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

Author

García-Marín, Luis M., Campos, Adrian I., Diaz-Torres, Santiago, Rabinowitz, Jill A., Ceja, Zuriel, Mitchell, Brittany L., Grasby, Katrina L., Thorp, Jackson G., Agartz, Ingrid, Alhusaini, Saud, Ames, David, Amouyel, Philippe, Andreassen, Ole A., Arfanakis, Konstantinos, Arias-Vasquez, Alejandro, Armstrong, Nicola J., Athanasiu, Lavinia, Bastin, Mark E., Beiser, Alexa S., Bennett, David A., Bis, Joshua C., Boks, Marco P. M., Boomsma, Dorret I., Brodaty, Henry, Brouwer, Rachel M., Buitelaar, Jan K., Burkhardt, Ralph, Cahn, Wiepke, Calhoun, Vince D., Carmichael, Owen T., Chakravarty, Mallar, Chen, Qiang, Ching, Christopher R. K., Cichon, Sven, Crespo-Facorro, Benedicto, Crivello, Fabrice, Dale, Anders M., Smith, George Davey, de Geus, Eco J. C., De Jager, Philip L., de Zubicaray, Greig I., Debette, Stéphanie, DeCarli, Charles, Depondt, Chantal, Desrivières, Sylvane, Djurovic, Srdjan, Ehrlich, Stefan, Erk, Susanne, Espeseth, Thomas, Fernández, Guillén, Filippi, Irina, Fisher, Simon E., Fleischman, Debra A., Fletcher, Evan, Fornage, Myriam, Forstner, Andreas J., Francks, Clyde, Franke, Barbara, Ge, Tian, Goldman, Aaron L., Grabe, Hans J., Green, Robert C., Grimm, Oliver, Groenewold, Nynke A., Gruber, Oliver, Gudnason, Vilmundur, Håberg, Asta K., Haukvik, Unn K., Heinz, Andreas, Hibar, Derrek P., Hilal, Saima, Himali, Jayandra J., Ho, Beng-Choon, Hoehn, David F., Hoekstra, Pieter J., Hofer, Edith, Hoffmann, Wolfgang, Holmes, Avram J., Homuth, Georg, Hosten, Norbert, Ikram, M. Kamran, Ipser, Jonathan C., Jack Jr, Clifford R., Jahanshad, Neda, Jönsson, Erik G., Kahn, Rene S., Kanai, Ryota, Klein, Marieke, Knol, Maria J., Launer, Lenore J., Lawrie, Stephen M., Hellard, Stephanie Le, Lee, Phil H., Lemaître, Hervé, Li, Shuo, Liewald, David C. M., Lin, Honghuang, Longstreth, Jr, W. T., Lopez, Oscar L., Luciano, Michelle, Maillard, Pauline, Marquand, Andre F., Martin, Nicholas G., Martinot, Jean-Luc, Mather, Karen A., Mattay, Venkata S., McMahon, Katie L., Mecocci, Patrizia, Melle, Ingrid, Meyer-Lindenberg, Andreas, Mirza-Schreiber, Nazanin, Milaneschi, Yuri, Mosley, Thomas H., Mühleisen, Thomas W., Müller-Myhsok, Bertram, Maniega, Susana Muñoz, Nauck, Matthias, Nho, Kwangsik, Niessen, Wiro J., Nöthen, Markus M., Nyquist, Paul A., Oosterlaan, Jaap, Pandolfo, Massimo, Paus, Tomas, Pausova, Zdenka, Penninx, Brenda W. J. H., Pike, G. Bruce, Psaty, Bruce M., Pütz, Benno, Reppermund, Simone, Rietschel, Marcella D., Risacher, Shannon L., Romanczuk-Seiferth, Nina, Romero-Garcia, Rafael, Roshchupkin, Gennady V., Rotter, Jerome I., Sachdev, Perminder S., Sämann, Philipp G., Saremi, Arvin, Sargurupremraj, Muralidharan, Saykin, Andrew J., Schmaal, Lianne, Schmidt, Helena, Schmidt, Reinhold, Schofield, Peter R., Scholz, Markus, Schumann, Gunter, Schwarz, Emanuel, Shen, Li, Shin, Jean, Sisodiya, Sanjay M., Smith, Albert V., Smoller, Jordan W., Soininen, Hilkka S., Steen, Vidar M., Stein, Dan J., Stein, Jason L., Thomopoulos, Sophia I., Toga, Arthur W., Tordesillas-Gutiérrez, Diana, Trollor, Julian N., Valdes-Hernandez, Maria C., van ′t Ent, Dennis, van Bokhoven, Hans, van der Meer, Dennis, van der Wee, Nic J. A., Vázquez-Bourgon, Javier, Veltman, Dick J., Vernooij, Meike W., Villringer, Arno, Vinke, Louis N., Völzke, Henry, Walter, Henrik, Wardlaw, Joanna M., Weinberger, Daniel R., Weiner, Michael W., Wen, Wei, Westlye, Lars T., Westman, Eric, White, Tonya, Witte, A. Veronica, Wolf, Christiane, Yang, Jingyun, Zwiers, Marcel P., Ikram, M. Arfan, Seshadri, Sudha, Thompson, Paul M., Satizabal, Claudia L., Medland, Sarah E., and Rentería, Miguel E.

Published
2024
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3. DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Zeng, Ling-Li, Ching, Christopher R. K., Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Brosch, Katharina, Bülow, Robin, Colle, Romain, Connolly, Colm G., Corruble, Emmanuelle, Couvy-Duchesne, Baptiste, Cullen, Kathryn, Dannlowski, Udo, Davey, Christopher G., Dols, Annemiek, Ernsting, Jan, Evans, Jennifer W., Fisch, Lukas, Fuentes-Claramonte, Paola, Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke A., Grotegerd, Dominik, Hahn, Tim, Hamilton, J. Paul, Han, Laura K. M., Harrison, Ben J, Ho, Tiffany C., Jahanshad, Neda, Jamieson, Alec J., Karuk, Andriana, Kircher, Tilo, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Leenings, Ramona, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Meinert, Susanne, Melloni, Elisa, Mueller, Bryon A., Mwangi, Benson, Nenadić, Igor, Ojha, Amar, Okamoto, Yasumasa, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Radua, Joaquim, Rodríguez-Cano, Elena, Sacchet, Matthew D., Salvador, Raymond, Schrantee, Anouk, Sim, Kang, Soares, Jair C., Solanes, Aleix, Stein, Dan J., Stein, Frederike, Stolicyn, Aleks, Thomopoulos, Sophia I., Toenders, Yara J., Uyar-Demir, Aslihan, Vieta, Eduard, Vives-Gilabert, Yolanda, Völzke, Henry, Walter, Martin, Whalley, Heather C., Whittle, Sarah, Winter, Nils, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible.

Published
2023

4. Smaller total and subregional cerebellar volumes in posttraumatic stress disorder: a mega-analysis by the ENIGMA-PGC PTSD workgroup

Author

Huggins, Ashley A, Baird, C Lexi, Briggs, Melvin, Laskowitz, Sarah, Hussain, Ahmed, Fouda, Samar, Haswell, Courtney, Sun, Delin, Salminen, Lauren E, Jahanshad, Neda, Thomopoulos, Sophia I, Veltman, Dick J, Frijling, Jessie L, Olff, Miranda, van Zuiden, Mirjam, Koch, Saskia BJ, Nawjin, Laura, Wang, Li, Zhu, Ye, Li, Gen, Stein, Dan J, Ipser, Jonathan, Seedat, Soraya, du Plessis, Stefan, van den Heuvel, Leigh L, Suarez-Jimenez, Benjamin, Zhu, Xi, Kim, Yoojean, He, Xiaofu, Zilcha-Mano, Sigal, Lazarov, Amit, Neria, Yuval, Stevens, Jennifer S, Ressler, Kerry J, Jovanovic, Tanja, van Rooij, Sanne JH, Fani, Negar, Hudson, Anna R, Mueller, Sven C, Sierk, Anika, Manthey, Antje, Walter, Henrik, Daniels, Judith K, Schmahl, Christian, Herzog, Julia I, Říha, Pavel, Rektor, Ivan, Lebois, Lauren AM, Kaufman, Milissa L, Olson, Elizabeth A, Baker, Justin T, Rosso, Isabelle M, King, Anthony P, Liberzon, Isreal, Angstadt, Mike, Davenport, Nicholas D, Sponheim, Scott R, Disner, Seth G, Straube, Thomas, Hofmann, David, Qi, Rongfeng, Lu, Guang Ming, Baugh, Lee A, Forster, Gina L, Simons, Raluca M, Simons, Jeffrey S, Magnotta, Vincent A, Fercho, Kelene A, Maron-Katz, Adi, Etkin, Amit, Cotton, Andrew S, O’Leary, Erin N, Xie, Hong, Wang, Xin, Quidé, Yann, El-Hage, Wissam, Lissek, Shmuel, Berg, Hannah, Bruce, Steven, Cisler, Josh, Ross, Marisa, Herringa, Ryan J, Grupe, Daniel W, Nitschke, Jack B, Davidson, Richard J, Larson, Christine L, deRoon-Cassini, Terri A, Tomas, Carissa W, Fitzgerald, Jacklynn M, Blackford, Jennifer Urbano, Olatunji, Bunmi O, Kremen, William S, Lyons, Michael J, Franz, Carol E, Gordon, Evan M, May, Geoffrey, Nelson, Steven M, Abdallah, Chadi G, Levy, Ifat, and Harpaz-Rotem, Ilan

Subjects
Clinical and Health Psychology, Psychology, Mind and Body, Mental Illness, Anxiety Disorders, Neurosciences, Brain Disorders, Post-Traumatic Stress Disorder (PTSD), Behavioral and Social Science, Mental Health, Mental health, Humans, Stress Disorders, Post-Traumatic, Cerebellum, Female, Male, Adult, Magnetic Resonance Imaging, Middle Aged, White Matter, Gray Matter, Organ Size, Deep Learning, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p-FDR

Published
2024

6. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Aghajani, Moji, Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Bülow, Robin, Ching, Christopher R. K., Connolly, Colm G., Cullen, Kathryn, Davey, Christopher G., Dima, Danai, Dols, Annemiek, Evans, Jennifer W., Fu, Cynthia H. Y., Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke, Hamilton, J Paul, Harrison, Ben J., Ho, Tiffany C., Mwangi, Benson, Jaworska, Natalia, Jahanshad, Neda, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Melloni, Elisa, Mueller, Bryon A., Ojha, Amar, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Reneman, Liesbeth, Sacchet, Matthew D., Sämann, Philipp G., Schrantee, Anouk, Sim, Kang, Soares, Jair C., Stein, Dan J., Thomopoulos, Sophia I., Uyar-Demir, Aslihan, van der Wee, Nic J. A., van der Werff, Steven J. A., Völzke, Henry, Whittle, Sarah, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Published
2024
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7. Examining the association between posttraumatic stress disorder and disruptions in cortical networks identified using data-driven methods

Author

Yang, Jin, Huggins, Ashley A., Sun, Delin, Baird, C. Lexi, Haswell, Courtney C., Frijling, Jessie L., Olff, Miranda, van Zuiden, Mirjam, Koch, Saskia B. J., Nawijn, Laura, Veltman, Dick J., Suarez-Jimenez, Benjamin, Zhu, Xi, Neria, Yuval, Hudson, Anna R., Mueller, Sven C., Baker, Justin T., Lebois, Lauren A. M., Kaufman, Milissa L., Qi, Rongfeng, Lu, Guang Ming, Říha, Pavel, Rektor, Ivan, Dennis, Emily L., Ching, Christopher R. K., Thomopoulos, Sophia I., Salminen, Lauren E., Jahanshad, Neda, Thompson, Paul M., Stein, Dan J., Koopowitz, Sheri M., Ipser, Jonathan C., Seedat, Soraya, du Plessis, Stefan, van den Heuvel, Leigh L., Wang, Li, Zhu, Ye, Li, Gen, Sierk, Anika, Manthey, Antje, Walter, Henrik, Daniels, Judith K., Schmahl, Christian, Herzog, Julia I., Liberzon, Israel, King, Anthony, Angstadt, Mike, Davenport, Nicholas D., Sponheim, Scott R., Disner, Seth G., Straube, Thomas, Hofmann, David, Grupe, Daniel W., Nitschke, Jack B., Davidson, Richard J., Larson, Christine L., deRoon-Cassini, Terri A., Blackford, Jennifer U., Olatunji, Bunmi O., Gordon, Evan M., May, Geoffrey, Nelson, Steven M., Abdallah, Chadi G., Levy, Ifat, Harpaz-Rotem, Ilan, Krystal, John H., Morey, Rajendra A., and Sotiras, Aristeidis

Published
2024
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8. Volume of subcortical brain regions in social anxiety disorder: mega-analytic results from 37 samples in the ENIGMA-Anxiety Working Group.

Author

Groenewold, Nynke, Bas-Hoogendam, Janna, Amod, Alyssa, Laansma, Max, Van Velzen, Laura, Aghajani, Moji, Hilbert, Kevin, Oh, Hyuntaek, Salas, Ramiro, Jackowski, Andrea, Pan, Pedro, Salum, Giovanni, Blair, James, Blair, Karina, Hirsch, Joy, Pantazatos, Spiro, Schneier, Franklin, Talati, Ardesheer, Roelofs, Karin, Volman, Inge, Blanco-Hinojo, Laura, Cardoner, Narcís, Pujol, Jesus, Beesdo-Baum, Katja, Ching, Christopher, Thomopoulos, Sophia, Jansen, Andreas, Kircher, Tilo, Krug, Axel, Nenadić, Igor, Stein, Frederike, Dannlowski, Udo, Grotegerd, Dominik, Lemke, Hannah, Meinert, Susanne, Winter, Alexandra, Erb, Michael, Kreifelts, Benjamin, Gong, Qiyong, Lui, Su, Zhu, Fei, Mwangi, Benson, Soares, Jair, Wu, Mon-Ju, Bayram, Ali, Canli, Mesut, Tükel, Raşit, Westenberg, P, Heeren, Alexandre, Cremers, Henk, Hofmann, David, Straube, Thomas, Doruyter, Alexander, Lochner, Christine, Peterburs, Jutta, Van Tol, Marie-José, Gur, Raquel, Kaczkurkin, Antonia, Larsen, Bart, Satterthwaite, Theodore, Filippi, Courtney, Gold, Andrea, Harrewijn, Anita, Zugman, André, Bülow, Robin, Grabe, Hans, Völzke, Henry, Wittfeld, Katharina, Böhnlein, Joscha, Dohm, Katharina, Kugel, Harald, Schrammen, Elisabeth, Zwanzger, Peter, Leehr, Elisabeth, Sindermann, Lisa, Ball, Tali, Fonzo, Gregory, Paulus, Martin, Stein, Murray, Klumpp, Heide, Phan, K, Furmark, Tomas, Månsson, Kristoffer, Manzouri, Amirhossein, Avery, Suzanne, Blackford, Jennifer, Clauss, Jacqueline, Feola, Brandee, Harper, Jennifer, Sylvester, Chad, Lueken, Ulrike, Veltman, Dick, Winkler, Anderson, Jahanshad, Neda, Pine, Daniel, Thompson, Paul, Stein, Dan, Van der Wee, Nic, and Simmons, Alan

Subjects
Adult, Adolescent, Humans, Phobia, Social, Magnetic Resonance Imaging, Brain, Anxiety, Neuroimaging
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

Published
2023

9. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Gonul, Ali Saffet, Amod, Alyssa R., Ojha, Amar, Aleman, Andre, Dols, Annemiek, Scharntee, Anouk, Uyar-Demir, Aslihan, Harrison, Ben J, Irungu, Benson M., Besteher, Bianca, Klimes-Dougan, Bonnie, Penninx, Brenda W. J. H., Mueller, Bryon A., Zarate, Carlos, Davey, Christopher G., Ching, Christopher R. K., Connolly, Colm G., Fu, Cynthia H. Y., Stein, Dan J., Dima, Danai, Linden, David E. J., Mehler, David M. A., Pomarol-Clotet, Edith, Pozzi, Elena, Melloni, Elisa, Benedetti, Francesco, MacMaster, Frank P., Grabe, Hans J., Völzke, Henry, Gotlib, Ian H., Soares, Jair C., Evans, Jennifer W., Sim, Kang, Wittfeld, Katharina, Cullen, Kathryn, Reneman, Liesbeth, Oudega, Mardien L., Wright, Margaret J., Portella, Maria J., Sacchet, Matthew D., Li, Meng, Aghajani, Moji, Wu, Mon-Ju, Jaworska, Natalia, Jahanshad, Neda, van der Wee, Nic J. A., Groenewold, Nynke, Hamilton, Paul J., Saemann, Philipp, Bülow, Robin, Poletti, Sara, Whittle, Sarah, Thomopoulos, Sophia I., van, Steven J. A., Werff, der, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Ho, Tiffany C., Yang, Tony T., Basgoze, Zeynep, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (n=5,356) to provide a generalizable ML classification benchmark of major depressive disorder (MDD). Using brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD vs healthy controls (HC) with around 62% balanced accuracy, but when harmonizing the data using ComBat balanced accuracy dropped to approximately 52%. Similar results were observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may achieve more encouraging prospects., Comment: main document 37 pages; supplementary material 24 pages

Published
2022

10. Recalibrating single-study effect sizes using hierarchical Bayesian models.

Author

Morales, Angelica, London, Edythe, Lorenzetti, Valentina, Luijten, Maartje, Martin-Santos, Rocio, Momenan, Reza, Paulus, Martin, Schmaal, Lianne, Sinha, Rajita, Solowij, Nadia, Stein, Dan, Stein, Elliot, Uhlmann, Anne, van Holst, Ruth, Veltman, Dick, Wiers, Reinout, Yücel, Murat, Zhang, Sheng, Conrod, Patricia, Mackey, Scott, Garavan, Hugh, Cao, Zhipeng, McCabe, Matthew, Callas, Peter, Cupertino, Renata, Ottino-González, Jonatan, Murphy, Alistair, Pancholi, Devarshi, Schwab, Nathan, Catherine, Orr, Hutchison, Kent, Cousijn, Janna, Dagher, Alain, Foxe, John, Goudriaan, Anna, Hester, Robert, Li, Chiang-Shan, and Thompson, Wesley

Subjects
case-control differences, effect size recalibration, hierarchical Bayesian model, inflated effect size, small sample size, substance dependence
Abstract

INTRODUCTION: There are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance. METHODS: We estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method. RESULTS: The results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohens d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = -0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments. DISCUSSION: Our findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.

Published
2023

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

Author

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

Subjects
Good Health and Well Being, Adolescent, Adult, Aged, Aged, 80 and over, Case-Control Studies, Child, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neuroimaging, Stress Disorders, Post-Traumatic, Young Adult, Data Harmonization, Scanner Effects, Site Effects, Cortical Thickness, ComBat, ComBat-GAM, Linear Mixed-Effects Model, General Additive Model, PTSD, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery
Abstract

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

Published
2022

14. Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning

Author

Bruin, Willem B., Zhutovsky, Paul, van Wingen, Guido A., Bas-Hoogendam, Janna Marie, Groenewold, Nynke A., Hilbert, Kevin, Winkler, Anderson M., Zugman, Andre, Agosta, Federica, Åhs, Fredrik, Andreescu, Carmen, Antonacci, Chase, Asami, Takeshi, Assaf, Michal, Barber, Jacques P., Bauer, Jochen, Bavdekar, Shreya Y., Beesdo-Baum, Katja, Benedetti, Francesco, Bernstein, Rachel, Björkstrand, Johannes, Blair, Robert J., Blair, Karina S., Blanco-Hinojo, Laura, Böhnlein, Joscha, Brambilla, Paolo, Bressan, Rodrigo A., Breuer, Fabian, Cano, Marta, Canu, Elisa, Cardinale, Elise M., Cardoner, Narcís, Cividini, Camilla, Cremers, Henk, Dannlowski, Udo, Diefenbach, Gretchen J., Domschke, Katharina, Doruyter, Alexander G. G., Dresler, Thomas, Erhardt, Angelika, Filippi, Massimo, Fonzo, Gregory A., Freitag, Gabrielle F., Furmark, Tomas, Ge, Tian, Gerber, Andrew J., Gosnell, Savannah N., Grabe, Hans J., Grotegerd, Dominik, Gur, Ruben C., Gur, Raquel E., Hamm, Alfons O., Han, Laura K. M., Harper, Jennifer C., Harrewijn, Anita, Heeren, Alexandre, Hofmann, David, Jackowski, Andrea P., Jahanshad, Neda, Jett, Laura, Kaczkurkin, Antonia N., Khosravi, Parmis, Kingsley, Ellen N., Kircher, Tilo, Kostic, Milutin, Larsen, Bart, Lee, Sang-Hyuk, Leehr, Elisabeth J., Leibenluft, Ellen, Lochner, Christine, Lui, Su, Maggioni, Eleonora, Manfro, Gisele G., Månsson, Kristoffer N. T., Marino, Claire E., Meeten, Frances, Milrod, Barbara, Jovanovic, Ana Munjiza, Mwangi, Benson, Myers, Michael J., Neufang, Susanne, Nielsen, Jared A., Ohrmann, Patricia A., Ottaviani, Cristina, Paulus, Martin P., Perino, Michael T., Phan, K. Luan, Poletti, Sara, Porta-Casteràs, Daniel, Pujol, Jesus, Reinecke, Andrea, Ringlein, Grace V., Rjabtsenkov, Pavel, Roelofs, Karin, Salas, Ramiro, Salum, Giovanni A., Satterthwaite, Theodore D., Schrammen, Elisabeth, Sindermann, Lisa, Smoller, Jordan W., Soares, Jair C., Stark, Rudolf, Stein, Frederike, Straube, Thomas, Straube, Benjamin, Strawn, Jeffrey R., Suarez-Jimenez, Benjamin, Sylvester, Chad M., Talati, Ardesheer, Thomopoulos, Sophia I., Tükel, Raşit, van Nieuwenhuizen, Helena, Werwath, Kathryn, Wittfeld, Katharina, Wright, Barry, Wu, Mon-Ju, Yang, Yunbo, Zilverstand, Anna, Zwanzger, Peter, Blackford, Jennifer U., Avery, Suzanne N., Clauss, Jacqueline A., Lueken, Ulrike, Thompson, Paul M., Pine, Daniel S., Stein, Dan J., van der Wee, Nic J. A., Veltman, Dick J., and Aghajani, Moji

Published
2024
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15. Multiscale neural gradients reflect transdiagnostic effects of major psychiatric conditions on cortical morphology

Author

Park, Bo-yong, Kebets, Valeria, Larivière, Sara, Hettwer, Meike D, Paquola, Casey, van Rooij, Daan, Buitelaar, Jan, Franke, Barbara, Hoogman, Martine, Schmaal, Lianne, Veltman, Dick J, van den Heuvel, Odile A, Stein, Dan J, Andreassen, Ole A, Ching, Christopher RK, Turner, Jessica A, van Erp, Theo GM, Evans, Alan C, Dagher, Alain, Thomopoulos, Sophia I, Thompson, Paul M, Valk, Sofie L, Kirschner, Matthias, and Bernhardt, Boris C

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Depression, Neurosciences, Behavioral and Social Science, Serious Mental Illness, Mental Health, Brain Disorders, Schizophrenia, 2.1 Biological and endogenous factors, Aetiology, Neurological, Mental health, Good Health and Well Being, Autism Spectrum Disorder, Connectome, Dopamine, Humans, Neural Pathways, Serotonin, Biological sciences, Biomedical and clinical sciences
Abstract

It is increasingly recognized that multiple psychiatric conditions are underpinned by shared neural pathways, affecting similar brain systems. Here, we carried out a multiscale neural contextualization of shared alterations of cortical morphology across six major psychiatric conditions (autism spectrum disorder, attention deficit/hyperactivity disorder, major depression disorder, obsessive-compulsive disorder, bipolar disorder, and schizophrenia). Our framework cross-referenced shared morphological anomalies with respect to cortical myeloarchitecture and cytoarchitecture, as well as connectome and neurotransmitter organization. Pooling disease-related effects on MRI-based cortical thickness measures across six ENIGMA working groups, including a total of 28,546 participants (12,876 patients and 15,670 controls), we identified a cortex-wide dimension of morphological changes that described a sensory-fugal pattern, with paralimbic regions showing the most consistent alterations across conditions. The shared disease dimension was closely related to cortical gradients of microstructure as well as neurotransmitter axes, specifically cortex-wide variations in serotonin and dopamine. Multiple sensitivity analyses confirmed robustness with respect to slight variations in analytical choices. Our findings embed shared effects of common psychiatric conditions on brain structure in multiple scales of brain organization, and may provide insights into neural mechanisms of transdiagnostic vulnerability.

Published
2022

16. Cross disorder comparisons of brain structure in schizophrenia, bipolar disorder, major depressive disorder, and 22q11.2 deletion syndrome: A review of ENIGMA findings

Author

Cheon, Eun‐Jin, Bearden, Carrie E, Sun, Daqiang, Ching, Christopher RK, Andreassen, Ole A, Schmaal, Lianne, Veltman, Dick J, Thomopoulos, Sophia I, Kochunov, Peter, Jahanshad, Neda, Thompson, Paul M, Turner, Jessica A, and van Erp, Theo GM

Subjects
Depression, Neurosciences, Schizophrenia, Genetics, Clinical Research, Biomedical Imaging, Serious Mental Illness, Brain Disorders, Bipolar Disorder, Mental Health, Aetiology, 2.3 Psychological, social and economic factors, 2.1 Biological and endogenous factors, Mental health, Brain, Depressive Disorder, Major, DiGeorge Syndrome, Diffusion Tensor Imaging, Humans, Magnetic Resonance Imaging, bipolar disorder, ENIGMA, major depressive disorder, schizophrenia, velocardiofacial, Clinical Sciences, Cognitive Sciences
Abstract

This review compares the main brain abnormalities in schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and 22q11.2 Deletion Syndrome (22q11DS) determined by ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium investigations. We obtained ranked effect sizes for subcortical volumes, regional cortical thickness, cortical surface area, and diffusion tensor imaging abnormalities, comparing each of these disorders relative to healthy controls. In addition, the studies report on significant associations between brain imaging metrics and disorder-related factors such as symptom severity and treatments. Visual comparison of effect size profiles shows that effect sizes are generally in the same direction and scale in severity with the disorders (in the order SZ > BD > MDD). The effect sizes for 22q11DS, a rare genetic syndrome that increases the risk for psychiatric disorders, appear to be much larger than for either of the complex psychiatric disorders. This is consistent with the idea of generally larger effects on the brain of rare compared to common genetic variants. Cortical thickness and surface area effect sizes for 22q11DS with psychosis compared to 22q11DS without psychosis are more similar to those of SZ and BD than those of MDD; a pattern not observed for subcortical brain structures and fractional anisotropy effect sizes. The observed similarities in effect size profiles for cortical measures across the psychiatric disorders mimic those observed for shared genetic variance between these disorders reported based on family and genetic studies and are consistent with shared genetic risk for SZ and BD and structural brain phenotypes.

Published
2022

17. Genome-Wide Association Study Points to Novel Locus for Gilles de la Tourette Syndrome

Author

Barr, Cathy L., Batterson, James R., Berlin, Cheston, Budman, Cathy L., Coppola, Giovanni, Cox, Nancy J., Darrow, Sabrina, Dion, Yves, Freimer, Nelson B., Grados, Marco A., Greenberg, Erica, Hirschtritt, Matthew E., Huang, Alden Y., Illmann, Cornelia, King, Robert A., Kurlan, Roger, Leckman, James F., Lyon, Gholson J., Malaty, Irene A., McMahon, William M., Neale, Benjamin M., Okun, Michael S., Osiecki, Lisa, Robertson, Mary M., Rouleau, Guy A., Sandor, Paul, Singer, Harvey S., Smit, Jan H., Sul, Jae Hoon, Androutsos, Christos, Basha, Entela, Farkas, Luca, Fichna, Jakub, Janik, Piotr, Kapisyzi, Mira, Karagiannidis, Iordanis, Koumoula, Anastasia, Nagy, Peter, Puchala, Joanna, Szejko, Natalia, Szymanska, Urszula, Tsironi, Vaia, Apter, Alan, Ball, Juliane, Bodmer, Benjamin, Bognar, Emese, Buse, Judith, Vela, Marta Correa, Fremer, Carolin, Garcia-Delgar, Blanca, Gulisano, Mariangela, Hagen, Annelieke, Hagstrøm, Julie, Madruga-Garrido, Marcos, Pellico, Alessandra, Ruhrman, Daphna, Schnell, Jaana, Silvestri, Paola Rosaria, Skov, Liselotte, Steinberg, Tamar, Gloor, Friederike Tagwerker, Turner, Victoria L., Weidinger, Elif, Alexander, John, Aranyi, Tamas, Buisman, Wim R., Buitelaar, Jan K., Driessen, Nicole, Drineas, Petros, Fan, Siyan, Forde, Natalie J., Gerasch, Sarah, van den Heuvel, Odile A., Jespersgaard, Cathrine, Kanaan, Ahmad S., Möller, Harald E., Nawaz, Muhammad S., Nespoli, Ester, Pagliaroli, Luca, Poelmans, Geert, Pouwels, Petra J.W., Rizzo, Francesca, Veltman, Dick J., van der Werf, Ysbrand D., Widomska, Joanna, Zilhäo, Nuno R., Brown, Lawrence W., Cheon, Keun-Ah, Coffey, Barbara J., Fernandez, Thomas V., Gilbert, Donald L., Hong, Hyun Ju, Ibanez-Gomez, Laura, Kim, Eun-Joo, Kim, Young Key, Kim, Young-Shin, Koh, Yun-Joo, Kook, Sodahm, Kuperman, Samuel, Leventhal, Bennett L., Maras, Athanasios, Murphy, Tara L., Shin, Eun-Young, Song, Dong-Ho, Song, Jungeun, State, Matthew W., Visscher, Frank, Wang, Sheng, Zinner, Samuel H., Tsetsos, Fotis, Topaloudi, Apostolia, Jain, Pritesh, Yang, Zhiyu, Yu, Dongmei, Kolovos, Petros, Tumer, Zeynep, Rizzo, Renata, Hartmann, Andreas, Depienne, Christel, Worbe, Yulia, Müller-Vahl, Kirsten R., Cath, Danielle C., Boomsma, Dorret I., Wolanczyk, Tomasz, Zekanowski, Cezary, Barta, Csaba, Nemoda, Zsofia, Tarnok, Zsanett, Padmanabhuni, Shanmukha S., Buxbaum, Joseph D., Grice, Dorothy, Glennon, Jeffrey, Stefansson, Hreinn, Hengerer, Bastian, Yannaki, Evangelia, Stamatoyannopoulos, John A., Benaroya-Milshtein, Noa, Cardona, Francesco, Hedderly, Tammy, Heyman, Isobel, Huyser, Chaim, Mir, Pablo, Morer, Astrid, Mueller, Norbert, Munchau, Alexander, Plessen, Kerstin J., Porcelli, Cesare, Roessner, Veit, Walitza, Susanne, Schrag, Anette, Martino, Davide, Tischfield, Jay A., Heiman, Gary A., Willsey, A. Jeremy, Dietrich, Andrea, Davis, Lea K., Crowley, James J., Mathews, Carol A., Scharf, Jeremiah M., Georgitsi, Marianthi, Hoekstra, Pieter J., and Paschou, Peristera

Published
2024
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18. When a test is more than just a test: Findings from patient interviews and survey in the trial of a technology to measure antidepressant medication response (the PReDicT Trial)

Author

Brown, Susan, Ploeger, Cornelia, Guo, Boliang, Petersen, Juliana J., Beckenstrom, Amy C., Browning, Michael, Dawson, Gerard R., Deckert, Jürgen, Dias, Rebecca, Dourish, Colin T., Gorwood, Philip, Kingslake, Jonathan, Menke, Andreas, Sola, Victor Perez, Reif, Andreas, Ruhe, Henricus, Simon, Judit, Stäblein, Michael, van Schaik, Anneke, Veltman, Dick J., and Morriss, Richard

Published
2024
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20. ENIGMA-DTI: Translating reproducible white matter deficits into personalized vulnerability metrics in cross-diagnostic psychiatric research.

Author

Kochunov, Peter, Hong, L, Dennis, Emily, Morey, Rajendra, Tate, David, Wilde, Elisabeth, Logue, Mark, Kelly, Sinead, Donohoe, Gary, Favre, Pauline, Houenou, Josselin, Ching, Christopher, Holleran, Laurena, Andreassen, Ole, van Velzen, Laura, Schmaal, Lianne, Villalón-Reina, Julio, Piras, Fabrizio, Spalletta, Gianfranco, van den Heuvel, Odile, Veltman, Dick, Stein, Dan, Ryan, Meghann, Tan, Yunlong, Turner, Jessica, Haddad, Liz, Nir, Talia, Glahn, David, Thompson, Paul, Jahanshad, Neda, Bearden, Carrie, and Van Erp, Theodorus

Subjects
DTI, ENIGMA, RVI, big data, cross-disorder, white matter deficit patterns, Biomedical Research, Diffusion Tensor Imaging, Humans, Mental Disorders, Multicenter Studies as Topic, Psychiatry, White Matter
Abstract

The ENIGMA-DTI (diffusion tensor imaging) workgroup supports analyses that examine the effects of psychiatric, neurological, and developmental disorders on the white matter pathways of the human brain, as well as the effects of normal variation and its genetic associations. The seven ENIGMA disorder-oriented working groups used the ENIGMA-DTI workflow to derive patterns of deficits using coherent and coordinated analyses that model the disease effects across cohorts worldwide. This yielded the largest studies detailing patterns of white matter deficits in schizophrenia spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), traumatic brain injury (TBI), and 22q11 deletion syndrome. These deficit patterns are informative of the underlying neurobiology and reproducible in independent cohorts. We reviewed these findings, demonstrated their reproducibility in independent cohorts, and compared the deficit patterns across illnesses. We discussed translating ENIGMA-defined deficit patterns on the level of individual subjects using a metric called the regional vulnerability index (RVI), a correlation of an individuals brain metrics with the expected pattern for a disorder. We discussed the similarity in white matter deficit patterns among SSD, BD, MDD, and OCD and provided a rationale for using this index in cross-diagnostic neuropsychiatric research. We also discussed the difference in deficit patterns between idiopathic schizophrenia and 22q11 deletion syndrome, which is used as a developmental and genetic model of schizophrenia. Together, these findings highlight the importance of collaborative large-scale research to provide robust and reproducible effects that offer insights into individual vulnerability and cross-diagnosis features.

Published
2022

21. White matter microstructure differences in individuals with dependence on cocaine, methamphetamine, and nicotine: Findings from the ENIGMA-Addiction working group

Author

Ottino-González, Jonatan, Uhlmann, Anne, Hahn, Sage, Cao, Zhipeng, Cupertino, Renata B, Schwab, Nathan, Allgaier, Nicholas, Alia-Klein, Nelly, Ekhtiari, Hamed, Fouche, Jean-Paul, Goldstein, Rita Z, Li, Chiang-Shan R, Lochner, Christine, London, Edythe D, Luijten, Maartje, Masjoodi, Sadegh, Momenan, Reza, Oghabian, Mohammad Ali, Roos, Annerine, Stein, Dan J, Stein, Elliot A, Veltman, Dick J, Verdejo-García, Antonio, Zhang, Sheng, Zhao, Min, Zhong, Na, Jahanshad, Neda, Thompson, Paul M, Conrod, Patricia, Mackey, Scott, and Garavan, Hugh

Subjects
Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Brain Disorders, Substance Misuse, Drug Abuse (NIDA only), Clinical Research, Methamphetamine, Neurosciences, Mental health, Good Health and Well Being, Cocaine, Diffusion Tensor Imaging, Humans, Nicotine, White Matter, Addiction, DTI, FA, Myelin, Machine learning, Medical and Health Sciences, Psychology and Cognitive Sciences, Substance Abuse, Biochemistry and cell biology, Pharmacology and pharmaceutical sciences, Epidemiology
Abstract

BackgroundNicotine and illicit stimulants are very addictive substances. Although associations between grey matter and dependence on stimulants have been frequently reported, white matter correlates have received less attention.MethodsEleven international sites ascribed to the ENIGMA-Addiction consortium contributed data from individuals with dependence on cocaine (n = 147), methamphetamine (n = 132) and nicotine (n = 189), as well as non-dependent controls (n = 333). We compared the fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) of 20 bilateral tracts. Also, we compared the performance of various machine learning algorithms in deriving brain-based classifications on stimulant dependence.ResultsThe cocaine and methamphetamine groups had lower regional FA and higher RD in several association, commissural, and projection white matter tracts. The methamphetamine dependent group additionally showed lower regional AD. The nicotine group had lower FA and higher RD limited to the anterior limb of the internal capsule. The best performing machine learning algorithm was the support vector machine (SVM). The SVM successfully classified individuals with dependence on cocaine (AUC = 0.70, p

Published
2022

22. 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, Unn K, Gurholt, Tiril P, Nerland, Stener, Elvsåshagen, Torbjørn, Akudjedu, Theophilus N, Alda, Martin, Alnæs, Dag, Alonso‐Lana, Silvia, Bauer, Jochen, Baune, Bernhard T, Benedetti, Francesco, Berk, Michael, Bettella, Francesco, Bøen, Erlend, Bonnín, Caterina M, Brambilla, Paolo, Canales‐Rodríguez, Erick J, Cannon, Dara M, Caseras, Xavier, Dandash, Orwa, Dannlowski, Udo, Delvecchio, Giuseppe, Díaz‐Zuluaga, Ana M, Erp, Theo GM, Fatjó‐Vilas, Mar, Foley, Sonya F, Förster, Katharina, Fullerton, Janice M, Goikolea, José M, Grotegerd, Dominik, Gruber, Oliver, Haarman, Bartholomeus CM, Haatveit, Beathe, Hajek, Tomas, Hallahan, Brian, Harris, Mathew, Hawkins, Emma L, Howells, Fleur M, Hülsmann, Carina, Jahanshad, Neda, Jørgensen, Kjetil N, Kircher, Tilo, Krämer, Bernd, Krug, Axel, Kuplicki, Rayus, Lagerberg, Trine V, Lancaster, Thomas M, Lenroot, Rhoshel K, Lonning, Vera, López‐Jaramillo, Carlos, Malt, Ulrik F, McDonald, Colm, McIntosh, Andrew M, McPhilemy, Genevieve, Meer, Dennis, Melle, Ingrid, Melloni, Elisa MT, Mitchell, Philip B, Nabulsi, Leila, Nenadić, Igor, Oertel, Viola, Oldani, Lucio, Opel, Nils, Otaduy, Maria CG, Overs, Bronwyn J, Pineda‐Zapata, Julian A, Pomarol‐Clotet, Edith, Radua, Joaquim, Rauer, Lisa, Redlich, Ronny, Repple, Jonathan, Rive, Maria M, Roberts, Gloria, Ruhe, Henricus G, Salminen, Lauren E, Salvador, Raymond, Sarró, Salvador, Savitz, Jonathan, Schene, Aart H, Sim, Kang, Soeiro‐de‐Souza, Marcio G, Stäblein, Michael, Stein, Dan J, Stein, Frederike, Tamnes, Christian K, Temmingh, Henk S, Thomopoulos, Sophia I, Veltman, Dick J, Vieta, Eduard, Waltemate, Lena, Westlye, Lars T, Whalley, Heather C, Sämann, Philipp G, Thompson, Paul M, Ching, Christopher RK, Andreassen, Ole A, Agartz, Ingrid, and Group, ENIGMA Bipolar Disorder Working

Subjects
Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Brain Disorders, Mental Health, Serious Mental Illness, Neurosciences, Biomedical Imaging, Bipolar Disorder, Mental health, Genetics, Hippocampus, Humans, Magnetic Resonance Imaging, Neuroimaging, ENIGMA Bipolar Disorder Working Group, bipolar disorder subtype, hippocampus, large-scale, lithium, psychosis, structural brain MRI, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
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.

Published
2022

23. Local molecular and global connectomic contributions to cross-disorder cortical abnormalities

Author

Hansen, Justine Y, Shafiei, Golia, Vogel, Jacob W, Smart, Kelly, Bearden, Carrie E, Hoogman, Martine, Franke, Barbara, van Rooij, Daan, Buitelaar, Jan, McDonald, Carrie R, Sisodiya, Sanjay M, Schmaal, Lianne, Veltman, Dick J, van den Heuvel, Odile A, Stein, Dan J, van Erp, Theo GM, Ching, Christopher RK, Andreassen, Ole A, Hajek, Tomas, Opel, Nils, Modinos, Gemma, Aleman, André, van der Werf, Ysbrand, Jahanshad, Neda, Thomopoulos, Sophia I, Thompson, Paul M, Carson, Richard E, Dagher, Alain, and Misic, Bratislav

Subjects
Neurosciences, Pediatric, Brain Disorders, Mental Health, Aetiology, Underpinning research, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Mental health, Neurological, Brain, Brain Diseases, Connectome, Humans, Magnetic Resonance Imaging, Neural Pathways
Abstract

Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21,000 participants and N = 26,000 controls, collected using a harmonised processing protocol. We systematically compare cortical maps to multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, and myelination (molecular vulnerability), as well as global connectomic measures including number of connections, centrality, and connection diversity (connectomic vulnerability). We find a relationship between molecular vulnerability and white-matter architecture that drives cortical disorder profiles. Local attributes, particularly neurotransmitter receptor profiles, constitute the best predictors of both disorder-specific cortical morphology and cross-disorder similarity. Finally, we find that cross-disorder abnormalities are consistently subtended by a small subset of network epicentres in bilateral sensory-motor, inferior temporal lobe, precuneus, and superior parietal cortex. Collectively, our results highlight how local molecular attributes and global connectivity jointly shape cross-disorder cortical abnormalities.

Published
2022

24. Predicting alcohol dependence from multi‐site brain structural measures

Author

Hahn, Sage, Mackey, Scott, Cousijn, Janna, Foxe, John J, Heinz, Andreas, Hester, Robert, Hutchinson, Kent, Kiefer, Falk, Korucuoglu, Ozlem, Lett, Tristram, Li, Chiang‐Shan R, London, Edythe, Lorenzetti, Valentina, Maartje, Luijten, Momenan, Reza, Orr, Catherine, Paulus, Martin, Schmaal, Lianne, Sinha, Rajita, Sjoerds, Zsuzsika, Stein, Dan J, Stein, Elliot, Holst, Ruth J, Veltman, Dick, Walter, Henrik, Wiers, Reinout W, Yucel, Murat, Thompson, Paul M, Conrod, Patricia, Allgaier, Nicholas, and Garavan, Hugh

Subjects
Biological Psychology, Psychology, Brain Disorders, Neurosciences, Substance Misuse, Alcoholism, Alcohol Use and Health, Neurological, Good Health and Well Being, Alcoholism, Cerebral Cortex, Humans, Machine Learning, Magnetic Resonance Imaging, Multicenter Studies as Topic, Neuroimaging, Putamen, Reproducibility of Results, addiction, alcohol dependence, genetic algorithm, machine learning, multi-site, prediction, structural MRI, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

To identify neuroimaging biomarkers of alcohol dependence (AD) from structural magnetic resonance imaging, it may be useful to develop classification models that are explicitly generalizable to unseen sites and populations. This problem was explored in a mega-analysis of previously published datasets from 2,034 AD and comparison participants spanning 27 sites curated by the ENIGMA Addiction Working Group. Data were grouped into a training set used for internal validation including 1,652 participants (692 AD, 24 sites), and a test set used for external validation with 382 participants (146 AD, 3 sites). An exploratory data analysis was first conducted, followed by an evolutionary search based feature selection to site generalizable and high performing subsets of brain measurements. Exploratory data analysis revealed that inclusion of case- and control-only sites led to the inadvertent learning of site-effects. Cross validation methods that do not properly account for site can drastically overestimate results. Evolutionary-based feature selection leveraging leave-one-site-out cross-validation, to combat unintentional learning, identified cortical thickness in the left superior frontal gyrus and right lateral orbitofrontal cortex, cortical surface area in the right transverse temporal gyrus, and left putamen volume as final features. Ridge regression restricted to these features yielded a test-set area under the receiver operating characteristic curve of 0.768. These findings evaluate strategies for handling multi-site data with varied underlying class distributions and identify potential biomarkers for individuals with current AD.

Published
2022

25. An overview of the first 5 years of the ENIGMA obsessive–compulsive disorder working group: The power of worldwide collaboration

Author

van den Heuvel, Odile A, Boedhoe, Premika SW, Bertolin, Sara, Bruin, Willem B, Francks, Clyde, Ivanov, Iliyan, Jahanshad, Neda, Kong, Xiang‐Zhen, Kwon, Jun Soo, O'Neill, Joseph, Paus, Tomas, Patel, Yash, Piras, Fabrizio, Schmaal, Lianne, Soriano‐Mas, Carles, Spalletta, Gianfranco, van Wingen, Guido A, Yun, Je‐Yeon, Vriend, Chris, Simpson, H Blair, van Rooij, Daan, Hoexter, Marcelo Q, Hoogman, Martine, Buitelaar, Jan K, Arnold, Paul, Beucke, Jan C, Benedetti, Francesco, Bollettini, Irene, Bose, Anushree, Brennan, Brian P, De Nadai, Alessandro S, Fitzgerald, Kate, Gruner, Patricia, Grünblatt, Edna, Hirano, Yoshiyuki, Huyser, Chaim, James, Anthony, Koch, Kathrin, Kvale, Gerd, Lazaro, Luisa, Lochner, Christine, Marsh, Rachel, Mataix‐Cols, David, Morgado, Pedro, Nakamae, Takashi, Nakao, Tomohiro, Narayanaswamy, Janardhanan C, Nurmi, Erika, Pittenger, Christopher, Reddy, YC Janardhan, Sato, João R, Soreni, Noam, Stewart, S Evelyn, Taylor, Stephan F, Tolin, David, Thomopoulos, Sophia I, Veltman, Dick J, Venkatasubramanian, Ganesan, Walitza, Susanne, Wang, Zhen, Thompson, Paul M, Stein, Dan J, Abe, Yoshinari, Alonso, Pino, Assogna, Francesca, Banaj, Nerisa, Batistuzzo, Marcelo C, Brem, Silvia, Ciullo, Valentina, Feusner, Jamie, Martínez‐Zalacaín, Ignacio, Menchón, José M, Miguel, Euripedes C, Piacentini, John, Piras, Federica, Sakai, Yuki, Wolters, Lidewij, and Yamada, Kei

Subjects
Biological Psychology, Psychology, Brain Disorders, Clinical Research, Serious Mental Illness, Pediatric, Neurosciences, Mental Health, Mental health, Neurological, Cerebral Cortex, Humans, Machine Learning, Multicenter Studies as Topic, Neuroimaging, Obsessive-Compulsive Disorder, cortical thickness, ENIGMA, mega-analysis, meta-analysis, MRI, obsessive-compulsive disorder, surface area, volume, ENIGMA-OCD working group, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

Neuroimaging has played an important part in advancing our understanding of the neurobiology of obsessive-compulsive disorder (OCD). At the same time, neuroimaging studies of OCD have had notable limitations, including reliance on relatively small samples. International collaborative efforts to increase statistical power by combining samples from across sites have been bolstered by the ENIGMA consortium; this provides specific technical expertise for conducting multi-site analyses, as well as access to a collaborative community of neuroimaging scientists. In this article, we outline the background to, development of, and initial findings from ENIGMA's OCD working group, which currently consists of 47 samples from 34 institutes in 15 countries on 5 continents, with a total sample of 2,323 OCD patients and 2,325 healthy controls. Initial work has focused on studies of cortical thickness and subcortical volumes, structural connectivity, and brain lateralization in children, adolescents and adults with OCD, also including the study on the commonalities and distinctions across different neurodevelopment disorders. Additional work is ongoing, employing machine learning techniques. Findings to date have contributed to the development of neurobiological models of OCD, have provided an important model of global scientific collaboration, and have had a number of clinical implications. Importantly, our work has shed new light on questions about whether structural and functional alterations found in OCD reflect neurodevelopmental changes, effects of the disease process, or medication impacts. We conclude with a summary of ongoing work by ENIGMA-OCD, and a consideration of future directions for neuroimaging research on OCD within and beyond ENIGMA.

Published
2022

26. Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation

Author

Agartz, Ingrid, Asherson, Philip, Ayesa-Arriola, Rosa, Banaj, Nerisa, Banaschewski, Tobias, Baumeister, Sarah, Bertolino, Alessandro, Borgwardt, Stefan, Bourque, Josiane, Brandeis, Daniel, Breier, Alan, Buitelaar, Jan K, Cannon, Dara M, Cervenka, Simon, Conrod, Patricia J, Crespo-Facorro, Benedicto, Davey, Christopher G, de Haan, Lieuwe, de Zubicaray, Greig I, Di Giorgio, Annabella, Frodl, Thomas, Gruner, Patricia, Gur, Raquel E, Gur, Ruben C, Harrison, Ben J, Hatton, Sean N, Hickie, Ian, Howells, Fleur M, Huyser, Chaim, Jernigan, Terry L, Jiang, Jiyang, Joska, John A, Kahn, René S, Kalnin, Andrew J, Kochan, Nicole A, Koops, Sanne, Kuntsi, Jonna, Lagopoulos, Jim, Lazaro, Luisa, Lebedeva, Irina S, Lochner, Christine, Martin, Nicholas G, Mazoyer, Bernard, McDonald, Brenna C, McDonald, Colm, McMahon, Katie L, Medland, Sarah, Modabbernia, Amirhossein, Mwangi, Benson, Nakao, Tomohiro, Nyberg, Lars, Piras, Fabrizio, Portella, Maria J, Qiu, Jiang, Roffman, Joshua L, Sachdev, Perminder S, Sanford, Nicole, Satterthwaite, Theodore D, Saykin, Andrew J, Sellgren, Carl M, Sim, Kang, Smoller, Jordan W, Soares, Jair C, Sommer, Iris E, Spalletta, Gianfranco, Stein, Dan J, Thomopoulos, Sophia I, Tomyshev, Alexander S, Tordesillas-Gutiérrez, Diana, Trollor, Julian N, van 't Ent, Dennis, van den Heuvel, Odile A, van Erp, Theo GM, van Haren, Neeltje EM, Vecchio, Daniela, Veltman, Dick J, Wang, Yang, Weber, Bernd, Wei, Dongtao, Wen, Wei, Westlye, Lars T, Williams, Steven CR, Wright, Margaret J, Wu, Mon-Ju, Yu, Kevin, Ge, Ruiyang, Yu, Yuetong, Qi, Yi Xuan, Fan, Yu-nan, Chen, Shiyu, Gao, Chuntong, Haas, Shalaila S, New, Faye, Boomsma, Dorret I, Brodaty, Henry, Brouwer, Rachel M, Buckner, Randy, Caseras, Xavier, Crivello, Fabrice, Crone, Eveline A, Erk, Susanne, Fisher, Simon E, Franke, Barbara, Glahn, David C, Dannlowski, Udo, Grotegerd, Dominik, Gruber, Oliver, Hulshoff Pol, Hilleke E, Schumann, Gunter, Tamnes, Christian K, Walter, Henrik, Wierenga, Lara M, Jahanshad, Neda, Thompson, Paul M, and Frangou, Sophia

Published
2024
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27. Mapping cortical and subcortical asymmetries in substance dependence: Findings from the ENIGMA Addiction Working Group

Author

Cao, Zhipeng, Ottino‐Gonzalez, Jonatan, Cupertino, Renata B, Schwab, Nathan, Hoke, Colin, Catherine, Orr, Cousijn, Janna, Dagher, Alain, Foxe, John J, Goudriaan, Anna E, Hester, Robert, Hutchison, Kent, Li, Chiang‐Shan R, London, Edythe D, Lorenzetti, Valentina, Luijten, Maartje, Martin‐Santos, Rocio, Momenan, Reza, Paulus, Martin P, Schmaal, Lianne, Sinha, Rajita, Sjoerds, Zsuzsika, Solowij, Nadia, Stein, Dan J, Stein, Elliot A, Uhlmann, Anne, Holst, Ruth J, Veltman, Dick J, Wiers, Reinout W, Yücel, Murat, Zhang, Sheng, Jahanshad, Neda, Thompson, Paul M, Conrod, Patricia, Mackey, Scott, and Garavan, Hugh

Subjects
Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Substance Misuse, Drug Abuse (NIDA only), Neurosciences, Brain Disorders, Mental health, Good Health and Well Being, Adult, Alcoholism, Behavior, Addictive, Brain, Brain Cortical Thickness, Cerebellar Cortex, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neuroimaging, Nucleus Accumbens, Substance-Related Disorders, Tobacco Use Disorder, Young Adult, brain asymmetry, mega-analysis, substance dependence, Medical and Health Sciences, Psychology and Cognitive Sciences, Substance Abuse, Biomedical and clinical sciences, Health sciences
Abstract

Brain asymmetry reflects left-right hemispheric differentiation, which is a quantitative brain phenotype that develops with age and can vary with psychiatric diagnoses. Previous studies have shown that substance dependence is associated with altered brain structure and function. However, it is unknown whether structural brain asymmetries are different in individuals with substance dependence compared with nondependent participants. Here, a mega-analysis was performed using a collection of 22 structural brain MRI datasets from the ENIGMA Addiction Working Group. Structural asymmetries of cortical and subcortical regions were compared between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis (n = 1,796) and nondependent participants (n = 996). Substance-general and substance-specific effects on structural asymmetry were examined using separate models. We found that substance dependence was significantly associated with differences in volume asymmetry of the nucleus accumbens (NAcc; less rightward; Cohen's d = 0.15). This effect was driven by differences from controls in individuals with alcohol dependence (less rightward; Cohen's d = 0.10) and nicotine dependence (less rightward; Cohen's d = 0.11). These findings suggest that disrupted structural asymmetry in the NAcc may be a characteristic of substance dependence.

Published
2021

28. Cortical volume abnormalities in posttraumatic stress disorder: an ENIGMA-psychiatric genomics consortium PTSD workgroup mega-analysis

Author

Wang, Xin, Xie, Hong, Chen, Tian, Cotton, Andrew S, Salminen, Lauren E, Logue, Mark W, Clarke-Rubright, Emily K, Wall, John, Dennis, Emily L, O’Leary, Brian M, Abdallah, Chadi G, Andrew, Elpiniki, Baugh, Lee A, Bomyea, Jessica, Bruce, Steven E, Bryant, Richard, Choi, Kyle, Daniels, Judith K, Davenport, Nicholas D, Davidson, Richard J, DeBellis, Michael, deRoon-Cassini, Terri, Disner, Seth G, Fani, Negar, Fercho, Kelene A, Fitzgerald, Jacklynn, Forster, Gina L, Frijling, Jessie L, Geuze, Elbert, Gomaa, Hassaan, Gordon, Evan M, Grupe, Dan, Harpaz-Rotem, Ilan, Haswell, Courtney C, Herzog, Julia I, Hofmann, David, Hollifield, Michael, Hosseini, Bobak, Hudson, Anna R, Ipser, Jonathan, Jahanshad, Neda, Jovanovic, Tanja, Kaufman, Milissa L, King, Anthony P, Koch, Saskia BJ, Koerte, Inga K, Korgaonkar, Mayuresh S, Krystal, John H, Larson, Christine, Lebois, Lauren AM, Levy, Ifat, Li, Gen, Magnotta, Vincent A, Manthey, Antje, May, Geoffrey, McLaughlin, Katie A, Mueller, Sven C, Nawijn, Laura, Nelson, Steven M, Neria, Yuval, Nitschke, Jack B, Olff, Miranda, Olson, Elizabeth A, Peverill, Matthew, Phan, K Luan, Rashid, Faisal M, Ressler, Kerry, Rosso, Isabelle M, Sambrook, Kelly, Schmahl, Christian, Shenton, Martha E, Sierk, Anika, Simons, Jeffrey S, Simons, Raluca M, Sponheim, Scott R, Stein, Murray B, Stein, Dan J, Stevens, Jennifer S, Straube, Thomas, Suarez-Jimenez, Benjamin, Tamburrino, Marijo, Thomopoulos, Sophia I, van der Wee, Nic JA, van der Werff, Steven JA, van Erp, Theo GM, van Rooij, Sanne JH, van Zuiden, Mirjam, Varkevisser, Tim, Veltman, Dick J, Vermeiren, Robert RJM, Walter, Henrik, Wang, Li, Zhu, Ye, Zhu, Xi, Thompson, Paul M, Morey, Rajendra A, and Liberzon, Israel

Subjects
Clinical Research, Behavioral and Social Science, Mental Health, Post-Traumatic Stress Disorder (PTSD), Neurosciences, Brain Disorders, Mental health, Cerebral Cortex, Genomics, Humans, Magnetic Resonance Imaging, Stress Disorders, Post-Traumatic, Temporal Lobe, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Studies of posttraumatic stress disorder (PTSD) report volume abnormalities in multiple regions of the cerebral cortex. However, findings for many regions, particularly regions outside commonly studied emotion-related prefrontal, insular, and limbic regions, are inconsistent and tentative. Also, few studies address the possibility that PTSD abnormalities may be confounded by comorbid depression. A mega-analysis investigating all cortical regions in a large sample of PTSD and control subjects can potentially provide new insight into these issues. Given this perspective, our group aggregated regional volumes data of 68 cortical regions across both hemispheres from 1379 PTSD patients to 2192 controls without PTSD after data were processed by 32 international laboratories using ENIGMA standardized procedures. We examined whether regional cortical volumes were different in PTSD vs. controls, were associated with posttraumatic stress symptom (PTSS) severity, or were affected by comorbid depression. Volumes of left and right lateral orbitofrontal gyri (LOFG), left superior temporal gyrus, and right insular, lingual and superior parietal gyri were significantly smaller, on average, in PTSD patients than controls (standardized coefficients = -0.111 to -0.068, FDR corrected P values

Published
2021

29. Intrusive Traumatic Re-Experiencing Domain: Functional Connectivity Feature Classification by the ENIGMA PTSD Consortium

Author

Suarez-Jimenez, Benjamin, Lazarov, Amit, Zhu, Xi, Zilcha-Mano, Sigal, Kim, Yoojean, Marino, Claire E., Rjabtsenkov, Pavel, Bavdekar, Shreya Y., Pine, Daniel S., Bar-Haim, Yair, Larson, Christine L., Huggins, Ashley A., Terri deRoon-Cassini, Tomas, Carissa, Fitzgerald, Jacklynn, Kennis, Mitzy, Varkevisser, Tim, Geuze, Elbert, Quidé, Yann, El Hage, Wissam, Wang, Xin, O’Leary, Erin N., Cotton, Andrew S., Xie, Hong, Shih, Chiahao, Disner, Seth G., Davenport, Nicholas D., Sponheim, Scott R., Koch, Saskia B.J., Frijling, Jessie L., Nawijn, Laura, van Zuiden, Mirjam, Olff, Miranda, Veltman, Dick J., Gordon, Evan M., May, Geoffery, Nelson, Steven M., Jia-Richards, Meilin, Neria, Yuval, and Morey, Rajendra A.

Published
2024
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30. Structural brain imaging studies offer clues about the effects of the shared genetic etiology among neuropsychiatric disorders

Author

Radonjić, Nevena V, Hess, Jonathan L, Rovira, Paula, Andreassen, Ole, Buitelaar, Jan K, Ching, Christopher RK, Franke, Barbara, Hoogman, Martine, Jahanshad, Neda, McDonald, Carrie, Schmaal, Lianne, Sisodiya, Sanjay M, Stein, Dan J, van den Heuvel, Odile A, van Erp, Theo GM, van Rooij, Daan, Veltman, Dick J, Thompson, Paul, and Faraone, Stephen V

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Neurosciences, Serious Mental Illness, Genetics, Brain Disorders, Clinical Research, Autism, Intellectual and Developmental Disabilities (IDD), Schizophrenia, Mental Health, Prevention, Aetiology, 2.1 Biological and endogenous factors, Mental health, Attention Deficit Disorder with Hyperactivity, Autism Spectrum Disorder, Brain, Depressive Disorder, Major, Humans, Neuroimaging, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

Genomewide association studies have found significant genetic correlations among many neuropsychiatric disorders. In contrast, we know much less about the degree to which structural brain alterations are similar among disorders and, if so, the degree to which such similarities have a genetic etiology. From the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium, we acquired standardized mean differences (SMDs) in regional brain volume and cortical thickness between cases and controls. We had data on 41 brain regions for: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), epilepsy, major depressive disorder (MDD), obsessive compulsive disorder (OCD), and schizophrenia (SCZ). These data had been derived from 24,360 patients and 37,425 controls. The SMDs were significantly correlated between SCZ and BD, OCD, MDD, and ASD. MDD was positively correlated with BD and OCD. BD was positively correlated with OCD and negatively correlated with ADHD. These pairwise correlations among disorders were correlated with the corresponding pairwise correlations among disorders derived from genomewide association studies (r = 0.494). Our results show substantial similarities in sMRI phenotypes among neuropsychiatric disorders and suggest that these similarities are accounted for, in part, by corresponding similarities in common genetic variant architectures.

Published
2021

31. Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

Author

Zhu, Xi, Kim, Yoojean, Ravid, Orren, He, Xiaofu, Suarez-Jimenez, Benjamin, Zilcha-Mano, Sigal, Lazarov, Amit, Lee, Seonjoo, Abdallah, Chadi G., Angstadt, Michael, Averill, Christopher L., Baird, C. Lexi, Baugh, Lee A., Blackford, Jennifer U., Bomyea, Jessica, Bruce, Steven E., Bryant, Richard A., Cao, Zhihong, Choi, Kyle, Cisler, Josh, Cotton, Andrew S., Daniels, Judith K., Davenport, Nicholas D., Davidson, Richard J., DeBellis, Michael D., Dennis, Emily L., Densmore, Maria, deRoon-Cassini, Terri, Disner, Seth G., Hage, Wissam El, Etkin, Amit, Fani, Negar, Fercho, Kelene A., Fitzgerald, Jacklynn, Forster, Gina L., Frijling, Jessie L., Geuze, Elbert, Gonenc, Atilla, Gordon, Evan M., Gruber, Staci, Grupe, Daniel W, Guenette, Jeffrey P., Haswell, Courtney C., Herringa, Ryan J., Herzog, Julia, Hofmann, David Bernd, Hosseini, Bobak, Hudson, Anna R., Huggins, Ashley A., Ipser, Jonathan C., Jahanshad, Neda, Jia-Richards, Meilin, Jovanovic, Tanja, Kaufman, Milissa L., Kennis, Mitzy, King, Anthony, Kinzel, Philipp, Koch, Saskia B.J., Koerte, Inga K., Koopowitz, Sheri M., Korgaonkar, Mayuresh S., Krystal, John H., Lanius, Ruth, Larson, Christine L., Lebois, Lauren A.M., Li, Gen, Liberzon, Israel, Lu, Guang Ming, Luo, Yifeng, Magnotta, Vincent A., Manthey, Antje, Maron-Katz, Adi, May, Geoffery, McLaughlin, Katie, Mueller, Sven C., Nawijn, Laura, Nelson, Steven M., Neufeld, Richard W.J., Nitschke, Jack B, O'Leary, Erin M., Olatunji, Bunmi O., Olff, Miranda, Peverill, Matthew, Phan, K. Luan, Qi, Rongfeng, Quidé, Yann, Rektor, Ivan, Ressler, Kerry, Riha, Pavel, Ross, Marisa, Rosso, Isabelle M., Salminen, Lauren E., Sambrook, Kelly, Schmahl, Christian, Shenton, Martha E., Sheridan, Margaret, Shih, Chiahao, Sicorello, Maurizio, Sierk, Anika, Simmons, Alan N., Simons, Raluca M., Simons, Jeffrey S., Sponheim, Scott R., Stein, Murray B., Stein, Dan J., Stevens, Jennifer S., Straube, Thomas, Sun, Delin, Théberge, Jean, Thompson, Paul M., Thomopoulos, Sophia I., van der Wee, Nic J.A., van der Werff, Steven J.A., van Erp, Theo G.M., van Rooij, Sanne J.H., van Zuiden, Mirjam, Varkevisser, Tim, Veltman, Dick J., Vermeiren, Robert R.J.M., Walter, Henrik, Wang, Li, Wang, Xin, Weis, Carissa, Winternitz, Sherry, Xie, Hong, Zhu, Ye, Wall, Melanie, Neria, Yuval, and Morey, Rajendra A.

Published
2023
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32. Cortical profiles of numerous psychiatric disorders and normal development share a common pattern

Author

Cao, Zhipeng, Cupertino, Renata B., Ottino-Gonzalez, Jonatan, Murphy, Alistair, Pancholi, Devarshi, Juliano, Anthony, Chaarani, Bader, Albaugh, Matthew, Yuan, Dekang, Schwab, Nathan, Stafford, James, Goudriaan, Anna E., Hutchison, Kent, Li, Chiang-Shan R., Luijten, Maartje, Groefsema, Martine, Momenan, Reza, Schmaal, Lianne, Sinha, Rajita, van Holst, Ruth J., Veltman, Dick J., Wiers, Reinout W., Porjesz, Bernice, Lett, Tristram, Banaschewski, Tobias, Bokde, Arun L. W., Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Gowland, Penny, Heinz, Andreas, Brühl, Rüdiger, Martinot, Jean-Luc, Martinot, Marie-Laure Paillère, Artiges, Eric, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Paus, Tomáš, Poustka, Luise, Hohmann, Sarah, Millenet, Sabina, Fröhner, Juliane H., Robinson, Lauren, Smolka, Michael N., Walter, Henrik, Winterer, Jeanne, Schumann, Gunter, Whelan, Robert, Bhatt, Ravi R., Zhu, Alyssa, Conrod, Patricia, Jahanshad, Neda, Thompson, Paul M., Mackey, Scott, and Garavan, Hugh

Published
2023
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33. Cortical and subcortical brain structure in generalized anxiety disorder: findings from 28 research sites in the ENIGMA-Anxiety Working Group

Author

Harrewijn, Anita, Cardinale, Elise M, Groenewold, Nynke A, Bas-Hoogendam, Janna Marie, Aghajani, Moji, Hilbert, Kevin, Cardoner, Narcis, Porta-Casteràs, Daniel, Gosnell, Savannah, Salas, Ramiro, Jackowski, Andrea P, Pan, Pedro M, Salum, Giovanni A, Blair, Karina S, Blair, James R, Hammoud, Mira Z, Milad, Mohammed R, Burkhouse, Katie L, Phan, K Luan, Schroeder, Heidi K, Strawn, Jeffrey R, Beesdo-Baum, Katja, Jahanshad, Neda, Thomopoulos, Sophia I, Buckner, Randy, Nielsen, Jared A, Smoller, Jordan W, Soares, Jair C, Mwangi, Benson, Wu, Mon-Ju, Zunta-Soares, Giovana B, Assaf, Michal, Diefenbach, Gretchen J, Brambilla, Paolo, Maggioni, Eleonora, Hofmann, David, Straube, Thomas, Andreescu, Carmen, Berta, Rachel, Tamburo, Erica, Price, Rebecca B, Manfro, Gisele G, Agosta, Federica, Canu, Elisa, Cividini, Camilla, Filippi, Massimo, Kostić, Milutin, Munjiza Jovanovic, Ana, Alberton, Bianca AV, Benson, Brenda, Freitag, Gabrielle F, Filippi, Courtney A, Gold, Andrea L, Leibenluft, Ellen, Ringlein, Grace V, Werwath, Kathryn E, Zwiebel, Hannah, Zugman, André, Grabe, Hans J, Van der Auwera, Sandra, Wittfeld, Katharina, Völzke, Henry, Bülow, Robin, Balderston, Nicholas L, Ernst, Monique, Grillon, Christian, Mujica-Parodi, Lilianne R, van Nieuwenhuizen, Helena, Critchley, Hugo D, Makovac, Elena, Mancini, Matteo, Meeten, Frances, Ottaviani, Cristina, Ball, Tali M, Fonzo, Gregory A, Paulus, Martin P, Stein, Murray B, Gur, Raquel E, Gur, Ruben C, Kaczkurkin, Antonia N, Larsen, Bart, Satterthwaite, Theodore D, Harper, Jennifer, Myers, Michael, Perino, Michael T, Sylvester, Chad M, Yu, Qiongru, Lueken, Ulrike, Veltman, Dick J, Thompson, Paul M, Stein, Dan J, Van der Wee, Nic JA, Winkler, Anderson M, and Pine, Daniel S

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Clinical Sciences, Psychology, Neurosciences, Women's Health, Brain Disorders, Behavioral and Social Science, Biomedical Imaging, 4.2 Evaluation of markers and technologies, Mental health, Adult, Anxiety, Anxiety Disorders, Brain, Child, Female, Humans, Magnetic Resonance Imaging, Male, Public Health and Health Services, Clinical sciences, Biological psychology
Abstract

The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5-90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology.

Published
2021

34. Subcortical surface morphometry in substance dependence: An ENIGMA addiction working group study

Author

Chye, Yann, Mackey, Scott, Gutman, Boris A, Ching, Christopher RK, Batalla, Albert, Blaine, Sara, Brooks, Samantha, Caparelli, Elisabeth C, Cousijn, Janna, Dagher, Alain, Foxe, John J, Goudriaan, Anna E, Hester, Robert, Hutchison, Kent, Jahanshad, Neda, Kaag, Anne M, Korucuoglu, Ozlem, Li, Chiang‐Shan R, London, Edythe D, Lorenzetti, Valentina, Luijten, Maartje, Martin‐Santos, Rocio, Meda, Shashwath A, Momenan, Reza, Morales, Angelica, Orr, Catherine, Paulus, Martin P, Pearlson, Godfrey, Reneman, Liesbeth, Schmaal, Lianne, Sinha, Rajita, Solowij, Nadia, Stein, Dan J, Stein, Elliot A, Tang, Deborah, Uhlmann, Anne, Holst, Ruth, Veltman, Dick J, Verdejo‐Garcia, Antonio, Wiers, Reinout W, Yücel, Murat, Thompson, Paul M, Conrod, Patricia, and Garavan, Hugh

Subjects
Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Alcoholism, Alcohol Use and Health, Neurosciences, Drug Abuse (NIDA only), Brain Disorders, Substance Misuse, Mental health, Good Health and Well Being, Adolescent, Adult, Brain, Cannabis, Cocaine, Ethanol, Female, Humans, Magnetic Resonance Imaging, Male, Methamphetamine, Neuroimaging, Nicotine, Substance-Related Disorders, Young Adult, addiction, structural MRI, substance dependence, Medical and Health Sciences, Psychology and Cognitive Sciences, Substance Abuse, Biomedical and clinical sciences, Health sciences
Abstract

While imaging studies have demonstrated volumetric differences in subcortical structures associated with dependence on various abused substances, findings to date have not been wholly consistent. Moreover, most studies have not compared brain morphology across those dependent on different substances of abuse to identify substance-specific and substance-general dependence effects. By pooling large multinational datasets from 33 imaging sites, this study examined subcortical surface morphology in 1628 nondependent controls and 2277 individuals with dependence on alcohol, nicotine, cocaine, methamphetamine, and/or cannabis. Subcortical structures were defined by FreeSurfer segmentation and converted to a mesh surface to extract two vertex-level metrics-the radial distance (RD) of the structure surface from a medial curve and the log of the Jacobian determinant (JD)-that, respectively, describe local thickness and surface area dilation/contraction. Mega-analyses were performed on measures of RD and JD to test for the main effect of substance dependence, controlling for age, sex, intracranial volume, and imaging site. Widespread differences between dependent users and nondependent controls were found across subcortical structures, driven primarily by users dependent on alcohol. Alcohol dependence was associated with localized lower RD and JD across most structures, with the strongest effects in the hippocampus, thalamus, putamen, and amygdala. Meanwhile, nicotine use was associated with greater RD and JD relative to nonsmokers in multiple regions, with the strongest effects in the bilateral hippocampus and right nucleus accumbens. By demonstrating subcortical morphological differences unique to alcohol and nicotine use, rather than dependence across all substances, results suggest substance-specific relationships with subcortical brain structures.

Published
2020

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

Author

Tozzi, Leonardo, Garczarek, Lisa, Janowitz, Deborah, Stein, Dan J, Wittfeld, Katharina, Dobrowolny, Henrik, Lagopoulos, Jim, Hatton, Sean N, Hickie, Ian B, Carballedo, Angela, Brooks, Samantha J, Vuletic, Daniella, Uhlmann, Anne, Veer, Ilya M, Walter, Henrik, Bülow, Robin, Völzke, Henry, Klinger-König, Johanna, Schnell, Knut, Schoepf, Dieter, Grotegerd, Dominik, Opel, Nils, Dannlowski, Udo, Kugel, Harald, Schramm, Elisabeth, Konrad, Carsten, Kircher, Tilo, Jüksel, Dilara, Nenadić, Igor, Krug, Axel, Hahn, Tim, Steinsträter, Olaf, Redlich, Ronny, Zaremba, Dario, Zurowski, Bartosz, Fu, Cynthia HY, Dima, Danai, Cole, James, Grabe, Hans J, Connolly, Colm G, Yang, Tony T, Ho, Tiffany C, LeWinn, Kaja Z, Li, Meng, Groenewold, Nynke A, Salminen, Lauren E, Walter, Martin, Simmons, Alan N, van Erp, Theo GM, Jahanshad, Neda, Baune, Bernhard T, van der Wee, Nic JA, van Tol, Marie-Jose, Penninx, Brenda WJH, Hibar, Derrek P, Thompson, Paul M, Veltman, Dick J, Schmaal, Lianne, and Frodl, Thomas

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Clinical Sciences, Psychology, Pediatric, Neurosciences, Clinical Research, Mental Health, Brain Disorders, Depression, Child Abuse and Neglect Research, Behavioral and Social Science, Violence Research, Aetiology, 2.1 Biological and endogenous factors, Adolescent, Adult, Age Factors, Aged, Aged, 80 and over, Brain Cortical Thickness, Case-Control Studies, Cerebral Cortex, Child, Child Abuse, Cohort Studies, Depressive Disorder, Major, Female, Gyrus Cinguli, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Parietal Lobe, Prefrontal Cortex, Temporal Lobe, Young Adult, Childhood maltreatment, cortical thickness, ENIGMA, major depressive disorder, ‘for the ENIGMA-MDD Consortium’, Public Health and Health Services, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

BackgroundChildhood 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.MethodsWithin 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.ResultsCM 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.ConclusionsSeverity 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

37. Deep Learning for Quality Control of Subcortical Brain 3D Shape Models

Author

Petrov, Dmitry, Kuznetsov, Boris A. Gutman Egor, van Erp, Theo G. M., Turner, Jessica A., Schmaal, Lianne, Veltman, Dick, Wang, Lei, Alpert, Kathryn, Isaev, Dmitry, Zavaliangos-Petropulu, Artemis, Ching, Christopher R. K., Calhoun, Vince, Glahn, David, Satterthwaite, Theodore D., Andreassen, Ole Andreas, Borgwardt, Stefan, Howells, Fleur, Groenewold, Nynke, Voineskos, Aristotle, Radua, Joaquim, Potkin, Steven G., Crespo-Facorro, Benedicto, Tordesillas-Gutierrez, Diana, Shen, Li, Lebedeva, Irina, Spalletta, Gianfranco, Donohoe, Gary, Kochunov, Peter, Rosa, Pedro G. P., James, Anthony, Dannlowski, Udo, Baune, Bernhard T., Aleman, Andre, Gotlib, Ian H., Walter, Henrik, Walter, Martin, Soares, Jair C., Ehrlich, Stefan, Gur, Ruben C., Doan, N. Trung, Agartz, Ingrid, Westlye, Lars T., Harrisberger, Fabienne, Riecher-Rossler, Anita, Uhlmann, Anne, Stein, Dan J., Dickie, Erin W., Pomarol-Clotet, Edith, Fuentes-Claramonte, Paola, Canales-Rodriguez, Erick Jorge, Salvador, Raymond, Huang, Alexander J., Roiz-Santianez, Roberto, Cong, Shan, Tomyshev, Alexander, Piras, Fabrizio, Vecchio, Daniela, Banaj, Nerisa, Ciullo, Valentina, Hong, Elliot, Busatto, Geraldo, Zanetti, Marcus V., Serpa, Mauricio H., Cervenka, Simon, Kelly, Sinead, Grotegerd, Dominik, Sacchet, Matthew D., Veer, Ilya M., Li, Meng, Wu, Mon-Ju, Irungu, Benson, Walton, Esther, and Thompson, Paul M.

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

We present several deep learning models for assessing the morphometric fidelity of deep grey matter region models extracted from brain MRI. We test three different convolutional neural net architectures (VGGNet, ResNet and Inception) over 2D maps of geometric features. Further, we present a novel geometry feature augmentation technique based on a parametric spherical mapping. Finally, we present an approach for model decision visualization, allowing human raters to see the areas of subcortical shapes most likely to be deemed of failing quality by the machine. Our training data is comprised of 5200 subjects from the ENIGMA Schizophrenia MRI cohorts, and our test dataset contains 1500 subjects from the ENIGMA Major Depressive Disorder cohorts. Our final models reduce human rater time by 46-70%. ResNet outperforms VGGNet and Inception for all of our predictive tasks., Comment: Accepted to Shape in Medical Imaging (ShapeMI) workshop at MICCAI 2018. arXiv admin note: substantial text overlap with arXiv:1707.06353

Published
2018

38. Mega-Analysis of Gray Matter Volume in Substance Dependence: General and Substance-Specific Regional Effects.

Author

Mackey, Scott, Allgaier, Nicholas, Chaarani, Bader, Spechler, Philip, Orr, Catherine, Bunn, Janice, Allen, Nicholas B, Alia-Klein, Nelly, Batalla, Albert, Blaine, Sara, Brooks, Samantha, Caparelli, Elisabeth, Chye, Yann Ying, Cousijn, Janna, Dagher, Alain, Desrivieres, Sylvane, Feldstein-Ewing, Sarah, Foxe, John J, Goldstein, Rita Z, Goudriaan, Anna E, Heitzeg, Mary M, Hester, Robert, Hutchison, Kent, Korucuoglu, Ozlem, Li, Chiang-Shan R, London, Edythe, Lorenzetti, Valentina, Luijten, Maartje, Martin-Santos, Rocio, May, April, Momenan, Reza, Morales, Angelica, Paulus, Martin P, Pearlson, Godfrey, Rousseau, Marc-Etienne, Salmeron, Betty Jo, Schluter, Renée, Schmaal, Lianne, Schumann, Gunter, Sjoerds, Zsuzsika, Stein, Dan J, Stein, Elliot A, Sinha, Rajita, Solowij, Nadia, Tapert, Susan, Uhlmann, Anne, Veltman, Dick, van Holst, Ruth, Whittle, Sarah, Wright, Margaret J, Yücel, Murat, Zhang, Sheng, Yurgelun-Todd, Deborah, Hibar, Derrek P, Jahanshad, Neda, Evans, Alan, Thompson, Paul M, Glahn, David C, Conrod, Patricia, Garavan, Hugh, and ENIGMA Addiction Working Group

Subjects
ENIGMA Addiction Working Group, Brain, Cerebral Cortex, Humans, Substance-Related Disorders, Alcoholism, Amphetamine-Related Disorders, Cocaine-Related Disorders, Marijuana Abuse, Tobacco Use Disorder, Methamphetamine, Organ Size, Adult, Middle Aged, Female, Male, Young Adult, Gray Matter, Support Vector Machine, Mega-Analysis, Structural MRI, Neurosciences, Drug Abuse (NIDA only), Brain Disorders, Alcoholism, Alcohol Use and Health, Substance Misuse, Mental health, Good Health and Well Being, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

ObjectiveAlthough lower brain volume has been routinely observed in individuals with substance dependence compared with nondependent control subjects, the brain regions exhibiting lower volume have not been consistent across studies. In addition, it is not clear whether a common set of regions are involved in substance dependence regardless of the substance used or whether some brain volume effects are substance specific. Resolution of these issues may contribute to the identification of clinically relevant imaging biomarkers. Using pooled data from 14 countries, the authors sought to identify general and substance-specific associations between dependence and regional brain volumes.MethodBrain structure was examined in a mega-analysis of previously published data pooled from 23 laboratories, including 3,240 individuals, 2,140 of whom had substance dependence on one of five substances: alcohol, nicotine, cocaine, methamphetamine, or cannabis. Subcortical volume and cortical thickness in regions defined by FreeSurfer were compared with nondependent control subjects when all sampled substance categories were combined, as well as separately, while controlling for age, sex, imaging site, and total intracranial volume. Because of extensive associations with alcohol dependence, a secondary contrast was also performed for dependence on all substances except alcohol. An optimized split-half strategy was used to assess the reliability of the findings.ResultsLower volume or thickness was observed in many brain regions in individuals with substance dependence. The greatest effects were associated with alcohol use disorder. A set of affected regions related to dependence in general, regardless of the substance, included the insula and the medial orbitofrontal cortex. Furthermore, a support vector machine multivariate classification of regional brain volumes successfully classified individuals with substance dependence on alcohol or nicotine relative to nondependent control subjects.ConclusionsThe results indicate that dependence on a range of different substances shares a common neural substrate and that differential patterns of regional volume could serve as useful biomarkers of dependence on alcohol and nicotine.

Published
2019

40. Associations of medication with subcortical morphology across the lifespan in OCD: Results from the international ENIGMA Consortium

Author

Poletti, Sara, Fridgeirsson, Egill Axfjord, Ikuta, Toshikazu, de Wit, Stella J., Vriend, Chris, Kasprzak, Selina, Kuno, Masaru, Takahashi, Jumpei, Miguel, Euripedes C., Shavitt, Roseli G., Hough, Morgan, Pariente, Jose C., Ortiz, Ana E., Bertolín, Sara, Real, Eva, Segalàs, Cinto, Moreira, Pedro Silva, Sousa, Nuno, Narumoto, Jin, Yamada, Kei, Tang, Jinsong, Fouche, Jean-Paul, Kim, Taekwan, Choi, Sunah, Ha, Minji, Park, Sunghyun, Ivanov, Iliyan, Boedhoe, Premika S.W., Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie H., Arnold, Paul D., Balachander, Srinivas, Baker, Justin T., Banaj, Nerisa, Bargalló, Nuria, Batistuzzo, Marcelo C., Benedetti, Francesco, Beucke, Jan C., Bollettini, Irene, Brem, Silvia, Brennan, Brian P., Buitelaar, Jan, Calvo, Rosa, Cheng, Yuqi, Cho, Kang Ik K., Dallaspezia, Sara, Denys, Damiaan, Diniz, Juliana B., Ely, Benjamin A., Feusner, Jamie D., Ferreira, Sónia, Fitzgerald, Kate D., Fontaine, Martine, Gruner, Patricia, Hanna, Gregory L., Hirano, Yoshiyuki, Hoexter, Marcelo Q., Huyser, Chaim, Ikari, Keisuke, James, Anthony, Jaspers-Fayer, Fern, Jiang, Hongyan, Kathmann, Norbert, Kaufmann, Christian, Kim, Minah, Koch, Kathrin, Kwon, Jun Soo, Lázaro, Luisa, Liu, Yanni, Lochner, Christine, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Mataix-Cols, David, Menchón, José M., Minuzzi, Luciano, Morer, Astrid, Morgado, Pedro, Nakagawa, Akiko, Nakamae, Takashi, Nakao, Tomohiro, Narayanaswamy, Janardhanan C., Nurmi, Erika L., Oh, Sanghoon, Perriello, Chris, Piacentini, John C., Picó-Pérez, Maria, Piras, Fabrizio, Piras, Federica, Reddy, Y.C. Janardhan, Manrique, Daniela Rodriguez, Sakai, Yuki, Shimizu, Eiji, Simpson, H. Blair, Soreni, Noam, Soriano-Mas, Carles, Spalletta, Gianfranco, Stern, Emily R., Stevens, Michael C., Stewart, S. Evelyn, Szeszko, Philip R., Tolin, David F., van Rooij, Daan, Veltman, Dick J., van der Werf, Ysbrand D., van Wingen, Guido A., Venkatasubramanian, Ganesan, Walitza, Susanne, Wang, Zhen, Watanabe, Anri, Wolters, Lidewij H., Xu, Xiufeng, Yun, Je-Yeon, Zarei, Mojtaba, Zhang, Fengrui, Zhao, Qing, Jahanshad, Neda, Thomopoulos, Sophia I., Thompson, Paul M., Stein, Dan J., van den Heuvel, Odile A., and O'Neill, Joseph

Published
2022
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41. Remodeling of the Cortical Structural Connectome in Posttraumatic Stress Disorder: Results From the ENIGMA-PGC Posttraumatic Stress Disorder Consortium

Author

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

Published
2022
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43. Virtual Ontogeny of Cortical Growth Preceding Mental Illness

Author

Patel, Yash, Shin, Jean, Abé, Christoph, Agartz, Ingrid, Alloza, Clara, Alnæs, Dag, Ambrogi, Sonia, Antonucci, Linda A., Arango, Celso, Arolt, Volker, Auzias, Guillaume, Ayesa-Arriola, Rosa, Banaj, Nerisa, Banaschewski, Tobias, Bandeira, Cibele, Başgöze, Zeynep, Cupertino, Renata Basso, Bau, Claiton H.D., Bauer, Jochen, Baumeister, Sarah, Bernardoni, Fabio, Bertolino, Alessandro, Bonnin, Caterina del Mar, Brandeis, Daniel, Brem, Silvia, Bruggemann, Jason, Bülow, Robin, Bustillo, Juan R., Calderoni, Sara, Calvo, Rosa, Canales-Rodríguez, Erick J., Cannon, Dara M., Carmona, Susanna, Carr, Vaughan J., Catts, Stanley V., Chenji, Sneha, Chew, Qian Hui, Coghill, David, Connolly, Colm G., Conzelmann, Annette, Craven, Alexander R., Crespo-Facorro, Benedicto, Cullen, Kathryn, Dahl, Andreas, Dannlowski, Udo, Davey, Christopher G., Deruelle, Christine, Díaz-Caneja, Covadonga M., Dohm, Katharina, Ehrlich, Stefan, Epstein, Jeffery, Erwin-Grabner, Tracy, Eyler, Lisa T., Fedor, Jennifer, Fitzgerald, Jacqueline, Foran, William, Ford, Judith M., Fortea, Lydia, Fuentes-Claramonte, Paola, Fullerton, Janice, Furlong, Lisa, Gallagher, Louise, Gao, Bingchen, Gao, Si, Goikolea, Jose M., Gotlib, Ian, Goya-Maldonado, Roberto, Grabe, Hans J., Green, Melissa, Grevet, Eugenio H., Groenewold, Nynke A., Grotegerd, Dominik, Gruber, Oliver, Haavik, Jan, Hahn, Tim, Harrison, Ben J., Heindel, Walter, Henskens, Frans, Heslenfeld, Dirk J., Hilland, Eva, Hoekstra, Pieter J., Hohmann, Sarah, Holz, Nathalie, Howells, Fleur M., Ipser, Jonathan C., Jahanshad, Neda, Jakobi, Babette, Jansen, Andreas, Janssen, Joost, Jonassen, Rune, Kaiser, Anna, Kaleda, Vasiliy, Karantonis, James, King, Joseph A., Kircher, Tilo, Kochunov, Peter, Koopowitz, Sheri-Michelle, Landén, Mikael, Landrø, Nils Inge, Lawrie, Stephen, Lebedeva, Irina, Luna, Beatriz, Lundervold, Astri J., MacMaster, Frank P., Maglanoc, Luigi A., Mathalon, Daniel H., McDonald, Colm, McIntosh, Andrew, Meinert, Susanne, Michie, Patricia T., Mitchell, Philip, Moreno-Alcázar, Ana, Mowry, Bryan, Muratori, Filippo, Nabulsi, Leila, Nenadić, Igor, O'Gorman Tuura, Ruth, Oosterlaan, Jaap, Overs, Bronwyn, Pantelis, Christos, Parellada, Mara, Pariente, Jose C., Pauli, Paul, Pergola, Giulio, Piarulli, Francesco Maria, Picon, Felipe, Piras, Fabrizio, Pomarol-Clotet, Edith, Pretus, Clara, Quidé, Yann, Radua, Joaquim, Ramos-Quiroga, J. Antoni, Rasser, Paul E., Reif, Andreas, Retico, Alessandra, Roberts, Gloria, Rossell, Susan, Rovaris, Diego Luiz, Rubia, Katya, Sacchet, Matthew D., Salavert, Josep, Salvador, Raymond, Sarró, Salvador, Sawa, Akira, Schall, Ulrich, Scott, Rodney, Selvaggi, Pierluigi, Silk, Tim, Sim, Kang, Skoch, Antonin, Spalletta, Gianfranco, Spaniel, Filip, Stein, Dan J., Steinsträter, Olaf, Stolicyn, Aleks, Takayanagi, Yoichiro, Tamm, Leanne, Tavares, Maria, Teumer, Alexander, Thiel, Katharina, Thomopoulos, Sophia I., Tomecek, David, Tomyshev, Alexander S., Tordesillas-Gutiérrez, Diana, Tosetti, Michela, Uhlmann, Anne, Van Rheenen, Tamsyn, Vazquez-Bourgón, Javier, Vernooij, Meike W., Vieta, Eduard, Vilarroya, Oscar, Weickert, Cynthia, Weickert, Thomas, Westlye, Lars T., Whalley, Heather, Willinger, David, Winter, Alexandra, Wittfeld, Katharina, Yang, Tony T., Yoncheva, Yuliya, Zijlmans, Jendé L., Hoogman, Martine, Franke, Barbara, van Rooij, Daan, Buitelaar, Jan, Ching, Christopher R.K., Andreassen, Ole A., Pozzi, Elena, Veltman, Dick, Schmaal, Lianne, van Erp, Theo G.M., Turner, Jessica, Castellanos, F. Xavier, Pausova, Zdenka, Thompson, Paul, and Paus, Tomas

Published
2022
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44. Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging

Author

Petrov, Dmitry, Gutman, Boris A., Shih-Hua, Yu, van Erp, Theo G. M., Turner, Jessica A., Schmaal, Lianne, Veltman, Dick, Wang, Lei, Alpert, Kathryn, Isaev, Dmitry, Zavaliangos-Petropulu, Artemis, Ching, Christopher R. K., Calhoun, Vince, Glahn, David, Satterthwaite, Theodore D., Andreasen, Ole Andreas, Borgwardt, Stefan, Howells, Fleur, Groenewold, Nynke, Voineskos, Aristotle, Radua, Joaquim, Potkin, Steven G., Crespo-Facorro, Benedicto, Tordesillas-Gutierrez, Diana, Shen, Li, Lebedeva, Irina, Spalletta, Gianfranco, Donohoe, Gary, Kochunov, Peter, Rosa, Pedro G. P., James, Anthony, Dannlowski, Udo, Baune, Bernhard T., Aleman, Andre, Gotlib, Ian H., Walter, Henrik, Walter, Martin, Soares, Jair C., Ehrlich, Stefan, Gur, Ruben C., Doan, N. Trung, Agartz, Ingrid, Westlye, Lars T., Harrisberger, Fabienne, Riecher-Rossler, Anita, Uhlmann, Anne, Stein, Dan J., Dickie, Erin W., Pomarol-Clotet, Edith, Fuentes-Claramonte, Paola, Canales-Rodriguez, Erick Jorge, Salvador, Raymond, Huang, Alexander J., Roiz-Santianez, Roberto, Cong, Shan, Tomyshev, Alexander, Piras, Fabrizio, Vecchio, Daniela, Banaj, Nerisa, Ciullo, Valentina, Hong, Elliot, Busatto, Geraldo, Zanetti, Marcus V., Serpa, Mauricio H., Cervenka, Simon, Kelly, Sinead, Grotegerd, Dominik, Sacchet, Matthew D., Veer, Ilya M., Li, Meng, Wu, Mon-Ju, Irungu, Benson, Walton, Esther, and Thompson, Paul M.

Subjects
Quantitative Biology - Quantitative Methods
Abstract

As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70\%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability., Comment: Arxiv version of the MICCAI 2017 Machine Learning in Medical Imaging workshop paper

Published
2017

45. Classification of Major Depressive Disorder via Multi-Site Weighted LASSO Model

Author

Zhu, Dajiang, Riedel, Brandalyn C., Jahanshad, Neda, Groenewold, Nynke A., Stein, Dan J., Gotlib, Ian H., Sacchet, Matthew D., Dima, Danai, Cole, James H., Fu, Cynthia H. Y., Walter, Henrik, Veer, Ilya M., Frodl, Thomas, Schmaal, Lianne, Veltman, Dick J., and Thompson, Paul M.

Subjects
Computer Science - Learning, Computer Science - Computational Engineering, Finance, and Science, Statistics - Applications
Abstract

Large-scale collaborative analysis of brain imaging data, in psychiatry and neu-rology, offers a new source of statistical power to discover features that boost ac-curacy 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 fea-tures that help to improve the classification accuracy are preserved. In tests on da-ta 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 ef-fective and practical collaborative platform for machine learning on large scale distributed imaging and biobank data., Comment: Accepted by MICCAI 2017

Published
2017

47. Expansion of hippocampal and amygdala shape in posttraumatic stress and early life stress

Author

Klaming, Ruth, Spadoni, Andrea D, Veltman, Dick J, and Simmons, Alan N

Subjects
Biological Psychology, Psychology, Behavioral and Social Science, Mental Health, Mental Illness, Anxiety Disorders, Violence Research, Brain Disorders, Post-Traumatic Stress Disorder (PTSD), Mental health, Adult, Adverse Childhood Experiences, Amygdala, Hippocampus, Humans, Male, Middle Aged, Stress Disorders, Post-Traumatic, Stress, Psychological, Veterans, Young Adult, Posttraumatic stress disorder, Early life stress, Shape analysis, Neurosciences, Biological psychology, Clinical and health psychology
Abstract

ObjectiveThe aim of this study was to examine the effect of Posttraumatic Stress Disorder (PTSD) and childhood adversity on brain structure. We assessed hippocampal and amygdala shape in veterans with varying levels of PTSD symptom severity and exposure to early life stressors (ELS).MethodsA total of 70 male veterans, who were deployed to a combat area during OIF/OEF/OND and who had been exposed to trauma during deployment, were included in the study. We applied a vertex-wise shape analysis of 3T MRI scans to measure indentation or expansion in hippocampal and amygdala shape.ResultsAnalyses showed a positive correlation between number of ELS and vertices in the right amygdala and the right hippocampus, as well as a positive correlation between PTSD symptom severity and right hippocampal vertices. There were no significant interactions between PTSD symptoms, ELS, and brain shape.DiscussionResults indicate a relationship between exposure to more childhood adversity and expansion in amygdala and hippocampal shape as well as between more severe PTSD symptoms and expansion in hippocampal shape. These findings may have important implications for the pathophysiology of trauma-related disorders.

Published
2019

49. 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, Kevin, primary, Boeken, Ole Jonas, additional, Langhammer, Till, additional, Groenewold, Nynke A., additional, Bas-Hoogendam, Janna Marie, additional, Aghajani, Moji, additional, Zugman, André, additional, Åhs, Fredrik, additional, Arolt, Volker, additional, Beesdo-Baum, Katja, additional, Björkstrand, Johannes, additional, Blackford, Jennifer U., additional, Blanco-Hinojo, Laura, additional, Böhnlein, Joscha, additional, Bülow, Robin, additional, Cano, Marta, additional, Cardoner, Narcis, additional, Caseras, Xavier, additional, Dannlowski, Udo, additional, Domschke, Katharina, additional, Fehm, Lydia, additional, Feola, Brandee, additional, Fredrikson, Mats, additional, Goossens, Liesbet, additional, Grabe, Hans J., additional, Grotegerd, Dominik, additional, Gur, Raquel E., additional, Hamm, Alfons O., additional, Harrewijn, Anita, additional, Heinig, Ingmar, additional, Herrmann, Martin J., additional, Hofmann, David, additional, Jackowski, Andrea P., additional, Jansen, Andreas, additional, Kaczkurkin, Antonia N., additional, Kindt, Merel, additional, Kingsley, Ellen N., additional, Kircher, Tilo, additional, Klahn, Anna L., additional, Koelkebeck, Katja, additional, Krug, Axel, additional, Kugel, Harald, additional, Larsen, Bart, additional, Leehr, Elisabeth J., additional, Leonhardt, Lieselotte, additional, Lotze, Martin, additional, Margraf, Jürgen, additional, Michałowski, Jarosław, additional, Muehlhan, Markus, additional, Nenadić, Igor, additional, Pan, Pedro M., additional, Pauli, Paul, additional, Peñate, Wenceslao, additional, Pittig, Andre, additional, Plag, Jens, additional, Pujol, Jesus, additional, Richter, Jan, additional, Rivero, Francisco L., additional, Salum, Giovanni A., additional, Satterthwaite, Theodore D., additional, Schäfer, Axel, additional, Schäfer, Judith, additional, Schienle, Anne, additional, Schneider, Silvia, additional, Schrammen, Elisabeth, additional, Schruers, Koen, additional, Schulz, Stefan M., additional, Seidl, Esther, additional, Stark, Rudolf M., additional, Stein, Frederike, additional, Straube, Benjamin, additional, Straube, Thomas, additional, Ströhle, Andreas, additional, Suchan, Boris, additional, Thomopoulos, Sophia I., additional, Ventura-Bort, Carlos, additional, Visser, Renee, additional, Völzke, Henry, additional, Wabnegger, Albert, additional, Wannemüller, André, additional, Wendt, Julia, additional, Wiemer, Julian, additional, Wittchen, Hans-Ulrich, additional, Wittfeld, Katharina, additional, Wright, Barry, additional, Yang, Yunbo, additional, Zilverstand, Anna, additional, Zwanzger, Peter, additional, Veltman, Dick J., additional, Winkler, Anderson M., additional, Pine, Daniel S., additional, Jahanshad, Neda, additional, Thompson, Paul M., additional, Stein, Dan J., additional, Van der Wee, Nic J.A., additional, and Lueken, Ulrike, additional

Published
2024
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50. 10. Shared and Distinct Alterations in Brain Structure of Children and Adolescents with Internalising or Externalising Disorders: Findings From the ENIGMA Antisocial Behavior, ADHD, MDD and Anxiety Working Groups

Author

Townend, Sophie, primary, Staginnus, Marlene, additional, Gao, Yidian, additional, Franke, Barbara, additional, Hoogman, Martine, additional, Schmaal, Lianne, additional, Veltman, Dick, additional, Pozzi, Elena, additional, Bas-Hoogendam, Janna Marie, additional, Groenewold, Nynke A., additional, Stein, Dan J., additional, van der Wee, Nic.J.A., additional, Aghajani, Moji, additional, Cecil, Charlotte, additional, Klapwijk, Eduard, additional, Baskin-Sommers, Arielle, additional, Pine, Daniel S., additional, Thomopoulos, Sophia I., additional, Jahanshad, Neda, additional, Thompson, Paul, additional, Walton, Esther, additional, De Brito, Stephane A., additional, and Fairchild, Graeme, additional

Published
2024
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