), Yoncheva, Y. (Yulyia), Zanetti, M.V. (Marcus V.), Ziegler, G.C. (Georg C.), Thompson, P.M. (Paul), Dima, D. (Danai), Frangou, S. (Sophia), Modabbernia, A. (Amirhossein), Williams, S.C.R. (Steven C. R.), Papachristou, E. (Efstathios), Doucet, G.E. (Gaelle E.), Agartz, I. (Ingrid), Aghajani, M. (Moji), Akudjedu, T.N. (Theophilus N.), Albajes-Eizagirre, A. (Anton), Alnæs, D. (Dag), Alpert, K. (Kathryn), Andersson, M. (Micael), Andreasen, N.C. (Nancy C.), Andreassen, O.A. (Ole), Asherson, P. (Philip), Banaschewski, T. (Tobias), Bargallo, N. (Nuria), Baumeister, S. (Sarah), Baur-Streubel, R. (Ramona), Bertolino, A. (Alessandro), Bonvino, A. (Aurora), Boomsma, D.I. (Dorret I.), Borgwardt, S. (Stefan), Bourque, J. (Josiane), Brandeis, D. (Daniel), Breier, A. (Alan), Brodaty, H. (Henry), Brouwer, R.M. (Rachel), Buitelaar, J.K. (Jan K.), Busatto, G.F. (Geraldo F.), Buckner, M., Calhoun, V.D. (Vince), Canales-Rodríguez, E.J. (Erick J.), Cannon, D.M. (Dara M.), Caseras, X. (Xavier), Castellanos, F.X. (Francisco X.), Cervenka, S. (Simon), Chaim-Avancini, T.M. (Tiffany M.), Ching, C.R.K. (Christopher), Chubar, V. (Victoria), Clark, V.P. (Vincent P.), Conrod, P. (Patricia), Conzelmann, A. (Annette), Crespo-Facorro, B. (Benedicto), Crivello, F. (Fabrice), Crone, E.A. (Eveline), Dale, A.M. (Anders), Davey, C.G. (Christopher), Geus, E.J.C. (Eco) de, Haan, L. (Lieuwe) de, Zubicaray, G.I. (Greig) de, Braber, A. (Anouk) den, Dickie, E.W. (Erin W.), Di Giorgio, A. (Annabella), Doan, N.T. (Nhat Trung), Dørum, E.S. (Erlend S.), Ehrlich, S.M. (Stefan), Erk, S., Espeseth, T. (Thomas), Fatouros-Bergman, H. (Helena), Fisher, S.E. (Simon), Fouche, J.-P. (Jean-Paul), Franke, B. (Barbara), Frodl, T. (Thomas), Fuentes-Claramonte, P. (Paola), Glahn, D.C. (David), Gotlib, I.H. (Ian H.), Grabe, H.J. (Hans Jörgen), Grimm, O. (Oliver), Groenewold, N.A. (Nynke A.), Grotegerd, D. (Dominik), Gruber, O. (Oliver), Gruner, P. (Patricia), Gur, R.E. (Rachel E.), Gur, R.C. (Ruben C.), Harrison, B.J. (Ben J.), Hartman, C.A. (Catharine A.), Hatton, W., Heinz, A. (Andreas), Heslenfeld, D.J. (Dirk), Hibar, D.P. (Derrek P.), Hickie, I.B. (Ian), Ho, B.-C. (Beng-Choon), Hoekstra, P.J. (Pieter), Hohmann, S. (Sarah), Holmes, A.J. (Avram J.), Hoogman, M. (Martine), Hosten, N. (Norbert), Howells, F.M. (Fleur M.), Hulshoff Pol, H.E. (Hilleke E.), Huyser, J. (Jochanan), Jahanshad, N. (Neda), James, A., Jernigan, T.L. (Terry L.), Jiang, J. (Jiyang), Jönsson, E.G. (Erik G.), Joska, J.A. (John A.), Kahn, R. (Rene), Kalnin, A. (Andrew), Kanai, R. (Ryota), Klein, M. (Marieke), Klyushnik, T.P. (Tatyana P.), Koenders, L. (Laura), Koops, S. (Sanne), Krämer, B. (Bernd), Kuntsi, J. (Jonna), Lagopoulos, J. (Jim), Lázaro, L. (Luisa), Lebedeva, I. (Irina), Lee, W.H. (Won Hee), Lesch, K.-P. (Klaus-Peter), Lochner, C. (Christine), Machielsen, M.W.J. (Marise), Maingault, S. (Sophie), Martin, N.G. (Nicholas G.), Martínez-Zalacaín, I. (Ignacio), Mataix-Cols, D. (David), Mazoyer, B. (Bernard), McDonald, C. (Colm), McDonald, B.C. (Brenna C.), McIntosh, A.M. (Andrew), McMahon, K.L. (Katie L.), McPhilemy, G. (Genevieve), Menchón, J.M. (José M.), Medland, S.E. (Sarah), Meyer-Lindenberg, A. (Andreas), Naaijen, J. (Jilly), Najt, P. (Pablo), Nakao, T. (Tomohiro), Nordvik, J.E. (Jan E.), Nyberg, L. (Lisa), Oosterlaan, J. (Jaap), de la Foz, V.O.-G. (Víctor Ortiz-García), Paloyelis, Y. (Yannis), Pauli, P. (Paul), Pergola, G. (Giulio), Pomarol-Clotet, E. (Edith), Portella, M.J. (Maria J.), Potkin, S.G. (Steven G.), Radua, J. (Joaquim), Reif, A. (Andreas), Rinker, D.A. (Daniel A.), Roffman, J.L. (Joshua), Rosa, P.G.P. (Pedro G. P.), Sacchet, M.D. (Matthew D.), Sachdev, P.S. (Perminder), Salvador, R. (Raymond), Sánchez-Juan, P. (Pascual), Sarró, S. (Salvador), Satterthwaite, T.D. (Theodore), Saykin, A.J. (Andrew), Serpa, M.H. (Mauricio H.), Schmaal, L. (Lianne), Schnell, K. (Kerry), Schumann, G. (Gunter), Sim, K. (Kang), Smoller, J.W., Sommer, I. (Iris), Soriano-Mas, C. (Carles), Stein, D.J. (Dan J.), Strike, L.T. (Lachlan), Swagerman, S.C. (Suzanne C.), Tamnes, C.K. (Christian K.), Temmingh, H.S. (Henk S.), Thomopoulos, S.I. (Sophia I.), Tomyshev, A.S. (Alexander S.), Tordesillas-Gutierrez, D. (Diana), Trollor, J., Turner, J.A. (Jessica A.), Uhlmann, A. (Anne), Heuvel, O.A. (Odile A.), van den Meer, D. (Dennis), Wee, N.J. (Nic) van der, van Haren, N.E.M. (Neeltje E. M.), Ent, D. (Dennis) van 't, Erp, T.G.M. (Theo G.) van, Veer, I.M. (Ilya), Veltman, D.J. (Dick), Voineskos, A. (Aristotle), Völzke, H. (Henry), Walter, H. (Henrik), Walton, E. (Esther), Wang, L. (Lei), Wang, Y. (Yang), Wassink, A.M.J. (Annemarie), Weber, B. (Bernd), Wen, W. (Wei), West, J.D. (John D.), Westlye, L.T. (Lars), Whalley, H. (Heather), Wierenga, L.M. (Lara M.), Wittfeld, K. (Katharina), Wolf, D.H. (Daniel H.), Worker, A. (Amanda), Wright, M.J. (Margaret J.), Yang, K. (Kun), Yoncheva, Y. (Yulyia), Zanetti, M.V. (Marcus V.), Ziegler, G.C. (Georg C.), Thompson, P.M. (Paul), and Dima, D. (Danai)

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
2021
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2. Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3–90 years

Author

Dima, D. (Danai), Modabbernia, A. (Amirhossein), Papachristou, E. (Efstathios), Doucet, G.E. (Gaelle E.), Agartz, I. (Ingrid), Aghajani, M. (Moji), Akudjedu, T.N. (Theophilus N.), Albajes-Eizagirre, A. (Anton), Alnæs, D. (Dag), Alpert, K. (Kathryn), Andersson, M. (Micael), Andreasen, N.C. (Nancy C.), Andreassen, O.A. (Ole), Asherson, P. (Philip), Banaschewski, T. (Tobias), Bargallo, N. (Nuria), Baumeister, S. (Sarah), Baur-Streubel, R. (Ramona), Bertolino, A. (Alessandro), Bonvino, A. (Aurora), Boomsma, D.I. (Dorret I.), Borgwardt, S. (Stefan), Bourque, J. (Josiane), Brandeis, D. (Daniel), Breier, A. (Alan), Brodaty, H. (Henry), Brouwer, R.M. (Rachel), Buitelaar, J.K. (Jan K.), Busatto, G.F. (Geraldo F.), Buckner, M., Calhoun, V.D. (Vince), Canales-Rodríguez, E.J. (Erick J.), Cannon, D.M. (Dara M.), Caseras, X. (Xavier), Castellanos, F.X. (Francisco X.), Cervenka, S. (Simon), Chaim-Avancini, T.M. (Tiffany M.), Ching, C.R.K. (Christopher), Chubar, V. (Victoria), Clark, V.P. (Vincent P.), Conrod, P. (Patricia), Conzelmann, A. (Annette), Crespo-Facorro, B. (Benedicto), Crivello, F. (Fabrice), Crone, E.A. (Eveline), Dale, A.M. (Anders), Davey, C.G. (Christopher), Geus, E.J.C. (Eco) de, Haan, L. (Lieuwe) de, Zubicaray, G.I. (Greig) de, Braber, A. (Anouk) den, Dickie, E.W. (Erin W.), Di Giorgio, A. (Annabella), Doan, N.T. (Nhat Trung), Dørum, E.S. (Erlend S.), Ehrlich, S.M. (Stefan), Erk, S., Espeseth, T. (Thomas), Fatouros-Bergman, H. (Helena), Fisher, S.E. (Simon), Fouche, J.-P. (Jean-Paul), Franke, B. (Barbara), Frodl, T. (Thomas), Fuentes-Claramonte, P. (Paola), Glahn, D.C. (David), Gotlib, I.H. (Ian H.), Grabe, H.J. (Hans Jörgen), Grimm, O. (Oliver), Groenewold, N.A. (Nynke A.), Grotegerd, D. (Dominik), Gruber, O. (Oliver), Gruner, P. (Patricia), Gur, R.E. (Rachel E.), Gur, R.C. (Ruben C.), Harrison, B.J. (Ben J.), Hartman, C.A. (Catharine A.), Hatton, W., Heinz, A. (Andreas), Heslenfeld, D.J. (Dirk), Hibar, D.P. (Derrek P.), Hickie, I.B. (Ian), Ho, B.-C. (Beng-Choon), Hoekstra, P.J. (Pieter), Hohmann, S. (Sarah), Holmes, A.J. (Avram J.), Hoogman, M. (Martine), Hosten, N. (Norbert), Howells, F.M. (Fleur M.), Hulshoff Pol, H.E. (Hilleke E.), Huyser, J. (Jochanan), Jahanshad, N. (Neda), James, A., Jernigan, T.L. (Terry L.), Jiang, J. (Jiyang), Jönsson, E.G. (Erik G.), Joska, J.A. (John A.), Kahn, R. (Rene), Kalnin, A. (Andrew), Kanai, R. (Ryota), Klein, M. (Marieke), Klyushnik, T.P. (Tatyana P.), Koenders, L. (Laura), Koops, S. (Sanne), Krämer, B. (Bernd), Kuntsi, J. (Jonna), Lagopoulos, J. (Jim), Lázaro, L. (Luisa), Lebedeva, I. (Irina), Lee, W.H. (Won Hee), Lesch, K.-P. (Klaus-Peter), Lochner, C. (Christine), Machielsen, M.W.J. (Marise), Maingault, S. (Sophie), Martin, N.G. (Nicholas G.), Martínez-Zalacaín, I. (Ignacio), Mataix-Cols, D. (David), Mazoyer, B. (Bernard), McDonald, C. (Colm), McDonald, B.C. (Brenna C.), McIntosh, A.M. (Andrew), McMahon, K.L. (Katie L.), McPhilemy, G. (Genevieve), Menchón, J.M. (José M.), Medland, S.E. (Sarah), Meyer-Lindenberg, A. (Andreas), Naaijen, J. (Jilly), Najt, P. (Pablo), Nakao, T. (Tomohiro), Nordvik, J.E. (Jan E.), Nyberg, L., Oosterlaan, J. (Jaap), de la Foz, V.O.-G. (Víctor Ortiz-García), Paloyelis, Y. (Yannis), Pauli, P. (Paul), Pergola, G. (Giulio), Pomarol-Clotet, E. (Edith), Portella, M.J. (Maria J.), Potkin, S.G. (Steven G.), Radua, J. (Joaquim), Reif, A. (Andreas), Rinker, D.A. (Daniel A.), Roffman, J.L. (Joshua), Rosa, P.G.P. (Pedro G. P.), Sacchet, M.D. (Matthew D.), Sachdev, P.S. (Perminder), Salvador, R. (Raymond), Sánchez-Juan, P. (Pascual), Sarró, S. (Salvador), Satterthwaite, T.D. (Theodore), Saykin, A.J. (Andrew), Serpa, M.H. (Mauricio H.), Schmaal, L. (Lianne), Schnell, K. (Kerry), Schumann, G. (Gunter), Sim, K. (Kang), Smoller, J.W., Sommer, I. (Iris), Soriano-Mas, C. (Carles), Stein, D.J. (Dan J.), Strike, L.T. (Lachlan), Swagerman, S.C. (Suzanne C.), Tamnes, C.K. (Christian K.), Temmingh, H.S. (Henk S.), Thomopoulos, S.I. (Sophia I.), Tomyshev, A.S. (Alexander S.), Tordesillas-Gutierrez, D. (Diana), Trollor, J., Turner, J.A. (Jessica A.), Uhlmann, A. (Anne), Heuvel, O.A. (Odile A.), van den Meer, D. (Dennis), Wee, N.J. (Nic) van der, van Haren, N.E.M. (Neeltje E. M.), van't Ent, D. (Dennis), Erp, T.G.M. (Theo G.) van, Veer, I.M. (Ilya), Veltman, D.J. (Dick), Voineskos, A. (Aristotle), Völzke, H. (Henry), Walter, H. (Henrik), Walton, E. (Esther), Wang, L. (Lei), Wang, Y. (Yang), Wassink, A.M.J. (Annemarie), Weber, B. (Bernd), Wen, W. (Wei), West, J.D. (John D.), Westlye, L.T. (Lars), Whalley, H. (Heather), Wierenga, L.M. (Lara M.), Williams, S.C.R. (Steven C. R.), Wittfeld, K. (Katharina), Wolf, D.H. (Daniel H.), Worker, A. (Amanda), Wright, M.J. (Margaret J.), Yang, K. (Kun), Yoncheva, Y. (Yulyia), Zanetti, M.V. (Marcus V.), Ziegler, G.C. (Georg C.), Thompson, P.M. (Paul), Frangou, S. (Sophia), Dima, D. (Danai), Modabbernia, A. (Amirhossein), Papachristou, E. (Efstathios), Doucet, G.E. (Gaelle E.), Agartz, I. (Ingrid), Aghajani, M. (Moji), Akudjedu, T.N. (Theophilus N.), Albajes-Eizagirre, A. (Anton), Alnæs, D. (Dag), Alpert, K. (Kathryn), Andersson, M. (Micael), Andreasen, N.C. (Nancy C.), Andreassen, O.A. (Ole), Asherson, P. (Philip), Banaschewski, T. (Tobias), Bargallo, N. (Nuria), Baumeister, S. (Sarah), Baur-Streubel, R. (Ramona), Bertolino, A. (Alessandro), Bonvino, A. (Aurora), Boomsma, D.I. (Dorret I.), Borgwardt, S. (Stefan), Bourque, J. (Josiane), Brandeis, D. (Daniel), Breier, A. (Alan), Brodaty, H. (Henry), Brouwer, R.M. (Rachel), Buitelaar, J.K. (Jan K.), Busatto, G.F. (Geraldo F.), Buckner, M., Calhoun, V.D. (Vince), Canales-Rodríguez, E.J. (Erick J.), Cannon, D.M. (Dara M.), Caseras, X. (Xavier), Castellanos, F.X. (Francisco X.), Cervenka, S. (Simon), Chaim-Avancini, T.M. (Tiffany M.), Ching, C.R.K. (Christopher), Chubar, V. (Victoria), Clark, V.P. (Vincent P.), Conrod, P. (Patricia), Conzelmann, A. (Annette), Crespo-Facorro, B. (Benedicto), Crivello, F. (Fabrice), Crone, E.A. (Eveline), Dale, A.M. (Anders), Davey, C.G. (Christopher), Geus, E.J.C. (Eco) de, Haan, L. (Lieuwe) de, Zubicaray, G.I. (Greig) de, Braber, A. (Anouk) den, Dickie, E.W. (Erin W.), Di Giorgio, A. (Annabella), Doan, N.T. (Nhat Trung), Dørum, E.S. (Erlend S.), Ehrlich, S.M. (Stefan), Erk, S., Espeseth, T. (Thomas), Fatouros-Bergman, H. (Helena), Fisher, S.E. (Simon), Fouche, J.-P. (Jean-Paul), Franke, B. (Barbara), Frodl, T. (Thomas), Fuentes-Claramonte, P. (Paola), Glahn, D.C. (David), Gotlib, I.H. (Ian H.), Grabe, H.J. (Hans Jörgen), Grimm, O. (Oliver), Groenewold, N.A. (Nynke A.), Grotegerd, D. (Dominik), Gruber, O. (Oliver), Gruner, P. (Patricia), Gur, R.E. (Rachel E.), Gur, R.C. (Ruben C.), Harrison, B.J. (Ben J.), Hartman, C.A. (Catharine A.), Hatton, W., Heinz, A. (Andreas), Heslenfeld, D.J. (Dirk), Hibar, D.P. (Derrek P.), Hickie, I.B. (Ian), Ho, B.-C. (Beng-Choon), Hoekstra, P.J. (Pieter), Hohmann, S. (Sarah), Holmes, A.J. (Avram J.), Hoogman, M. (Martine), Hosten, N. (Norbert), Howells, F.M. (Fleur M.), Hulshoff Pol, H.E. (Hilleke E.), Huyser, J. (Jochanan), Jahanshad, N. (Neda), James, A., Jernigan, T.L. (Terry L.), Jiang, J. (Jiyang), Jönsson, E.G. (Erik G.), Joska, J.A. (John A.), Kahn, R. (Rene), Kalnin, A. (Andrew), Kanai, R. (Ryota), Klein, M. (Marieke), Klyushnik, T.P. (Tatyana P.), Koenders, L. (Laura), Koops, S. (Sanne), Krämer, B. (Bernd), Kuntsi, J. (Jonna), Lagopoulos, J. (Jim), Lázaro, L. (Luisa), Lebedeva, I. (Irina), Lee, W.H. (Won Hee), Lesch, K.-P. (Klaus-Peter), Lochner, C. (Christine), Machielsen, M.W.J. (Marise), Maingault, S. (Sophie), Martin, N.G. (Nicholas G.), Martínez-Zalacaín, I. (Ignacio), Mataix-Cols, D. (David), Mazoyer, B. (Bernard), McDonald, C. (Colm), McDonald, B.C. (Brenna C.), McIntosh, A.M. (Andrew), McMahon, K.L. (Katie L.), McPhilemy, G. (Genevieve), Menchón, J.M. (José M.), Medland, S.E. (Sarah), Meyer-Lindenberg, A. (Andreas), Naaijen, J. (Jilly), Najt, P. (Pablo), Nakao, T. (Tomohiro), Nordvik, J.E. (Jan E.), Nyberg, L., Oosterlaan, J. (Jaap), de la Foz, V.O.-G. (Víctor Ortiz-García), Paloyelis, Y. (Yannis), Pauli, P. (Paul), Pergola, G. (Giulio), Pomarol-Clotet, E. (Edith), Portella, M.J. (Maria J.), Potkin, S.G. (Steven G.), Radua, J. (Joaquim), Reif, A. (Andreas), Rinker, D.A. (Daniel A.), Roffman, J.L. (Joshua), Rosa, P.G.P. (Pedro G. P.), Sacchet, M.D. (Matthew D.), Sachdev, P.S. (Perminder), Salvador, R. (Raymond), Sánchez-Juan, P. (Pascual), Sarró, S. (Salvador), Satterthwaite, T.D. (Theodore), Saykin, A.J. (Andrew), Serpa, M.H. (Mauricio H.), Schmaal, L. (Lianne), Schnell, K. (Kerry), Schumann, G. (Gunter), Sim, K. (Kang), Smoller, J.W., Sommer, I. (Iris), Soriano-Mas, C. (Carles), Stein, D.J. (Dan J.), Strike, L.T. (Lachlan), Swagerman, S.C. (Suzanne C.), Tamnes, C.K. (Christian K.), Temmingh, H.S. (Henk S.), Thomopoulos, S.I. (Sophia I.), Tomyshev, A.S. (Alexander S.), Tordesillas-Gutierrez, D. (Diana), Trollor, J., Turner, J.A. (Jessica A.), Uhlmann, A. (Anne), Heuvel, O.A. (Odile A.), van den Meer, D. (Dennis), Wee, N.J. (Nic) van der, van Haren, N.E.M. (Neeltje E. M.), van't Ent, D. (Dennis), Erp, T.G.M. (Theo G.) van, Veer, I.M. (Ilya), Veltman, D.J. (Dick), Voineskos, A. (Aristotle), Völzke, H. (Henry), Walter, H. (Henrik), Walton, E. (Esther), Wang, L. (Lei), Wang, Y. (Yang), Wassink, A.M.J. (Annemarie), Weber, B. (Bernd), Wen, W. (Wei), West, J.D. (John D.), Westlye, L.T. (Lars), Whalley, H. (Heather), Wierenga, L.M. (Lara M.), Williams, S.C.R. (Steven C. R.), Wittfeld, K. (Katharina), Wolf, D.H. (Daniel H.), Worker, A. (Amanda), Wright, M.J. (Margaret J.), Yang, K. (Kun), Yoncheva, Y. (Yulyia), Zanetti, M.V. (Marcus V.), Ziegler, G.C. (Georg C.), Thompson, P.M. (Paul), and Frangou, S. (Sophia)

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
2020
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3. What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group

Author

Ching, C.R.K. (Christopher), Hibar, D.P. (Derrek P.), Gurholt, T.P. (Tiril P.), Nunes, A. (Abraham), Thomopoulos, S.I. (Sophia I.), Abé, C. (Christoph), Agartz, I. (Ingrid), Brouwer, R.M. (Rachel), Cannon, D.M. (Dara), de Zwarte, S.M.C. (Sonja M. C.), Eyler, L.T. (Lisa T.), Favre, P. (Pauline), Hajek, T. (Tomas), Haukvik, U.K. (Unn), Houenou, J. (Josselin), Landén, M. (Mikael), Lett, T.A. (Tristram A.), McDonald, C. (Colm), Nabulsi, L. (Leila), Patel, Y. (Yash), Pauling, M.E. (Melissa E.), Paus, T. (Tomas), Radua, J. (Joaquim), Soeiro-de-Souza, M.G. (Marcio G.), Tronchin, G. (Giulia), van Haren, N.E.M. (Neeltje E. M.), Vieta, E. (Eduard), Walter, H.J. (Henrik), Zeng, L.-L. (Ling-Li), Alda, M. (Martin), Almeida, J. (Jorge), Alnæs, D. (Dag), Alonso-Lana, S. (Silvia), Altimus, C. (Cara), Bauer, M. (Michael), Baune, B.T., Bearden, C.E. (Carrie), Bellani, M. (Marcella), Benedetti, F. (Francesco), Berk, M. (Michael), Bilderbeck, A.C. (Amy C.), Blumberg, H.P. (Hilary P.), Bøen, E. (Erlend), Bollettini, I. (Irene), del Mar Bonnin, C. (Caterina), Brambilla, P. (Paolo), Canales-Rodríguez, E.J. (Erick J.), Caseras, X. (Xavier), Dandash, O. (Orwa), Dannlowski, U. (Udo), Delvecchio, G. (Giuseppe), Díaz-Zuluaga, A.M. (Ana M.), Dima, D. (Danai), Duchesnay, É. (Édouard), Elvsåshagen, T. (Torbjørn), Fears, S. (Scott), Frangou, S. (Sophia), Fullerton, J.M. (Janice M.), Glahn, D.C. (David), Goikolea, J.M. (Jose M.), Green, M.J. (Melissa J.), Grotegerd, D. (Dominik), Gruber, O. (Oliver), Haarman, B.C.M. (Benno), Henry, C. (C.), Howells, F.M. (Fleur M.), Ives-Deliperi, V. (Victoria), Jansen, A. (Andreas), Kircher, T.T.J. (Tilo T. J.), Knöchel, C. (Christian), Kramer, B. (Bernd), Lafer, B. (Beny), López-Jaramillo, C. (Carlos), Machado-Vieira, R. (Rodrigo), MacIntosh, B.J. (Bradley J), Melloni, E.M.T. (Elisa M. T.), Mitchell, P.B. (Philip B.), Nenadic, I. (Igor), Nery, F. (Fabiano), Nugent, A.C. (Allison), Oertel, V. (Viola), Ophoff, R.A. (Roel), Ota, M. (Miho), Overs, B.J. (Bronwyn J.), Pham, D.L. (Daniel L.), Phillips, M.L. (Mary L.), Pineda-Zapata, J.A. (Julian A.), Poletti, S. (Sara), Polosan, M. (Mircea), Pomarol-Clotet, E. (Edith), Pouchon, A. (Arnaud), Quidé, Y. (Yann), Rive, M.M. (Maria M.), Roberts, G. (Gloria), Ruhé, H.G. (Henricus G.Eric), Salvador, R. (Raymond), Sarró, S. (Salvador), Satterthwaite, T.D. (Theodore), Schene, A.H. (Aart), Sim, K. (Kang), Soares, J.C. (Jair C.), Stäblein, M. (Michael), Stein, D.J. (Dan J.), Tamnes, C.K. (Christian K.), Thomaidis, G.V. (Georgios V.), Upegui, C.V. (Cristian Vargas), Veltman, D.J. (Dick), Wessa, M. (Michèle), Westlye, L.T. (Lars), Whalley, H.C. (Heather C.), Wolf, D.H. (Daniel H.), Wu, M.-J. (Mon-Ju), Yatham, L.N. (Lakshmi N.), Zarate, C.A. (Carlos A.), Thompson, P.M. (Paul), Andreassen, O.A. (Ole), Ching, C.R.K. (Christopher), Hibar, D.P. (Derrek P.), Gurholt, T.P. (Tiril P.), Nunes, A. (Abraham), Thomopoulos, S.I. (Sophia I.), Abé, C. (Christoph), Agartz, I. (Ingrid), Brouwer, R.M. (Rachel), Cannon, D.M. (Dara), de Zwarte, S.M.C. (Sonja M. C.), Eyler, L.T. (Lisa T.), Favre, P. (Pauline), Hajek, T. (Tomas), Haukvik, U.K. (Unn), Houenou, J. (Josselin), Landén, M. (Mikael), Lett, T.A. (Tristram A.), McDonald, C. (Colm), Nabulsi, L. (Leila), Patel, Y. (Yash), Pauling, M.E. (Melissa E.), Paus, T. (Tomas), Radua, J. (Joaquim), Soeiro-de-Souza, M.G. (Marcio G.), Tronchin, G. (Giulia), van Haren, N.E.M. (Neeltje E. M.), Vieta, E. (Eduard), Walter, H.J. (Henrik), Zeng, L.-L. (Ling-Li), Alda, M. (Martin), Almeida, J. (Jorge), Alnæs, D. (Dag), Alonso-Lana, S. (Silvia), Altimus, C. (Cara), Bauer, M. (Michael), Baune, B.T., Bearden, C.E. (Carrie), Bellani, M. (Marcella), Benedetti, F. (Francesco), Berk, M. (Michael), Bilderbeck, A.C. (Amy C.), Blumberg, H.P. (Hilary P.), Bøen, E. (Erlend), Bollettini, I. (Irene), del Mar Bonnin, C. (Caterina), Brambilla, P. (Paolo), Canales-Rodríguez, E.J. (Erick J.), Caseras, X. (Xavier), Dandash, O. (Orwa), Dannlowski, U. (Udo), Delvecchio, G. (Giuseppe), Díaz-Zuluaga, A.M. (Ana M.), Dima, D. (Danai), Duchesnay, É. (Édouard), Elvsåshagen, T. (Torbjørn), Fears, S. (Scott), Frangou, S. (Sophia), Fullerton, J.M. (Janice M.), Glahn, D.C. (David), Goikolea, J.M. (Jose M.), Green, M.J. (Melissa J.), Grotegerd, D. (Dominik), Gruber, O. (Oliver), Haarman, B.C.M. (Benno), Henry, C. (C.), Howells, F.M. (Fleur M.), Ives-Deliperi, V. (Victoria), Jansen, A. (Andreas), Kircher, T.T.J. (Tilo T. J.), Knöchel, C. (Christian), Kramer, B. (Bernd), Lafer, B. (Beny), López-Jaramillo, C. (Carlos), Machado-Vieira, R. (Rodrigo), MacIntosh, B.J. (Bradley J), Melloni, E.M.T. (Elisa M. T.), Mitchell, P.B. (Philip B.), Nenadic, I. (Igor), Nery, F. (Fabiano), Nugent, A.C. (Allison), Oertel, V. (Viola), Ophoff, R.A. (Roel), Ota, M. (Miho), Overs, B.J. (Bronwyn J.), Pham, D.L. (Daniel L.), Phillips, M.L. (Mary L.), Pineda-Zapata, J.A. (Julian A.), Poletti, S. (Sara), Polosan, M. (Mircea), Pomarol-Clotet, E. (Edith), Pouchon, A. (Arnaud), Quidé, Y. (Yann), Rive, M.M. (Maria M.), Roberts, G. (Gloria), Ruhé, H.G. (Henricus G.Eric), Salvador, R. (Raymond), Sarró, S. (Salvador), Satterthwaite, T.D. (Theodore), Schene, A.H. (Aart), Sim, K. (Kang), Soares, J.C. (Jair C.), Stäblein, M. (Michael), Stein, D.J. (Dan J.), Tamnes, C.K. (Christian K.), Thomaidis, G.V. (Georgios V.), Upegui, C.V. (Cristian Vargas), Veltman, D.J. (Dick), Wessa, M. (Michèle), Westlye, L.T. (Lars), Whalley, H.C. (Heather C.), Wolf, D.H. (Daniel H.), Wu, M.-J. (Mon-Ju), Yatham, L.N. (Lakshmi N.), Zarate, C.A. (Carlos A.), Thompson, P.M. (Paul), and Andreassen, O.A. (Ole)

Abstract

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studi

Published
2020
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4. What we learn about bipolar disorder from large-scale neuroimaging

Author

Christian K. Tamnes, Bartholomeus C M Haarman, Jair C. Soares, Ole A. Andreassen, Viola Oertel, Theodore D. Satterthwaite, G. Tronchin, Michael Stäblein, Bradley J. MacIntosh, Melissa Pauling, Christopher R.K. Ching, Daniel H. Wolf, Dick J. Veltman, Ingrid Agartz, Bernhard T. Baune, Salvador Sarró, Mon-Ju Wu, Scott C Fears, Eduard Vieta, Melissa J. Green, Neeltje E.M. van Haren, Yann Quidé, Erlend Bøen, Yash Patel, Igor Nenadic, Martin Alda, Lisa T. Eyler, Arnaud Pouchon, Danai Dima, Tomáš Paus, Irene Bollettini, Torbjørn Elvsåshagen, Rachel M. Brouwer, Lakshmi N. Yatham, Michael Bauer, Caterina del Mar Bonnín, C. McDonald, Udo Dannlowski, Bronwyn Overs, Edith Pomarol-Clotet, Cristian Vargas Upegui, Oliver Gruber, Henricus G. Ruhé, Márcio Gerhardt Soeiro-de-Souza, Edouard Duchesnay, Hilary P. Blumberg, Tilo Kircher, Miho Ota, Michael Berk, Christoph Abé, Andreas Jansen, Kang Sim, Heather C. Whalley, Derrek P. Hibar, Roel A. Ophoff, Georgios V Thomaidis, Henrik Walter, Sophia Frangou, Michèle Wessa, Dara M. Cannon, Cara M. Altimus, Allison C. Nugent, Rodrigo Machado-Vieira, Orwa Dandash, Marcella Bellani, Unn K. Haukvik, Philip B. Mitchell, Ling-Li Zeng, Christian Knöchel, Jose Manuel Goikolea, Sonja M C de Zwarte, Francesco Benedetti, Sara Poletti, Janice M. Fullerton, Carlos A. Zarate, Aart H. Schene, Dan J. Stein, Chantal Henry, Tristram A. Lett, Mikael Landén, Daniel L Pham, Paolo Brambilla, Silvia Alonso-Lana, Sophia I. Thomopoulos, Carlos López-Jaramillo, Tomas Hajek, Bernd Kramer, G. Delvecchio, Maria M. Rive, Lars T. Westlye, Erick J. Canales-Rodríguez, Victoria L. Ives-Deliperi, Dominik Grotegerd, Beny Lafer, Abraham Nunes, Carrie E. Bearden, Raymond Salvador, Joaquim Radua, Amy C Bilderbeck, Xavier Caseras, Paul M. Thompson, Jorge R. C. Almeida, Pauline Favre, Gloria Roberts, David C. Glahn, Dag Alnæs, Julian A Pineda-Zapata, Tiril P. Gurholt, Mircea Polosan, Josselin Houenou, Fabiano G. Nery, Leila Nabulsi, Mary L. Phillips, Fleur M. Howells, Ana M. Díaz-Zuluaga, Elisa M T Melloni, Ching, C. R. K., Hibar, D. P., Gurholt, T. P., Nunes, A., Thomopoulos, S. I., Abe, C., Agartz, I., Brouwer, R. M., Cannon, D. M., de Zwarte, S. M. C., Eyler, L. T., Favre, P., Hajek, T., Haukvik, U. K., Houenou, J., Landen, M., Lett, T. A., Mcdonald, C., Nabulsi, L., Patel, Y., Pauling, M. E., Paus, T., Radua, J., Soeiro-de-Souza, M. G., Tronchin, G., van Haren, N. E. M., Vieta, E., Walter, H., Zeng, L. -L., Alda, M., Almeida, J., Alnaes, D., Alonso-Lana, S., Altimus, C., Bauer, M., Baune, B. T., Bearden, C. E., Bellani, M., Benedetti, F., Berk, M., Bilderbeck, A. C., Blumberg, H. P., Boen, E., Bollettini, I., del Mar Bonnin, C., Brambilla, P., Canales-Rodriguez, E. J., Caseras, X., Dandash, O., Dannlowski, U., Delvecchio, G., Diaz-Zuluaga, A. M., Dima, D., Duchesnay, E., Elvsashagen, T., Fears, S. C., Frangou, S., Fullerton, J. M., Glahn, D. C., Goikolea, J. M., Green, M. J., Grotegerd, D., Gruber, O., Haarman, B. C. M., Henry, C., Howells, F. M., Ives-Deliperi, V., Jansen, A., Kircher, T. T. J., Knochel, C., Kramer, B., Lafer, B., Lopez-Jaramillo, C., Machado-Vieira, R., Macintosh, B. J., Melloni, E. M. T., Mitchell, P. B., Nenadic, I., Nery, F., Nugent, A. C., Oertel, V., Ophoff, R. A., Ota, M., Overs, B. J., Pham, D. L., Phillips, M. L., Pineda-Zapata, J. A., Poletti, S., Polosan, M., Pomarol-Clotet, E., Pouchon, A., Quide, Y., Rive, M. M., Roberts, G., Ruhe, H. G., Salvador, R., Sarro, S., Satterthwaite, T. D., Schene, A. H., Sim, K., Soares, J. C., Stablein, M., Stein, D. J., Tamnes, C. K., Thomaidis, G. V., Upegui, C. V., Veltman, D. J., Wessa, M., Westlye, L. T., Whalley, H. C., Wolf, D. H., Wu, M. -J., Yatham, L. N., Zarate, C. A., Thompson, P. M., and Andreassen, O. A.

Subjects
mega-analysis, Stress-related disorders Donders Center for Medical Neuroscience [Radboudumc 13], cortical surface area, Review Article, 0302 clinical medicine, Manic-depressive illness, Multicenter Studies as Topic, Spectrum disorder, Review Articles, bipolar disorder, Cerebral Cortex, Trastorn bipolar, neuroimaging, Radiological and Ultrasound Technology, 05 social sciences, ENIGMA, HUMAN BRAIN, Magnetic Resonance Imaging, psychiatry, 3. Good health, Neurology, Meta-analysis, Scale (social sciences), Anatomy, Psychology, Clinical risk factor, Clinical psychology, MRI, MAJOR PSYCHIATRIC-DISORDERS, Schizoaffective disorder, 050105 experimental psychology, 03 medical and health sciences, Magnetic resonance imaging, Neuroimaging, Meta-Analysis as Topic, SDG 3 - Good Health and Well-being, Imatges per ressonància magnètica, medicine, Humans, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, Bipolar disorder, HIPPOCAMPAL VOLUMES, mega‐analysis, GRAY-MATTER VOLUME, SPECTRUM DISORDER, volume, DIABETES-MELLITUS, cortical thickness, COGNITIVE IMPAIRMENT, medicine.disease, Mental illness, meta-analysis, meta‐analysis, RC0321, Neurology (clinical), SCHIZOAFFECTIVE DISORDER, PSYCHOTIC FEATURES, 030217 neurology & neurosurgery
Abstract

MRI‐derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta‐Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis‐driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large‐scale meta‐ and mega‐analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large‐scale, collaborative studies of mental illness., This review discusses the major challenges facing neuroimaging research of bipolar disorder and highlights the major accomplishments, ongoing challenges and future goals of the ENIGMA Bipolar Disorder Working Group.

Published
2022

5. A comparison of intracranial volume estimation methods and their cross-sectional and longitudinal associations with age.

Author

Nerland S, Stokkan TS, Jørgensen KN, Wortinger LA, Richard G, Beck D, van der Meer D, Westlye LT, Andreassen OA, Agartz I, and Barth C

Subjects
Adolescent, Adult, Aged, Aged, 80 and over, Child, Cross-Sectional Studies, Humans, Middle Aged, Young Adult, Brain diagnostic imaging, Magnetic Resonance Imaging
Abstract

Intracranial volume (ICV) is frequently used in volumetric magnetic resonance imaging (MRI) studies, both as a covariate and as a variable of interest. Findings of associations between ICV and age have varied, potentially due to differences in ICV estimation methods. Here, we compared five commonly used ICV estimation methods and their associations with age. T1-weighted cross-sectional MRI data was included for 651 healthy individuals recruited through the NORMENT Centre (mean age = 46.1 years, range = 12.0-85.8 years) and 2410 healthy individuals recruited through the UK Biobank study (UKB, mean age = 63.2 years, range = 47.0-80.3 years), where longitudinal data was also available. ICV was estimated with FreeSurfer (eTIV and sbTIV), SPM12, CAT12, and FSL. We found overall high correlations across ICV estimation method, with the lowest observed correlations between FSL and eTIV (r = .87) and between FSL and CAT12 (r = .89). Widespread proportional bias was found, indicating that the agreement between methods varied as a function of head size. Body weight, age, sex, and mean ICV across methods explained the most variance in the differences between ICV estimation methods, indicating possible confounding for some estimation methods. We found both positive and negative cross-sectional associations with age, depending on dataset and ICV estimation method. Longitudinal ICV reductions were found for all ICV estimation methods, with annual percentage change ranging from -0.293% to -0.416%. This convergence of longitudinal results across ICV estimation methods offers strong evidence for age-related ICV reductions in mid- to late adulthood., (© 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Published
2022
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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 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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7. Greater male than female variability in regional brain structure across the lifespan.

Author

Wierenga LM, Doucet GE, Dima D, Agartz I, Aghajani M, Akudjedu TN, Albajes-Eizagirre A, Alnaes D, Alpert KI, Andreassen OA, Anticevic A, Asherson P, Banaschewski T, Bargallo N, Baumeister S, Baur-Streubel R, Bertolino A, Bonvino A, Boomsma DI, Borgwardt S, Bourque J, den Braber A, Brandeis D, Breier A, Brodaty H, Brouwer RM, Buitelaar JK, Busatto GF, Calhoun VD, Canales-Rodríguez EJ, Cannon DM, Caseras X, Castellanos FX, Chaim-Avancini TM, Ching CR, Clark VP, Conrod PJ, Conzelmann A, Crivello F, Davey CG, Dickie EW, Ehrlich S, Van't Ent D, Fisher SE, Fouche JP, Franke B, Fuentes-Claramonte P, de Geus EJ, Di Giorgio A, Glahn DC, Gotlib IH, Grabe HJ, Gruber O, Gruner P, Gur RE, Gur RC, Gurholt TP, de Haan L, Haatveit B, Harrison BJ, Hartman CA, Hatton SN, Heslenfeld DJ, van den Heuvel OA, Hickie IB, Hoekstra PJ, Hohmann S, Holmes AJ, Hoogman M, Hosten N, Howells FM, Hulshoff Pol HE, Huyser C, Jahanshad N, James AC, Jiang J, Jönsson EG, Joska JA, Kalnin AJ, Klein M, Koenders L, Kolskår KK, Krämer B, Kuntsi J, Lagopoulos J, Lazaro L, Lebedeva IS, Lee PH, Lochner C, Machielsen MW, Maingault S, Martin NG, Martínez-Zalacaín I, Mataix-Cols D, Mazoyer B, McDonald BC, McDonald C, McIntosh AM, McMahon KL, McPhilemy G, van der Meer D, Menchón JM, Naaijen J, Nyberg L, Oosterlaan J, Paloyelis Y, Pauli P, Pergola G, Pomarol-Clotet E, Portella MJ, Radua J, Reif A, Richard G, Roffman JL, Rosa PG, Sacchet MD, Sachdev PS, Salvador R, Sarró S, Satterthwaite TD, Saykin AJ, Serpa MH, Sim K, Simmons A, Smoller JW, Sommer IE, Soriano-Mas C, Stein DJ, Strike LT, Szeszko PR, Temmingh HS, Thomopoulos SI, Tomyshev AS, Trollor JN, Uhlmann A, Veer IM, Veltman DJ, Voineskos A, Völzke H, Walter H, Wang L, Wang Y, Weber B, Wen W, West JD, Westlye LT, Whalley HC, Williams SC, Wittfeld K, Wolf DH, Wright MJ, Yoncheva YN, Zanetti MV, Ziegler GC, de Zubicaray GI, Thompson PM, Crone EA, Frangou S, and Tamnes CK

Subjects
Female, Humans, Male, Brain Cortical Thickness, Cerebral Cortex anatomy & histology, Cerebral Cortex diagnostic imaging, Biological Variation, Population physiology, Brain anatomy & histology, Brain diagnostic imaging, Human Development physiology, Magnetic Resonance Imaging, Neuroimaging, Sex Characteristics
Abstract

For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Published
2022
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8. In vivo hippocampal subfield volumes in bipolar disorder-A mega-analysis from The Enhancing Neuro Imaging Genetics through Meta-Analysis Bipolar Disorder Working Group.

Author

Haukvik UK, Gurholt TP, Nerland S, Elvsåshagen T, Akudjedu TN, Alda M, Alnaes D, Alonso-Lana S, Bauer J, Baune BT, Benedetti F, Berk M, Bettella F, Bøen E, Bonnín CM, Brambilla P, Canales-Rodríguez EJ, Cannon DM, Caseras X, Dandash O, Dannlowski U, Delvecchio G, Díaz-Zuluaga AM, van Erp TGM, Fatjó-Vilas M, Foley SF, Förster K, Fullerton JM, Goikolea JM, Grotegerd D, Gruber O, Haarman BCM, Haatveit B, Hajek T, Hallahan B, Harris M, Hawkins EL, Howells FM, Hülsmann C, Jahanshad N, Jørgensen KN, Kircher T, Krämer B, Krug A, Kuplicki R, Lagerberg TV, Lancaster TM, Lenroot RK, Lonning V, López-Jaramillo C, Malt UF, McDonald C, McIntosh AM, McPhilemy G, van der Meer D, Melle I, Melloni EMT, Mitchell PB, Nabulsi L, Nenadić I, Oertel V, Oldani L, Opel N, Otaduy MCG, Overs BJ, Pineda-Zapata JA, Pomarol-Clotet E, Radua J, Rauer L, Redlich R, Repple J, Rive MM, Roberts G, Ruhe HG, Salminen LE, Salvador R, Sarró S, Savitz J, Schene AH, Sim K, Soeiro-de-Souza MG, Stäblein M, Stein DJ, Stein F, Tamnes CK, Temmingh HS, Thomopoulos SI, Veltman DJ, Vieta E, Waltemate L, Westlye LT, Whalley HC, Sämann PG, Thompson PM, Ching CRK, Andreassen OA, and Agartz I

Subjects
Bipolar Disorder drug therapy, Genetics, Hippocampus drug effects, Humans, Bipolar Disorder diagnostic imaging, Bipolar Disorder pathology, Hippocampus diagnostic imaging, Hippocampus pathology, Magnetic Resonance Imaging, Neuroimaging
Abstract

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Published
2022
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9. Intelligence, educational attainment, and brain structure in those at familial high-risk for schizophrenia or bipolar disorder.

Author

de Zwarte SMC, Brouwer RM, Agartz I, Alda M, Alonso-Lana S, Bearden CE, Bertolino A, Bonvino A, Bramon E, Buimer EEL, Cahn W, Canales-Rodríguez EJ, Cannon DM, Cannon TD, Caseras X, Castro-Fornieles J, Chen Q, Chung Y, De la Serna E, Del Mar Bonnin C, Demro C, Di Giorgio A, Doucet GE, Eker MC, Erk S, Fatjó-Vilas M, Fears SC, Foley SF, Frangou S, Fullerton JM, Glahn DC, Goghari VM, Goikolea JM, Goldman AL, Gonul AS, Gruber O, Hajek T, Hawkins EL, Heinz A, Hidiroglu Ongun C, Hillegers MHJ, Houenou J, Hulshoff Pol HE, Hultman CM, Ingvar M, Johansson V, Jönsson EG, Kane F, Kempton MJ, Koenis MMG, Kopecek M, Krämer B, Lawrie SM, Lenroot RK, Marcelis M, Mattay VS, McDonald C, Meyer-Lindenberg A, Michielse S, Mitchell PB, Moreno D, Murray RM, Mwangi B, Nabulsi L, Newport J, Olman CA, van Os J, Overs BJ, Ozerdem A, Pergola G, Picchioni MM, Piguet C, Pomarol-Clotet E, Radua J, Ramsay IS, Richter A, Roberts G, Salvador R, Saricicek Aydogan A, Sarró S, Schofield PR, Simsek EM, Simsek F, Soares JC, Sponheim SR, Sugranyes G, Toulopoulou T, Tronchin G, Vieta E, Walter H, Weinberger DR, Whalley HC, Wu MJ, Yalin N, Andreassen OA, Ching CRK, Thomopoulos SI, van Erp TGM, Jahanshad N, Thompson PM, Kahn RS, and van Haren NEM

Subjects
Bipolar Disorder complications, Bipolar Disorder diagnostic imaging, Cognitive Dysfunction diagnostic imaging, Family, Humans, Magnetic Resonance Imaging, Schizophrenia complications, Schizophrenia diagnostic imaging, Schizophrenia etiology, Bipolar Disorder pathology, Cognitive Dysfunction pathology, Educational Status, Genetic Predisposition to Disease, Intelligence physiology, Neuroimaging, Schizophrenia pathology
Abstract

First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d = -0.42, p = 3 × 10 -5 ), with weak evidence of IQ reductions among BD-FDRs (d = -0.23, p = .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. In contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Published
2022
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10. Intracranial and subcortical volumes in adolescents with early-onset psychosis: A multisite mega-analysis from the ENIGMA consortium.

Author

Gurholt TP, Lonning V, Nerland S, Jørgensen KN, Haukvik UK, Alloza C, Arango C, Barth C, Bearden CE, Berk M, Bohman H, Dandash O, Díaz-Caneja CM, Edbom CT, van Erp TGM, Fett AJ, Frangou S, Goldstein BI, Grigorian A, Jahanshad N, James AC, Janssen J, Johannessen C, Karlsgodt KH, Kempton MJ, Kochunov P, Krabbendam L, Kyriakopoulos M, Lundberg M, MacIntosh BJ, Rund BR, Smelror RE, Sultan A, Tamnes CK, Thomopoulos SI, Vajdi A, Wedervang-Resell K, Myhre AM, Andreassen OA, Thompson PM, and Agartz I

Subjects
Adolescent, Affective Disorders, Psychotic diagnostic imaging, Age of Onset, Brain diagnostic imaging, Globus Pallidus diagnostic imaging, Globus Pallidus pathology, Hippocampus diagnostic imaging, Hippocampus pathology, Humans, Magnetic Resonance Imaging, Psychotic Disorders diagnostic imaging, Schizophrenia diagnostic imaging, Adolescent Development physiology, Affective Disorders, Psychotic pathology, Brain pathology, Psychotic Disorders pathology, Schizophrenia pathology
Abstract

Early-onset psychosis disorders are serious mental disorders arising before the age of 18 years. Here, we investigate the largest neuroimaging dataset, to date, of patients with early-onset psychosis and healthy controls for differences in intracranial and subcortical brain volumes. The sample included 263 patients with early-onset psychosis (mean age: 16.4 ± 1.4 years, mean illness duration: 1.5 ± 1.4 years, 39.2% female) and 359 healthy controls (mean age: 15.9 ± 1.7 years, 45.4% female) with magnetic resonance imaging data, pooled from 11 clinical cohorts. Patients were diagnosed with early-onset schizophrenia (n = 183), affective psychosis (n = 39), or other psychotic disorders (n = 41). We used linear mixed-effects models to investigate differences in intracranial and subcortical volumes across the patient sample, diagnostic subgroup and antipsychotic medication, relative to controls. We observed significantly lower intracranial (Cohen's d = -0.39) and hippocampal (d = -0.25) volumes, and higher caudate (d = 0.25) and pallidum (d = 0.24) volumes in patients relative to controls. Intracranial volume was lower in both early-onset schizophrenia (d = -0.34) and affective psychosis (d = -0.42), and early-onset schizophrenia showed lower hippocampal (d = -0.24) and higher pallidum (d = 0.29) volumes. Patients who were currently treated with antipsychotic medication (n = 193) had significantly lower intracranial volume (d = -0.42). The findings demonstrate a similar pattern of brain alterations in early-onset psychosis as previously reported in adult psychosis, but with notably low intracranial volume. The low intracranial volume suggests disrupted neurodevelopment in adolescent early-onset psychosis., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Published
2022
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11. Effects of copy number variations on brain structure and risk for psychiatric illness: Large-scale studies from the ENIGMA working groups on CNVs.

Author

Sønderby IE, Ching CRK, Thomopoulos SI, van der Meer D, Sun D, Villalon-Reina JE, Agartz I, Amunts K, Arango C, Armstrong NJ, Ayesa-Arriola R, Bakker G, Bassett AS, Boomsma DI, Bülow R, Butcher NJ, Calhoun VD, Caspers S, Chow EWC, Cichon S, Ciufolini S, Craig MC, Crespo-Facorro B, Cunningham AC, Dale AM, Dazzan P, de Zubicaray GI, Djurovic S, Doherty JL, Donohoe G, Draganski B, Durdle CA, Ehrlich S, Emanuel BS, Espeseth T, Fisher SE, Ge T, Glahn DC, Grabe HJ, Gur RE, Gutman BA, Haavik J, Håberg AK, Hansen LA, Hashimoto R, Hibar DP, Holmes AJ, Hottenga JJ, Hulshoff Pol HE, Jalbrzikowski M, Knowles EEM, Kushan L, Linden DEJ, Liu J, Lundervold AJ, Martin-Brevet S, Martínez K, Mather KA, Mathias SR, McDonald-McGinn DM, McRae AF, Medland SE, Moberget T, Modenato C, Monereo Sánchez J, Moreau CA, Mühleisen TW, Paus T, Pausova Z, Prieto C, Ragothaman A, Reinbold CS, Reis Marques T, Repetto GM, Reymond A, Roalf DR, Rodriguez-Herreros B, Rucker JJ, Sachdev PS, Schmitt JE, Schofield PR, Silva AI, Stefansson H, Stein DJ, Tamnes CK, Tordesillas-Gutiérrez D, Ulfarsson MO, Vajdi A, van 't Ent D, van den Bree MBM, Vassos E, Vázquez-Bourgon J, Vila-Rodriguez F, Walters GB, Wen W, Westlye LT, Wittfeld K, Zackai EH, Stefánsson K, Jacquemont S, Thompson PM, Bearden CE, and Andreassen OA

Subjects
Humans, Multicenter Studies as Topic, Brain diagnostic imaging, Brain growth & development, Brain pathology, DNA Copy Number Variations, Magnetic Resonance Imaging, Mental Disorders diagnostic imaging, Mental Disorders genetics, Mental Disorders pathology, Neurodevelopmental Disorders diagnostic imaging, Neurodevelopmental Disorders genetics, Neurodevelopmental Disorders pathology, Neuroimaging
Abstract

The Enhancing NeuroImaging Genetics through Meta-Analysis copy number variant (ENIGMA-CNV) and 22q11.2 Deletion Syndrome Working Groups (22q-ENIGMA WGs) were created to gain insight into the involvement of genetic factors in human brain development and related cognitive, psychiatric and behavioral manifestations. To that end, the ENIGMA-CNV WG has collated CNV and magnetic resonance imaging (MRI) data from ~49,000 individuals across 38 global research sites, yielding one of the largest studies to date on the effects of CNVs on brain structures in the general population. The 22q-ENIGMA WG includes 12 international research centers that assessed over 533 individuals with a confirmed 22q11.2 deletion syndrome, 40 with 22q11.2 duplications, and 333 typically developing controls, creating the largest-ever 22q11.2 CNV neuroimaging data set. In this review, we outline the ENIGMA infrastructure and procedures for multi-site analysis of CNVs and MRI data. So far, ENIGMA has identified effects of the 22q11.2, 16p11.2 distal, 15q11.2, and 1q21.1 distal CNVs on subcortical and cortical brain structures. Each CNV is associated with differences in cognitive, neurodevelopmental and neuropsychiatric traits, with characteristic patterns of brain structural abnormalities. Evidence of gene-dosage effects on distinct brain regions also emerged, providing further insight into genotype-phenotype relationships. Taken together, these results offer a more comprehensive picture of molecular mechanisms involved in typical and atypical brain development. This "genotype-first" approach also contributes to our understanding of the etiopathogenesis of brain disorders. Finally, we outline future directions to better understand effects of CNVs on brain structure and behavior., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Published
2022
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12. 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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13. What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group.

Author

Ching CRK, Hibar DP, Gurholt TP, Nunes A, Thomopoulos SI, Abé C, Agartz I, Brouwer RM, Cannon DM, de Zwarte SMC, Eyler LT, Favre P, Hajek T, Haukvik UK, Houenou J, Landén M, Lett TA, McDonald C, Nabulsi L, Patel Y, Pauling ME, Paus T, Radua J, Soeiro-de-Souza MG, Tronchin G, van Haren NEM, Vieta E, Walter H, Zeng LL, Alda M, Almeida J, Alnaes D, Alonso-Lana S, Altimus C, Bauer M, Baune BT, Bearden CE, Bellani M, Benedetti F, Berk M, Bilderbeck AC, Blumberg HP, Bøen E, Bollettini I, Del Mar Bonnin C, Brambilla P, Canales-Rodríguez EJ, Caseras X, Dandash O, Dannlowski U, Delvecchio G, Díaz-Zuluaga AM, Dima D, Duchesnay É, Elvsåshagen T, Fears SC, Frangou S, Fullerton JM, Glahn DC, Goikolea JM, Green MJ, Grotegerd D, Gruber O, Haarman BCM, Henry C, Howells FM, Ives-Deliperi V, Jansen A, Kircher TTJ, Knöchel C, Kramer B, Lafer B, López-Jaramillo C, Machado-Vieira R, MacIntosh BJ, Melloni EMT, Mitchell PB, Nenadic I, Nery F, Nugent AC, Oertel V, Ophoff RA, Ota M, Overs BJ, Pham DL, Phillips ML, Pineda-Zapata JA, Poletti S, Polosan M, Pomarol-Clotet E, Pouchon A, Quidé Y, Rive MM, Roberts G, Ruhe HG, Salvador R, Sarró S, Satterthwaite TD, Schene AH, Sim K, Soares JC, Stäblein M, Stein DJ, Tamnes CK, Thomaidis GV, Upegui CV, Veltman DJ, Wessa M, Westlye LT, Whalley HC, Wolf DH, Wu MJ, Yatham LN, Zarate CA, Thompson PM, and Andreassen OA

Subjects
Humans, Meta-Analysis as Topic, Multicenter Studies as Topic, Bipolar Disorder diagnostic imaging, Bipolar Disorder pathology, Cerebral Cortex diagnostic imaging, Cerebral Cortex pathology, Magnetic Resonance Imaging, Neuroimaging
Abstract

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Published
2022
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14. A meta-analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium.

Author

Gutman BA, van Erp TGM, Alpert K, Ching CRK, Isaev D, Ragothaman A, Jahanshad N, Saremi A, Zavaliangos-Petropulu A, Glahn DC, Shen L, Cong S, Alnaes D, Andreassen OA, Doan NT, Westlye LT, Kochunov P, Satterthwaite TD, Wolf DH, Huang AJ, Kessler C, Weideman A, Nguyen D, Mueller BA, Faziola L, Potkin SG, Preda A, Mathalon DH, Bustillo J, Calhoun V, Ford JM, Walton E, Ehrlich S, Ducci G, Banaj N, Piras F, Piras F, Spalletta G, Canales-Rodríguez EJ, Fuentes-Claramonte P, Pomarol-Clotet E, Radua J, Salvador R, Sarró S, Dickie EW, Voineskos A, Tordesillas-Gutiérrez D, Crespo-Facorro B, Setién-Suero E, van Son JM, Borgwardt S, Schönborn-Harrisberger F, Morris D, Donohoe G, Holleran L, Cannon D, McDonald C, Corvin A, Gill M, Filho GB, Rosa PGP, Serpa MH, Zanetti MV, Lebedeva I, Kaleda V, Tomyshev A, Crow T, James A, Cervenka S, Sellgren CM, Fatouros-Bergman H, Agartz I, Howells F, Stein DJ, Temmingh H, Uhlmann A, de Zubicaray GI, McMahon KL, Wright M, Cobia D, Csernansky JG, Thompson PM, Turner JA, and Wang L

Subjects
Amygdala diagnostic imaging, Corpus Striatum diagnostic imaging, Hippocampus diagnostic imaging, Humans, Multicenter Studies as Topic, Schizophrenia diagnostic imaging, Thalamus diagnostic imaging, Amygdala pathology, Corpus Striatum pathology, Hippocampus pathology, Neuroimaging, Schizophrenia pathology, Thalamus pathology
Abstract

Schizophrenia is associated with widespread alterations in subcortical brain structure. While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta-analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between individuals with schizophrenia and healthy control participants. The study analyzed data from 2,833 individuals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippocampus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1-weighted structural MRI scans. Mass univariate meta-analyses revealed more-concave-than-convex shape differences in the hippocampus, amygdala, accumbens, and thalamus in individuals with schizophrenia compared with control participants, more-convex-than-concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippocampus, amygdala, and thalamus in individuals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta-analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Published
2022
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15. Replicating extensive brain structural heterogeneity in individuals with schizophrenia and bipolar disorder.

Author

Wolfers T, Rokicki J, Alnaes D, Berthet P, Agartz I, Kia SM, Kaufmann T, Zabihi M, Moberget T, Melle I, Beckmann CF, Andreassen OA, Marquand AF, and Westlye LT

Subjects
Adult, Bipolar Disorder diagnostic imaging, Female, Gray Matter diagnostic imaging, Humans, Male, Middle Aged, Reproducibility of Results, Schizophrenia diagnostic imaging, Young Adult, Bipolar Disorder pathology, Gray Matter pathology, Magnetic Resonance Imaging methods, Magnetic Resonance Imaging standards, Neuroimaging methods, Neuroimaging standards, Schizophrenia pathology
Abstract

Identifying brain processes involved in the risk and development of mental disorders is a major aim. We recently reported substantial interindividual heterogeneity in brain structural aberrations among patients with schizophrenia and bipolar disorder. Estimating the normative range of voxel-based morphometry (VBM) data among healthy individuals using a Gaussian process regression (GPR) enables us to map individual deviations from the healthy range in unseen datasets. Here, we aim to replicate our previous results in two independent samples of patients with schizophrenia (n1 = 94; n2 = 105), bipolar disorder (n1 = 116; n2 = 61), and healthy individuals (n1 = 400; n2 = 312). In line with previous findings with exception of the cerebellum our results revealed robust group level differences between patients and healthy individuals, yet only a small proportion of patients with schizophrenia or bipolar disorder exhibited extreme negative deviations from normality in the same brain regions. These direct replications support that group level-differences in brain structure disguise considerable individual differences in brain aberrations, with important implications for the interpretation and generalization of group-level brain imaging findings to the individual with a mental disorder., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Published
2021
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16. Sparse deep neural networks on imaging genetics for schizophrenia case-control classification.

Author

Chen J, Li X, Calhoun VD, Turner JA, van Erp TGM, Wang L, Andreassen OA, Agartz I, Westlye LT, Jönsson E, Ford JM, Mathalon DH, Macciardi F, O'Leary DS, Liu J, and Ji S

Subjects
Adult, Case-Control Studies, Cerebral Cortex diagnostic imaging, Gray Matter diagnostic imaging, Humans, Magnetic Resonance Imaging, Polymorphism, Single Nucleotide, Schizophrenia classification, Schizophrenia diagnostic imaging, Support Vector Machine, Cerebral Cortex pathology, Deep Learning, Gray Matter pathology, Neuroimaging methods, Schizophrenia genetics, Schizophrenia pathology
Abstract

Deep learning methods hold strong promise for identifying biomarkers for clinical application. However, current approaches for psychiatric classification or prediction do not allow direct interpretation of original features. In the present study, we introduce a sparse deep neural network (DNN) approach to identify sparse and interpretable features for schizophrenia (SZ) case-control classification. An L 0 -norm regularization is implemented on the input layer of the network for sparse feature selection, which can later be interpreted based on importance weights. We applied the proposed approach on a large multi-study cohort with gray matter volume (GMV) and single nucleotide polymorphism (SNP) data for SZ classification. A total of 634 individuals served as training samples, and the classification model was evaluated for generalizability on three independent datasets of different scanning protocols (N = 394, 255, and 160, respectively). We examined the classification power of pure GMV features, as well as combined GMV and SNP features. Empirical experiments demonstrated that sparse DNN slightly outperformed independent component analysis + support vector machine (ICA + SVM) framework, and more effectively fused GMV and SNP features for SZ discrimination, with an average error rate of 28.98% on external data. The importance weights suggested that the DNN model prioritized to select frontal and superior temporal gyrus for SZ classification with high sparsity, with parietal regions further included with lower sparsity, echoing previous literature. The results validate the application of the proposed approach to SZ classification, and promise extended utility on other data modalities and traits which ultimately may result in clinically useful tools., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Published
2021
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17. Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders.

Author

Rokicki J, Wolfers T, Nordhøy W, Tesli N, Quintana DS, Alnaes D, Richard G, de Lange AG, Lund MJ, Norbom L, Agartz I, Melle I, Naerland T, Selbaek G, Persson K, Nordvik JE, Schwarz E, Andreassen OA, Kaufmann T, and Westlye LT

Subjects
Adolescent, Adult, Age Factors, Aged, Aged, 80 and over, Alzheimer Disease pathology, Bipolar Disorder pathology, Brain blood supply, Brain pathology, Case-Control Studies, Cerebrovascular Circulation physiology, Cognitive Dysfunction pathology, Female, Humans, Male, Middle Aged, Multimodal Imaging, Schizophrenia pathology, Spin Labels, Young Adult, Alzheimer Disease diagnostic imaging, Bipolar Disorder diagnostic imaging, Brain diagnostic imaging, Cognitive Dysfunction diagnostic imaging, Magnetic Resonance Imaging methods, Neuroimaging methods, Schizophrenia diagnostic imaging
Abstract

The deviation between chronological age and age predicted using brain MRI is a putative marker of overall brain health. Age prediction based on structural MRI data shows high accuracy in common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the underlying biological processes. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub-cortical volumes, cortical and subcortical T1/T2-weighted ratios, and cerebral blood flow (CBF) based on arterial spin labeling. For each of the 11 models we assessed the age prediction accuracy in healthy controls (HC, n = 750) and compared the obtained brain age gaps (BAGs) between age-matched subsets of HC and patients with Alzheimer's disease (AD, n = 54), mild (MCI, n = 90) and subjective (SCI, n = 56) cognitive impairment, schizophrenia spectrum (SZ, n = 159) and bipolar disorder (BD, n = 135). We found highest age prediction accuracy in HC when integrating all modalities. Furthermore, two-group case-control classifications revealed highest accuracy for AD using global T1-weighted BAG, while MCI, SCI, BD and SZ showed strongest effects in CBF-based BAGs. Combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. The multimodal BAG was most accurate in predicting age in HC, while group differences between patients and HC were often larger for BAGs based on single modalities. These findings indicate that multidimensional neuroimaging of patients may provide a brain-based mapping of overlapping and distinct pathophysiology in common disorders., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Published
2021
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18. Women's brain aging: Effects of sex-hormone exposure, pregnancies, and genetic risk for Alzheimer's disease.

Author

de Lange AG, Barth C, Kaufmann T, Maximov II, van der Meer D, Agartz I, and Westlye LT

Subjects
Adult, Age Factors, Aged, Aging drug effects, Apolipoprotein E4 genetics, Brain drug effects, Databases, Factual, Estradiol blood, Female, Genetic Predisposition to Disease, Humans, Middle Aged, Neuroimaging, Pregnancy, Risk Factors, Time Factors, Aging metabolism, Alzheimer Disease genetics, Alzheimer Disease metabolism, Brain diagnostic imaging, Brain metabolism, Estrogens metabolism, Hormone Replacement Therapy, Parity physiology
Abstract

Sex hormones such as estrogen fluctuate across the female lifespan, with high levels during reproductive years and natural decline during the transition to menopause. Women's exposure to estrogen may influence their heightened risk of Alzheimer's disease (AD) relative to men, but little is known about how it affects normal brain aging. Recent findings from the UK Biobank demonstrate less apparent brain aging in women with a history of multiple childbirths, highlighting a potential link between sex-hormone exposure and brain aging. We investigated endogenous and exogenous sex-hormone exposure, genetic risk for AD, and neuroimaging-derived biomarkers for brain aging in 16,854 middle to older-aged women. The results showed that as opposed to parity, higher cumulative sex-hormone exposure was associated with more evident brain aging, indicating that i) high levels of cumulative exposure to sex-hormones may have adverse effects on the brain, and ii) beneficial effects of pregnancies on the female brain are not solely attributable to modulations in sex-hormone exposure. In addition, for women using hormonal replacement therapy (HRT), starting treatment earlier was associated with less evident brain aging, but only in women with a genetic risk for AD. Genetic factors may thus contribute to how timing of HRT initiation influences women's brain aging trajectories., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Published
2020
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