Search

Your search keyword '"Wu, Mon-Ju"' showing total 30 results
Start Over Author "Wu, Mon-Ju" Publication Year Range Last 3 years
30 results on '"Wu, Mon-Ju"'

1. Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size

Author

Yu, Yuetong, Cui, Hao‐Qi, Haas, Shalaila S, New, Faye, Sanford, Nicole, Yu, Kevin, Zhan, Denghuang, Yang, Guoyuan, Gao, Jia‐Hong, Wei, Dongtao, Qiu, Jiang, Banaj, Nerisa, Boomsma, Dorret I, Breier, Alan, Brodaty, Henry, Buckner, Randy L, Buitelaar, Jan K, Cannon, Dara M, Caseras, Xavier, Clark, Vincent P, Conrod, Patricia J, Crivello, Fabrice, Crone, Eveline A, Dannlowski, Udo, Davey, Christopher G, de Haan, Lieuwe, de Zubicaray, Greig I, Di Giorgio, Annabella, Fisch, Lukas, Fisher, Simon E, Franke, Barbara, Glahn, David C, Grotegerd, Dominik, Gruber, Oliver, Gur, Raquel E, Gur, Ruben C, Hahn, Tim, Harrison, Ben J, Hatton, Sean, Hickie, Ian B, Pol, Hilleke E Hulshoff, Jamieson, Alec J, Jernigan, Terry L, Jiang, Jiyang, Kalnin, Andrew J, Kang, Sim, Kochan, Nicole A, Kraus, Anna, Lagopoulos, Jim, Lazaro, Luisa, McDonald, Brenna C, McDonald, Colm, McMahon, Katie L, Mwangi, Benson, Piras, Fabrizio, Rodriguez‐Cruces, Raul, Royer, Jessica, Sachdev, Perminder S, Satterthwaite, Theodore D, Saykin, Andrew J, Schumann, Gunter, Sevaggi, Pierluigi, Smoller, Jordan W, Soares, Jair C, Spalletta, Gianfranco, Tamnes, Christian K, Trollor, Julian N, Ent, Dennis Van't, Vecchio, Daniela, Walter, Henrik, Wang, Yang, Weber, Bernd, Wen, Wei, Wierenga, Lara M, Williams, Steven CR, Wu, Mon‐Ju, Zunta‐Soares, Giovana B, Bernhardt, Boris, Thompson, Paul, Frangou, Sophia, Ge, Ruiyang, and Group, ENIGMA‐Lifespan Working

Subjects
Biological Psychology, Psychology, Aging, Clinical Research, Neurosciences, Neurological, Mental health, Humans, Adolescent, Female, Aged, Adult, Child, Young Adult, Male, Brain, Aged, 80 and over, Child, Preschool, Middle Aged, Magnetic Resonance Imaging, Neuroimaging, Sample Size, benchmarking, brain aging, brainAGE, ENIGMA‐Lifespan Working Group, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Published
2024

2. 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

3. 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
Full Text
View/download PDF

4. 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

5. 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

6. Intelligence, educational attainment, and brain structure in those at familial high‐risk for schizophrenia or bipolar disorder

Author

Zwarte, Sonja MC, Brouwer, Rachel M, Agartz, Ingrid, Alda, Martin, Alonso‐Lana, Silvia, Bearden, Carrie E, Bertolino, Alessandro, Bonvino, Aurora, Bramon, Elvira, Buimer, Elizabeth EL, Cahn, Wiepke, Canales‐Rodríguez, Erick J, Cannon, Dara M, Cannon, Tyrone D, Caseras, Xavier, Castro‐Fornieles, Josefina, Chen, Qiang, Chung, Yoonho, De la Serna, Elena, Bonnin, Caterina Mar, Demro, Caroline, Di Giorgio, Annabella, Doucet, Gaelle E, Eker, Mehmet Cagdas, Erk, Susanne, Fatjó‐Vilas, Mar, Fears, Scott C, Foley, Sonya F, Frangou, Sophia, Fullerton, Janice M, Glahn, David C, Goghari, Vina M, Goikolea, Jose M, Goldman, Aaron L, Gonul, Ali Saffet, Gruber, Oliver, Hajek, Tomas, Hawkins, Emma L, Heinz, Andreas, Ongun, Ceren Hidiroglu, Hillegers, Manon HJ, Houenou, Josselin, Pol, Hilleke E Hulshoff, Hultman, Christina M, Ingvar, Martin, Johansson, Viktoria, Jönsson, Erik G, Kane, Fergus, Kempton, Matthew J, Koenis, Marinka MG, Kopecek, Miloslav, Krämer, Bernd, Lawrie, Stephen M, Lenroot, Rhoshel K, Marcelis, Machteld, Mattay, Venkata S, McDonald, Colm, Meyer‐Lindenberg, Andreas, Michielse, Stijn, Mitchell, Philip B, Moreno, Dolores, Murray, Robin M, Mwangi, Benson, Nabulsi, Leila, Newport, Jason, Olman, Cheryl A, Os, Jim, Overs, Bronwyn J, Ozerdem, Aysegul, Pergola, Giulio, Picchioni, Marco M, Piguet, Camille, Pomarol‐Clotet, Edith, Radua, Joaquim, Ramsay, Ian S, Richter, Anja, Roberts, Gloria, Salvador, Raymond, Aydogan, Aybala Saricicek, Sarró, Salvador, Schofield, Peter R, Simsek, Esma M, Simsek, Fatma, Soares, Jair C, Sponheim, Scott R, Sugranyes, Gisela, Toulopoulou, Timothea, Tronchin, Giulia, Vieta, Eduard, Walter, Henrik, Weinberger, Daniel R, Whalley, Heather C, Wu, Mon‐Ju, Yalin, Nefize, Andreassen, Ole A, Ching, Christopher RK, Thomopoulos, Sophia I, Erp, Theo GM, Jahanshad, Neda, and Thompson, Paul M

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Brain Disorders, Bipolar Disorder, Clinical Research, Serious Mental Illness, Neurosciences, Schizophrenia, Mental Health, Aetiology, 2.3 Psychological, social and economic factors, 2.1 Biological and endogenous factors, Mental health, Cognitive Dysfunction, Educational Status, Family, Genetic Predisposition to Disease, Humans, Intelligence, Magnetic Resonance Imaging, Neuroimaging, bipolar disorder, education, intelligence, neuroimaging, relatives, schizophrenia, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

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

Published
2022

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

Author

Han, Laura KM, Dinga, Richard, Hahn, Tim, Ching, Christopher RK, Eyler, Lisa T, Aftanas, Lyubomir, Aghajani, Moji, Aleman, André, Baune, Bernhard T, Berger, Klaus, Brak, Ivan, Filho, Geraldo Busatto, Carballedo, Angela, Connolly, Colm G, Couvy-Duchesne, Baptiste, Cullen, Kathryn R, Dannlowski, Udo, Davey, Christopher G, Dima, Danai, Duran, Fabio LS, Enneking, Verena, Filimonova, Elena, Frenzel, Stefan, Frodl, Thomas, Fu, Cynthia HY, Godlewska, Beata R, Gotlib, Ian H, Grabe, Hans J, Groenewold, Nynke A, Grotegerd, Dominik, Gruber, Oliver, Hall, Geoffrey B, Harrison, Ben J, Hatton, Sean N, Hermesdorf, Marco, Hickie, Ian B, Ho, Tiffany C, Hosten, Norbert, Jansen, Andreas, Kähler, Claas, Kircher, Tilo, Klimes-Dougan, Bonnie, Krämer, Bernd, Krug, Axel, Lagopoulos, Jim, Leenings, Ramona, MacMaster, Frank P, MacQueen, Glenda, McIntosh, Andrew, McLellan, Quinn, McMahon, Katie L, Medland, Sarah E, Mueller, Bryon A, Mwangi, Benson, Osipov, Evgeny, Portella, Maria J, Pozzi, Elena, Reneman, Liesbeth, Repple, Jonathan, Rosa, Pedro GP, Sacchet, Matthew D, Sämann, Philipp G, Schnell, Knut, Schrantee, Anouk, Simulionyte, Egle, Soares, Jair C, Sommer, Jens, Stein, Dan J, Steinsträter, Olaf, Strike, Lachlan T, Thomopoulos, Sophia I, van Tol, Marie-José, Veer, Ilya M, Vermeiren, Robert RJM, Walter, Henrik, van der Wee, Nic JA, van der Werff, Steven JA, Whalley, Heather, Winter, Nils R, Wittfeld, Katharina, Wright, Margaret J, Wu, Mon-Ju, Völzke, Henry, Yang, Tony T, Zannias, Vasileios, de Zubicaray, Greig I, Zunta-Soares, Giovana B, Abé, Christoph, Alda, Martin, Andreassen, Ole A, Bøen, Erlend, Bonnin, Caterina M, Canales-Rodriguez, Erick J, Cannon, Dara, Caseras, Xavier, Chaim-Avancini, Tiffany M, Elvsåshagen, Torbjørn, Favre, Pauline, Foley, Sonya F, and Fullerton, Janice M

Subjects
Depression, Aging, Brain Disorders, Biomedical Imaging, Serious Mental Illness, Mental Health, Major Depressive Disorder, Behavioral and Social Science, Clinical Research, Neurosciences, 2.3 Psychological, social and economic factors, Aetiology, Mental health, Adolescent, Adult, Aged, Brain, Depressive Disorder, Major, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Young Adult, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.

Published
2021

8. Structural brain alterations associated with suicidal thoughts and behaviors in young people: results from 21 international studies from the ENIGMA Suicidal Thoughts and Behaviours consortium

Author

van Velzen, Laura S., Dauvermann, Maria R., Colic, Lejla, Villa, Luca M., Savage, Hannah S., Toenders, Yara J., Zhu, Alyssa H., Bright, Joanna K., Campos, Adrián I., Salminen, Lauren E., Ambrogi, Sonia, Ayesa-Arriola, Rosa, Banaj, Nerisa, Başgöze, Zeynep, Bauer, Jochen, Blair, Karina, Blair, Robert James, Brosch, Katharina, Cheng, Yuqi, Colle, Romain, Connolly, Colm G., Corruble, Emmanuelle, Couvy-Duchesne, Baptiste, Crespo-Facorro, Benedicto, Cullen, Kathryn R., Dannlowski, Udo, Davey, Christopher G., Dohm, Katharina, Fullerton, Janice M., Gonul, Ali Saffet, Gotlib, Ian H., Grotegerd, Dominik, Hahn, Tim, Harrison, Ben J., He, Mengxin, Hickie, Ian B., Ho, Tiffany C., Iorfino, Frank, Jansen, Andreas, Jollant, Fabrice, Kircher, Tilo, Klimes-Dougan, Bonnie, Klug, Melissa, Leehr, Elisabeth J., Lippard, Elizabeth T. C., McLaughlin, Katie A., Meinert, Susanne, Miller, Adam Bryant, Mitchell, Philip B., Mwangi, Benson, Nenadić, Igor, Ojha, Amar, Overs, Bronwyn J., Pfarr, Julia-Katharina, Piras, Fabrizio, Ringwald, Kai G., Roberts, Gloria, Romer, Georg, Sanches, Marsal, Sheridan, Margaret A., Soares, Jair C., Spalletta, Gianfranco, Stein, Frederike, Teresi, Giana I., Tordesillas-Gutiérrez, Diana, Uyar-Demir, Aslihan, van der Wee, Nic J. A., van der Werff, Steven J., Vermeiren, Robert R. J. M., Winter, Alexandra, Wu, Mon-Ju, Yang, Tony T., Thompson, Paul M., Rentería, Miguel E., Jahanshad, Neda, Blumberg, Hilary P., van Harmelen, Anne-Laura, and Schmaal, Lianne

Published
2022
Full Text
View/download PDF

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

Author

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

Published
2024
Full Text
View/download PDF

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

Author

Opel, Nils, Thalamuthu, Anbupalam, Milaneschi, Yuri, Grotegerd, Dominik, Flint, Claas, Leenings, Ramona, Goltermann, Janik, Richter, Maike, Hahn, Tim, Woditsch, Georg, Berger, Klaus, Hermesdorf, Marco, McIntosh, Andrew, Whalley, Heather C., Harris, Mathew A., MacMaster, Frank P., Walter, Henrik, Veer, Ilya M., Frodl, Thomas, Carballedo, Angela, Krug, Axel, Nenadic, Igor, Kircher, Tilo, Aleman, Andre, Groenewold, Nynke A., Stein, Dan J., Soares, Jair C., Zunta-Soares, Giovana B., Mwangi, Benson, Wu, Mon-Ju, Walter, Martin, Li, Meng, Harrison, Ben J., Davey, Christopher G., Cullen, Kathryn R., Klimes-Dougan, Bonnie, Mueller, Bryon A., Sämann, Philipp G., Penninx, Brenda, Nawijn, Laura, Veltman, Dick J., Aftanas, Lyubomir, Brak, Ivan V., Filimonova, Elena A., Osipov, Evgeniy A., Reneman, Liesbeth, Schrantee, Anouk, Grabe, Hans J., Van der Auwera, Sandra, Wittfeld, Katharina, Hosten, Norbert, Völzke, Henry, Sim, Kang, Gotlib, Ian H., Sacchet, Matthew D., Lagopoulos, Jim, Hatton, Sean N., Hickie, Ian, Pozzi, Elena, Thompson, Paul M., Jahanshad, Neda, Schmaal, Lianne, Baune, Bernhard T., and Dannlowski, Udo

Published
2021
Full Text
View/download PDF

11. 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 A. V., 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 J. A., Winkler, Anderson M., and Pine, Daniel S.

Published
2021
Full Text
View/download PDF

12. Correction: Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders

Author

Opel, Nils, Thalamuthu, Anbupalam, Milaneschi, Yuri, Grotegerd, Dominik, Flint, Claas, Leenings, Ramona, Goltermann, Janik, Richter, Maike, Hahn, Tim, Woditsch, Georg, Berger, Klaus, Hermesdorf, Marco, McIntosh, Andrew, Whalley, Heather C., Harris, Mathew A., MacMaster, Frank P., Walter, Henrik, Veer, Ilya M., Frodl, Thomas, Carballedo, Angela, Krug, Axel, Nenadic, Igor, Kircher, Tilo, Aleman, Andre, Groenewold, Nynke A., Stein, Dan J., Soares, Jair C., Zunta-Soares, Giovana B., Mwangi, Benson, Wu, Mon-Ju, Walter, Martin, Li, Meng, Harrison, Ben J., Davey, Christopher G., Cullen, Kathryn R., Klimes-Dougan, Bonnie, Mueller, Bryon A., Sämann, Philipp G., Penninx, Brenda, Nawijn, Laura, Veltman, Dick J., Aftanas, Lyubomir, Brak, Ivan V., Filimonova, Elena A., Osipov, Evgeniy A., Reneman, Liesbeth, Schrantee, Anouk, Grabe, Hans J., Van der Auwera, Sandra, Wittfeld, Katharina, Hosten, Norbert, Völzke, Henry, Sim, Kang, Gotlib, Ian H., Sacchet, Matthew D., Lagopoulos, Jim, Hatton, Sean N., Hickie, Ian, Pozzi, Elena, Thompson, Paul M., Jahanshad, Neda, Schmaal, Lianne, Baune, Bernhard T., and Dannlowski, Udo

Published
2021
Full Text
View/download PDF

13. Normative Modeling of Brain Morphometry Across the Lifespan using CentileBrain: Algorithm Benchmarking and Model Optimization

Author

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

Published
2023
Full Text
View/download PDF

14. Bridging Big Data: Procedures for Combining Non-equivalent Cognitive Measures from the ENIGMA Consortium

Author

Kennedy, Eamonn, primary, Vadlamani, Shashank, additional, Lindsey, Hannah M, additional, Lei, Pui-Wa, additional, Jo-Pugh, Mary, additional, Adamson, Maheen, additional, Alda, Martin, additional, Alonso-Lana, Silvia, additional, Ambrogi, Sonia, additional, Anderson, Tim J, additional, Arango, Celso, additional, Asarnow, Robert F, additional, Avram, Mihai, additional, Ayesa-Arriola, Rosa, additional, Babikian, Talin, additional, Banaj, Nerisa, additional, Bird, Laura J, additional, Borgwardt, Stefan, additional, Brodtmann, Amy, additional, Brosch, Katharina, additional, Caeyenberghs, Karen, additional, Calhoun, Vince D, additional, Chiaravalloti, Nancy D, additional, Cifu, David X, additional, Crespo-Facorro, Benedicto, additional, Dalrymple-Alford, John C, additional, Dams-O’Connor, Kristen, additional, Dannlowski, Udo, additional, Darby, David, additional, Davenport, Nicholas, additional, DeLuca, John, additional, Diaz-Caneja, Covadonga M, additional, Disner, Seth G, additional, Dobryakova, Ekaterina, additional, Ehrlich, Stefan, additional, Esopenko, Carrie, additional, Ferrarelli, Fabio, additional, Frank, Lea E, additional, Franz, Carol, additional, Fuentes-Claramonte, Paola, additional, Genova, Helen, additional, Giza, Christopher C, additional, Goltermann, Janik, additional, Grotegerd, Dominik, additional, Gruber, Marius, additional, Gutierrez-Zotes, Alfonso, additional, Ha, Minji, additional, Haavik, Jan, additional, Hinkin, Charles, additional, Hoskinson, Kristen R, additional, Hubl, Daniela, additional, Irimia, Andrei, additional, Jansen, Andreas, additional, Kaess, Michael, additional, Kang, Xiaojian, additional, Kenney, Kimbra, additional, Keřková, Barbora, additional, Khlif, Mohamed Salah, additional, Kim, Minah, additional, Kindler, Jochen, additional, Kircher, Tilo, additional, Knížková, Karolina, additional, Kolskår, Knut K, additional, Krch, Denise, additional, Kremen, William S, additional, Kuhn, Taylor, additional, Kumari, Veena, additional, Kwon, Jun Soo, additional, Langella, Roberto, additional, Laskowitz, Sarah, additional, Lee, Jungha, additional, Lengenfelder, Jean, additional, Liebel, Spencer W, additional, Liou-Johnson, Victoria, additional, Lippa, Sara M, additional, Løvstad, Marianne, additional, Lundervold, Astri, additional, Marotta, Cassandra, additional, Marquardt, Craig A, additional, Mattos, Paulo, additional, Mayeli, Ahmad, additional, McDonald, Carrie R, additional, Meinert, Susanne, additional, Melzer, Tracy R, additional, Merchán-Naranjo, Jessica, additional, Michel, Chantal, additional, Morey, Rajendra A, additional, Mwangi, Benson, additional, Myall, Daniel J, additional, Nenadić, Igor, additional, Newsome, Mary R, additional, Nunes, Abraham, additional, O’Brien, Terence, additional, Oertel, Viola, additional, Ollinger, John, additional, Olsen, Alexander, additional, de la Foz, Victor Ortiz García, additional, Ozmen, Mustafa, additional, Pardoe, Heath, additional, Parent, Marise, additional, Piras, Fabrizio, additional, Piras, Federica, additional, Pomarol-Clotet, Edith, additional, Repple, Jonathan, additional, Richard, Geneviève, additional, Rodriguez, Jonathan, additional, Rodriguez, Mabel, additional, Rootes-Murdy, Kelly, additional, Rowland, Jared, additional, Ryan, Nicholas P, additional, Salvador, Raymond, additional, Sanders, Anne-Marthe, additional, Schmidt, Andre, additional, Soares, Jair C, additional, Spalleta, Gianfranco, additional, Španiel, Filip, additional, Stasenko, Alena, additional, Stein, Frederike, additional, Straube, Benjamin, additional, Thames, April, additional, Thomas-Odenthal, Florian, additional, Thomopoulos, Sophia I, additional, Tone, Erin, additional, Torres, Ivan, additional, Troyanskaya, Maya, additional, Turner, Jessica A, additional, Ulrichsen, Kristine M, additional, Umpierrez, Guillermo, additional, Vilella, Elisabet, additional, Vivash, Lucy, additional, Walker, William C, additional, Werden, Emilio, additional, Westlye, Lars T, additional, Wild, Krista, additional, Wroblewski, Adrian, additional, Wu, Mon-Ju, additional, Wylie, Glenn R, additional, Yatham, Lakshmi N, additional, Zunta-Soares, Giovana B, additional, Thompson, Paul M, additional, Tate, David F, additional, Hillary, Frank G, additional, Dennis, Emily L, additional, and Wilde, Elisabeth A, additional

Published
2023
Full Text
View/download PDF

15. Brain-Based Classification of Youth with Anxiety Disorders: an ENIGMA-ANXIETY Transdiagnostic Examination using Machine Learning

Author

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

Published
2022
Full Text
View/download PDF

16. ENIGMA generalized anxiety disorder

Author

Harrewijn, Anita, Cardinale, Elise, Bas-Hoogendam, Janna Marie, Groenewold, Nynke, Stein, Dan, van der Wee, Nic, Buckner, Randy, Fonzo, Gregory, Manfro, Gisele, Mwangi, Benson, Nielsen, Jared, Paulus, Martin, Salum, Giovanni, Jordan Smoller, ScD, Soares, Jair, Stein, Murray, Strawn, Jeffrey, Wu, Mon-Ju, group, ENIGMA, Thompson, Paul, Winkler, Anderson, and Pine, Daniel

Subjects
endocrine system
Abstract

Comparing cortical and subcortical brain structure between patients with generalized anxiety disorder and healthy comparison subjects – findings from the ENIGMA Generalized Anxiety Disorder Working Group

Published
2022
Full Text
View/download PDF

17. The role of educational attainment and brain morphology in major depressive disorder: Findings from the ENIGMA major depressive disorder consortium.

Author

Whittle, Sarah, primary, Rakesh, Divyangana, additional, Schmaal, Lianne, additional, Veltman, Dick J., additional, Thompson, Paul M., additional, Singh, Aditya, additional, Gonul, Ali Saffet, additional, Aleman, Andre, additional, Uyar Demir, Aslıhan, additional, Krug, Axel, additional, Mwangi, Benson, additional, Krämer, Bernd, additional, Baune, Bernhard T., additional, Stein, Dan J., additional, Grotegerd, Dominik, additional, Pomarol-Clotet, Edith, additional, Rodríguez-Cano, Elena, additional, Melloni, Elisa, additional, Benedetti, Francesco, additional, Stein, Frederike, additional, Grabe, Hans J., additional, Völzke, Henry, additional, Gotlib, Ian H., additional, Nenadić, Igor, additional, Soares, Jair C., additional, Repple, Jonathan, additional, Sim, Kang, additional, Brosch, Katharina, additional, Wittfeld, Katharina, additional, Berger, Klaus, additional, Hermesdorf, Marco, additional, Portella, Maria J., additional, Sacchet, Matthew D., additional, Wu, Mon-Ju, additional, Opel, Nils, additional, Groenewold, Nynke A., additional, Gruber, Oliver, additional, Fuentes-Claramonte, Paola, additional, Salvador, Raymond, additional, Goya-Maldonado, Roberto, additional, Sarró, Salvador, additional, Poletti, Sara, additional, Meinert, Susanne L., additional, Kircher, Tilo, additional, Dannlowski, Udo, additional, and Pozzi, Elena, additional

Published
2022
Full Text
View/download PDF

18. White matter microstructure associated with anhedonia among individuals with bipolar disorders and high-risk for bipolar disorders

Author

Diaz, Alexandre Paim, primary, Fernandes, Brisa S., additional, Teixeira, Antonio Lucio, additional, Mwangi, Benson, additional, Hasan, Khader M., additional, Wu, Mon-Ju, additional, Selvaraj, Sudhakar, additional, Suen, Paulo, additional, Zanao, Tamires Araujo, additional, Brunoni, Andre R., additional, Sanches, Marsal, additional, and Soares, Jair C., additional

Published
2022
Full Text
View/download PDF

19. Building and Advancing Coalition Capacity to Promote Health Equity: Insights from the Health Equity Collective's Approach to Addressing Social Determinants of Health

Author

John, Jemima C., primary, Kaleemullah, Tanweer, additional, McPherson, Heidi, additional, Mahata, Kallol, additional, Morrow, Robert B., additional, Bujnowski, Deborah, additional, Johnston, Alicia, additional, Danho, Melisa, additional, Siddiqui, Nadia, additional, Walsh, Michael T., additional, Haley, Sean A., additional, Sirajuddin, Anwar M., additional, Schauer, Timothy, additional, Wu, Mon-Ju, additional, Rechis, Ruth, additional, Galvan, Esperanza, additional, Correa, Nancy, additional, Browning, Nikki, additional, Ganelin, Deborah, additional, Gonzalez, Jennifer, additional, Lofton, Staci, additional, Banerjee, Deborah, additional, and Sharma, Shreela V., additional

Published
2021
Full Text
View/download PDF

20. Structural brain alterations associated with suicidal thoughts and behaviors in young people: results across 21 international studies from the ENIGMA Suicidal Thoughts and Behaviours consortium

Author

van Velzen, Laura S., primary, Dauvermann, Maria R., additional, Colic, Lejla, additional, Villa, Luca M., additional, Savage, Hannah S., additional, Toenders, Yara J., additional, Zhu, Alyssa H., additional, Bright, Joanna K., additional, Campos, Adrián I., additional, Salminen, Lauren, additional, Ambrogi, Sonia, additional, Ayesa-Arriola, Rosa, additional, Banaj, Nerisa, additional, Başgöze, Zeynep, additional, Bauer, Jochen, additional, Blair, Karina, additional, Blair, Robert James, additional, Brosch, Katharina, additional, Cheng, Yuqi, additional, Colle, Romain, additional, Connolly, Colm G., additional, Corruble, Emmanuelle, additional, Couvy-Duchesne, Baptiste, additional, Crespo-Facorro, Benedicto, additional, Cullen, Kathryn R., additional, Dannlowski, Udo, additional, Davey, Christopher G., additional, Dohm, Katharina, additional, Fullerton, Janice M., additional, Gonul, Ali Saffet, additional, Gotlib, Ian H., additional, Grotegerd, Dominik, additional, Hahn, Tim, additional, Harrison, Ben J., additional, He, Mengxin, additional, Hickie, Ian B., additional, Ho, Tiffany C., additional, Iorfino, Frank, additional, Jansen, Andreas, additional, Jollant, Fabrice, additional, Kircher, Tilo, additional, Klimes-Dougan, Bonnie, additional, Klug, Melissa, additional, Leehr, Elisabeth J., additional, Lippard, Elizabeth T.C., additional, McLaughlin, Katie A., additional, Meinert, Susanne, additional, Miller, Adam Bryant, additional, Mitchell, Philip B., additional, Mwangi, Benson, additional, Nenadić, Igor, additional, Ojha, Amar, additional, Overs, Bronwyn J., additional, Pfarr, Julia-Katharina, additional, Piras, Fabrizio, additional, Ringwald, Kai G., additional, Roberts, Gloria, additional, Romer, Georg, additional, Sanches, Marsal, additional, Sheridan, Margaret A., additional, Soares, Jair C, additional, Spalletta, Gianfranco, additional, Stein, Frederike, additional, Teresi, Giana I., additional, Tordesillas-Gutiérrez, Diana, additional, Uyar-Demir, Aslihan, additional, van der Wee, Nic J.A., additional, van der Werff, Steven J., additional, Vermeiren, Robert R.J.M., additional, Winter, Alexandra, additional, Wu, Mon-Ju, additional, Yang, Tony T., additional, Thompson, Paul M., additional, Rentería, Miguel E., additional, Jahanshad, Neda, additional, Blumberg, Hilary P., additional, van Harmelen, Anne-Laura, additional, and Schmaal, Lianne, additional

Published
2021
Full Text
View/download PDF

21. Investigation of endophenotype potential of decreased fractional anisotropy in pediatric bipolar disorder patients and unrelated offspring of bipolar disorder patients

Author

Camkurt, Mehmet A., Melicher, Tomas, Mwangi, Benson, Wu, Mon-Ju, Cao, Bo, Zeni, Cristian P., Tannous, Jonika, Zunta-Soares, Giovana, Hasan, Khader, Sanches, Marsal, and Soares, Jair C.

Abstract

AbstractBackgroundBipolar disorder (BD) is a severe psychiatric disorder associated with structural and functional brain abnormalities, some of which have been found in unaffected relatives as well. In this study, we examined the potential role of decreased fractional anisotropy (FA) as a BD endophenotype, in adolescents at high risk for BD.MethodsWe included 15 offspring of patients with BD, 16 pediatric BD patients, and 16 matched controls. Diffusion weighted scans were obtained on a 3T scanner using an echo-planar sequence. Scans were segmented using FreeSurfer.ResultsOur results showed significantly decreased FA in six brain areas of offspring group; left superior temporal gyrus (LSTG; P< .0001), left transverse temporal gyrus (LTTG; P= .002), left banks of the superior temporal sulcus (LBSTS; P= .002), left anterior cingulum (LAC; P= .003), right temporal pole (RTP; P= .004) and left frontal pole (LFP; P= .017). On analysis, LSTG, LAC, and RTP demonstrated a potential to be an endophenotype when comparing all three groups. FA values in three regions, LBSTS, LTTG, and LFP were increased only in controls.ConclusionOur findings point at decreased FA as a possible endophenotype for BD, as they were found in children of patients with BD. Most of these areas were previously found to have morphological and functional changes in adult and pediatric BD, and are thought to play important roles in affected domains of functioning. Prospective follow up studies should be performed to detect reliability of decreased FA as an endophenotype and effects of treatment on FA.

Published
2022
Full Text
View/download PDF

22. The role of educational attainment and brain morphology in major depressive disorder: Findings from the ENIGMA major depressive disorder consortium

Author

Sarah Whittle, Divyangana Rakesh, Lianne Schmaal, Dick J. Veltman, Paul M. Thompson, Aditya Singh, Ali Saffet Gonul, Andre Aleman, Aslıhan Uyar Demir, Axel Krug, Benson Mwangi, Bernd Krämer, Bernhard T. Baune, Dan J. Stein, Dominik Grotegerd, Edith Pomarol-Clotet, Elena Rodríguez-Cano, Elisa Melloni, Francesco Benedetti, Frederike Stein, Hans J. Grabe, Henry Völzke, Ian H. Gotlib, Igor Nenadić, Jair C. Soares, Jonathan Repple, Kang Sim, Katharina Brosch, Katharina Wittfeld, Klaus Berger, Marco Hermesdorf, Maria J. Portella, Matthew D. Sacchet, Mon-Ju Wu, Nils Opel, Nynke A. Groenewold, Oliver Gruber, Paola Fuentes-Claramonte, Raymond Salvador, Roberto Goya-Maldonado, Salvador Sarró, Sara Poletti, Susanne L. Meinert, Tilo Kircher, Udo Dannlowski, Elena Pozzi, Whittle, Sarah, Rakesh, Divyangana, Schmaal, Lianne, Veltman, Dick J., Thompson, Paul M., Singh, Aditya, Gonul, Ali Saffet, Aleman, Andre, Uyar Demir, Aslıhan, Krug, Axel, Mwangi, Benson, Krämer, Bernd, Baune, Bernhard T., Stein, Dan J., Grotegerd, Dominik, Pomarol-Clotet, Edith, Rodríguez-Cano, Elena, Melloni, Elisa, Benedetti, Francesco, Stein, Frederike, Grabe, Hans J., Völzke, Henry, Gotlib, Ian H., Nenadić, Igor, Soares, Jair C., Repple, Jonathan, Sim, Kang, Brosch, Katharina, Wittfeld, Katharina, Berger, Klau, Hermesdorf, Marco, Portella, Maria J., Sacchet, Matthew D., Wu, Mon-Ju, Opel, Nil, Groenewold, Nynke A., Gruber, Oliver, Fuentes-Claramonte, Paola, Salvador, Raymond, Goya-Maldonado, Roberto, Sarró, Salvador, Poletti, Sara, Meinert, Susanne L., Kircher, Tilo, Dannlowski, Udo, and Pozzi, Elena

Subjects
Adult, Depressive Disorder, Major, Brain, Educational Status, Humans, Magnetic Resonance Imaging, Frontal Lobe
Abstract

Brain structural abnormalities and low educational attainment are consistently associated with major depressive disorder (MDD), yet there has been little research investigating the complex interaction of these factors. Brain structural alterations may represent a vulnerability or differential susceptibility marker, and in the context of low educational attainment, predict MDD. We tested this moderation model in a large multisite sample of 1958 adults with MDD and 2921 controls (aged 18 to 86) from the ENIGMA MDD working group. Using generalized linear mixed models and within-sample split-half replication, we tested whether brain structure interacted with educational attainment to predict MDD status. Analyses revealed that cortical thickness in a number of occipital, parietal, and frontal regions significantly interacted with education to predict MDD. For the majority of regions, models suggested a differential susceptibility effect, whereby thicker cortex was more likely to predict MDD in individuals with low educational attainment, but less likely to predict MDD in individuals with high educational attainment. Findings suggest that greater thickness of brain regions subserving visuomotor and social-cognitive functions confers susceptibility to MDD, dependent on level of educational attainment. Longitudinal work, however, is ultimately needed to establish whether cortical thickness represents a preexisting susceptibility marker. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Published
2022

23. Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis.

Author

Kennedy E, Liebel SW, Lindsey HM, Vadlamani S, Lei PW, Adamson MM, Alda M, Alonso-Lana S, Anderson TJ, Arango C, Asarnow RF, Avram M, Ayesa-Arriola R, Babikian T, Banaj N, Bird LJ, Borgwardt S, Brodtmann A, Brosch K, Caeyenberghs K, Calhoun VD, Chiaravalloti ND, Cifu DX, Crespo-Facorro B, Dalrymple-Alford JC, Dams-O'Connor K, Dannlowski U, Darby D, Davenport N, DeLuca J, Diaz-Caneja CM, Disner SG, Dobryakova E, Ehrlich S, Esopenko C, Ferrarelli F, Frank LE, Franz CE, Fuentes-Claramonte P, Genova H, Giza CC, Goltermann J, Grotegerd D, Gruber M, Gutierrez-Zotes A, Ha M, Haavik J, Hinkin C, Hoskinson KR, Hubl D, Irimia A, Jansen A, Kaess M, Kang X, Kenney K, Keřková B, Khlif MS, Kim M, Kindler J, Kircher T, Knížková K, Kolskår KK, Krch D, Kremen WS, Kuhn T, Kumari V, Kwon J, Langella R, Laskowitz S, Lee J, Lengenfelder J, Liou-Johnson V, Lippa SM, Løvstad M, Lundervold AJ, Marotta C, Marquardt CA, Mattos P, Mayeli A, McDonald CR, Meinert S, Melzer TR, Merchán-Naranjo J, Michel C, Morey RA, Mwangi B, Myall DJ, Nenadić I, Newsome MR, Nunes A, O'Brien T, Oertel V, Ollinger J, Olsen A, Ortiz García de la Foz V, Ozmen M, Pardoe H, Parent M, Piras F, Piras F, Pomarol-Clotet E, Repple J, Richard G, Rodriguez J, Rodriguez M, Rootes-Murdy K, Rowland J, Ryan NP, Salvador R, Sanders AM, Schmidt A, Soares JC, Spalleta G, Španiel F, Sponheim SR, Stasenko A, Stein F, Straube B, Thames A, Thomas-Odenthal F, Thomopoulos SI, Tone EB, Torres I, Troyanskaya M, Turner JA, Ulrichsen KM, Umpierrez G, Vecchio D, Vilella E, Vivash L, Walker WC, Werden E, Westlye LT, Wild K, Wroblewski A, Wu MJ, Wylie GR, Yatham LN, Zunta-Soares GB, Thompson PM, Pugh MJ, Tate DF, Hillary FG, Wilde EA, and Dennis EL

Abstract

Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15-90. The effects of dementia, mild cognitive impairment, Parkinson's disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia ( p < 0.001), while neither depression nor ADHD showed consistent associations with VLM scores ( p > 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders.

Published
2024
Full Text
View/download PDF

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

Author

Belov V, Erwin-Grabner T, Aghajani M, Aleman A, Amod AR, Basgoze Z, Benedetti F, Besteher B, Bülow R, Ching CRK, Connolly CG, Cullen K, Davey CG, Dima D, Dols A, Evans JW, Fu CHY, Gonul AS, Gotlib IH, Grabe HJ, Groenewold N, Hamilton JP, Harrison BJ, Ho TC, Mwangi B, Jaworska N, Jahanshad N, Klimes-Dougan B, Koopowitz SM, Lancaster T, Li M, Linden DEJ, MacMaster FP, Mehler DMA, Melloni E, Mueller BA, Ojha A, Oudega ML, Penninx BWJH, Poletti S, Pomarol-Clotet E, Portella MJ, Pozzi E, Reneman L, Sacchet MD, Sämann PG, Schrantee A, Sim K, Soares JC, Stein DJ, Thomopoulos SI, Uyar-Demir A, van der Wee NJA, van der Werff SJA, Völzke H, Whittle S, Wittfeld K, Wright MJ, Wu MJ, Yang TT, Zarate C, Veltman DJ, Schmaal L, Thompson PM, and Goya-Maldonado R

Subjects
Humans, Benchmarking, Brain diagnostic imaging, Neuroimaging methods, Machine Learning, Magnetic Resonance Imaging methods, Depressive Disorder, Major diagnostic imaging, Depressive Disorder, Major psychology
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 = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also 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 yield more encouraging prospects., (© 2024. The Author(s).)

Published
2024
Full Text
View/download PDF

25. Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization.

Author

Ge R, Yu Y, Qi YX, Fan YV, Chen S, Gao C, Haas SS, Modabbernia A, New F, Agartz I, Asherson P, Ayesa-Arriola R, Banaj N, Banaschewski T, Baumeister S, Bertolino A, Boomsma DI, Borgwardt S, Bourque J, Brandeis D, Breier A, Brodaty H, Brouwer RM, Buckner R, Buitelaar JK, Cannon DM, Caseras X, Cervenka S, Conrod PJ, Crespo-Facorro B, Crivello F, Crone EA, de Haan L, de Zubicaray GI, Di Giorgio A, Erk S, Fisher SE, Franke B, Frodl T, Glahn DC, Grotegerd D, Gruber O, Gruner P, Gur RE, Gur RC, Harrison BJ, Hatton SN, Hickie I, Howells FM, Pol HEH, Huyser C, Jernigan TL, Jiang J, Joska JA, Kahn RS, Kalnin AJ, Kochan NA, Koops S, Kuntsi J, Lagopoulos J, Lazaro L, Lebedeva IS, Lochner C, Martin NG, Mazoyer B, McDonald BC, McDonald C, McMahon KL, Nakao T, Nyberg L, Piras F, Portella MJ, Qiu J, Roffman JL, Sachdev PS, Sanford N, Satterthwaite TD, Saykin AJ, Schumann G, Sellgren CM, Sim K, Smoller JW, Soares J, Sommer IE, Spalletta G, Stein DJ, Tamnes CK, Thomopolous SI, Tomyshev AS, Tordesillas-Gutiérrez D, Trollor JN, van 't Ent D, van den Heuvel OA, van Erp TG, van Haren NE, Vecchio D, Veltman DJ, Walter H, Wang Y, Weber B, Wei D, Wen W, Westlye LT, Wierenga LM, Williams SC, Wright MJ, Medland S, Wu MJ, Yu K, Jahanshad N, Thompson PM, and Frangou S

Abstract

We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain (https://centilebrain.org/)., Competing Interests: Declaration of interests SSH is supported by NIH National Institute of Mental Health (T32MH122394), and received a travel award from the Society of Biological Psychiatry to attend the annual meeting in 2023. HB declares an institutional grant from the National Health and Medical Research Council; has received compensation for being on an advisory board or a consultant to Biogen, Eisai, Eli Lilly, Roche, and Skin2Neuron; payment for being on the Cranbrook Care Medical Advisory Board, and honoraria for being on the Montefiore Homes Clinical Advisory Board. RMB and HEHP declare partial funding through the Geestkracht programme of the Dutch Health Research Council (Zon-Mw, grant No 10-000-1001), and matching funds from participating pharmaceutical companies (Lundbeck, AstraZeneca, Eli Lilly, Janssen Cilag) and universities and mental health care organizations (Amsterdam: Academic Psychiatric Centre of the Academic Medical Center and the mental health institutions: GGZ Ingeest, Arkin, Dijk en Duin, GGZ Rivierduinen, Erasmus Medical Centre, GGZ Noord Holland Noord. Groningen: University Medical Center Groningen and the mental health institutions: Lentis, GGZ Friesland, GGZ Drenthe, Dimence, Mediant, GGNet Warnsveld, Yulius Dordrecht and Parnassia psycho-medical center The Hague. Maastricht: Maastricht University Medical Centre and the mental health institutions: GGzE, GGZ Breburg, GGZ Oost-Brabant, Vincent van Gogh voor Geestelijke Gezondheid, Mondriaan, Virenze riagg, Zuyderland GGZ, MET ggz, Universitair Centrum Sint-Jozef Kortenberg, CAPRI University of Antwerp, PC Ziekeren Sint-Truiden, PZ Sancta Maria Sint-Truiden, GGZ Overpelt, OPZ Rekem. Utrecht: University Medical Center Utrecht and the mental health institutions Altrecht, GGZ Centraal and Delta), Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO 51.02.061 to H.H., NWO 51.02.062 to D. B., NWO–NIHC Programs of excellence 433-09-220 to H.H., NWO-MagW 480-04-004 to D. B., and NWO/SPI 56-464-14192 to D.B.); FP7 Ideas: European Research Council (ERC-230374 to D. B.); and Universiteit Utrecht (High Potential Grant to H. H.). RB declares funding by NIH National Institute on Aging (R01AG067420); compensation for being on the scientific advisory board from Alkermes and Cognito Therapeutics with no conflict to the present work; honoraria from academic institutions for talks all under $1000 and $1000 for speaking at MGH/HMS course; travel fees for services to attend the annual meeting from the Simons Foundation; serves as a Director on the Simons Foundation collaborative initiative on aging (SCPAB); is a paid scientific advisory board member for philanthropic grants for The Foundation for OCD Research and the Klarman Family Foundation. BF has received educational speaking fees from Medice. DG reports funding from the NIH. UD is funded through the German Research Foundation (DFG; DA 1151/9- 1, DA 1151/10- 1, DA 1151/11- 1). GS declares funding from the European Commission, DFG, and NSFC. CKT has received grants from the Research Council of Norway and the Norwegian Regional Health Authority, unrelated to the current work. HW reports funding from the German Research Foundation (WA 1539/11-1). NJ reports funding from the NIH and compensation from the International Neuropsychological Society. PT declares a grant from the NIH and travel funded by NIH grants. All other authors declare no competing interests.

Published
2023
Full Text
View/download PDF

26. Bridging Big Data: Procedures for Combining Non-equivalent Cognitive Measures from the ENIGMA Consortium.

Author

Kennedy E, Vadlamani S, Lindsey HM, Lei PW, Jo-Pugh M, Adamson M, Alda M, Alonso-Lana S, Ambrogi S, Anderson TJ, Arango C, Asarnow RF, Avram M, Ayesa-Arriola R, Babikian T, Banaj N, Bird LJ, Borgwardt S, Brodtmann A, Brosch K, Caeyenberghs K, Calhoun VD, Chiaravalloti ND, Cifu DX, Crespo-Facorro B, Dalrymple-Alford JC, Dams-O'Connor K, Dannlowski U, Darby D, Davenport N, DeLuca J, Diaz-Caneja CM, Disner SG, Dobryakova E, Ehrlich S, Esopenko C, Ferrarelli F, Frank LE, Franz C, Fuentes-Claramonte P, Genova H, Giza CC, Goltermann J, Grotegerd D, Gruber M, Gutierrez-Zotes A, Ha M, Haavik J, Hinkin C, Hoskinson KR, Hubl D, Irimia A, Jansen A, Kaess M, Kang X, Kenney K, Keřková B, Khlif MS, Kim M, Kindler J, Kircher T, Knížková K, Kolskår KK, Krch D, Kremen WS, Kuhn T, Kumari V, Kwon JS, Langella R, Laskowitz S, Lee J, Lengenfelder J, Liebel SW, Liou-Johnson V, Lippa SM, Løvstad M, Lundervold A, Marotta C, Marquardt CA, Mattos P, Mayeli A, McDonald CR, Meinert S, Melzer TR, Merchán-Naranjo J, Michel C, Morey RA, Mwangi B, Myall DJ, Nenadić I, Newsome MR, Nunes A, O'Brien T, Oertel V, Ollinger J, Olsen A, de la Foz VOG, Ozmen M, Pardoe H, Parent M, Piras F, Piras F, Pomarol-Clotet E, Repple J, Richard G, Rodriguez J, Rodriguez M, Rootes-Murdy K, Rowland J, Ryan NP, Salvador R, Sanders AM, Schmidt A, Soares JC, Spalleta G, Španiel F, Stasenko A, Stein F, Straube B, Thames A, Thomas-Odenthal F, Thomopoulos SI, Tone E, Torres I, Troyanskaya M, Turner JA, Ulrichsen KM, Umpierrez G, Vilella E, Vivash L, Walker WC, Werden E, Westlye LT, Wild K, Wroblewski A, Wu MJ, Wylie GR, Yatham LN, Zunta-Soares GB, Thompson PM, Tate DF, Hillary FG, Dennis EL, and Wilde EA

Abstract

Investigators in neuroscience have turned to Big Data to address replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. These efforts unveil new questions about integrating data arising from distinct sources and instruments. We focus on the most frequently assessed cognitive domain - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated global raw data from 53 studies totaling N = 10,505 individuals. A mega-analysis was conducted using empirical bayes harmonization to remove site effects, followed by linear models adjusting for common covariates. A continuous item response theory (IRT) model estimated each individual's latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance while preserving covariate effects, and our conversion tool is freely available online. This demonstrates that large-scale data sharing and harmonization initiatives can address reproducibility and integration challenges across the behavioral sciences., Competing Interests: Competing Interest Statement: Dr. Arango has been a consultant to or has received honoraria or grants from Acadia, Angelini, Biogen, Boehringer, Gedeon Richter, Janssen Cilag, Lundbeck, Medscape, Menarini, Minerva, Otsuka, Pfizer, Roche, Sage, Servier, Shire, Schering Plough, Sumitomo Dainippon Pharma, Sunovion and Takeda. Dr. Brodtmann serves on the editorial boards of Neurology and International Journal of Stroke. Dr. Diaz-Caneja has received honoraria from Exeltis and Angelinii. Dr. Giza: consultant for NBA, NFL, NHLPA, Los Angeles Lakers; Advisory Board: Highmark Interactive, Novartis, MLS, NBA, USSF; Medicolegal 1–2 cases annually. Dr. Soares: ALKERMES (Research Grant), ALLERGAN (Research Grant), ASOFARMA (Consultant), ATAI (Stock), BOEHRINGER Ingelheim (Consultant), COMPASS (Research Grant), JOHNSON & JOHNSON (Consultant), LIVANOVA (Consultant), PFIZER (Consultant), PULVINAR NEURO LLC (Consultant), RELMADA (Consultant), SANOFI (Consultant), SUNOVIAN (Consultant). Dr. Thompson received partial research support from Biogen, Inc., for research unrelated to this manuscript. Dr. Yatham has been on speaker or advisory boards for, or has received research grants from, Alkermes, Abbvie, Canadian Institutes of Health Research, Sumitomo Dainippon Pharma, GlaxoSmithKline, Intracellular Therapies, Merck, Sanofi, Sequiris, Servier, and Sunovion, over the past 3 years, all outside this work. The collection of this cohort was partially supported by an investigator-initiated research grant from Biogen (US). Biogen had no role in the analysis or writing of this manuscript. Eisai (JP) and Life Molecular Imaging for research unrelated to this manuscript. Dr. Wylie has received research support from the NJ Commission for brain injury research, from the Dept of Veterans’ Affairs, from Biogen, from Bristol, Myers, Squibb, from Genetech, and has served on advisory boards for the CDMRP and the VA. All of these activities are unrelated to this research. The views expressed in this article are those of the author(s) and do not reflect the official policy of the Department of Army/Navy/Air Force, Department of Defense, or U.S. Government.

Published
2023
Full Text
View/download PDF

27. Mega-analysis methods in ENIGMA: The experience of the generalized anxiety disorder working group.

Author

Zugman A, Harrewijn A, Cardinale EM, Zwiebel H, Freitag GF, Werwath KE, Bas-Hoogendam JM, Groenewold NA, Aghajani M, Hilbert K, Cardoner N, Porta-Casteràs D, Gosnell S, Salas R, Blair KS, Blair JR, Hammoud MZ, Milad M, Burkhouse K, Phan KL, Schroeder HK, Strawn JR, Beesdo-Baum K, Thomopoulos SI, Grabe HJ, Van der Auwera S, Wittfeld K, Nielsen JA, Buckner R, Smoller JW, Mwangi B, Soares JC, Wu MJ, Zunta-Soares GB, Jackowski AP, Pan PM, Salum GA, Assaf M, Diefenbach GJ, Brambilla P, Maggioni E, Hofmann D, Straube T, Andreescu C, Berta R, Tamburo E, Price R, Manfro GG, Critchley HD, Makovac E, Mancini M, Meeten F, Ottaviani C, Agosta F, Canu E, Cividini C, Filippi M, Kostić M, Munjiza A, Filippi CA, Leibenluft E, Alberton BAV, Balderston NL, Ernst M, Grillon C, Mujica-Parodi LR, van Nieuwenhuizen H, Fonzo GA, Paulus MP, Stein MB, Gur RE, Gur RC, Kaczkurkin AN, Larsen B, Satterthwaite TD, Harper J, Myers M, Perino MT, Yu Q, Sylvester CM, Veltman DJ, Lueken U, Van der Wee NJA, Stein DJ, Jahanshad N, Thompson PM, Pine DS, and Winkler AM

Subjects
Humans, Anxiety Disorders diagnostic imaging, Cerebral Cortex diagnostic imaging, Data Interpretation, Statistical, Meta-Analysis as Topic, Multicenter Studies as Topic methods, Multicenter Studies as Topic standards, Neuroimaging methods, Neuroimaging standards
Abstract

The ENIGMA group on Generalized Anxiety Disorder (ENIGMA-Anxiety/GAD) is part of a broader effort to investigate anxiety disorders using imaging and genetic data across multiple sites worldwide. The group is actively conducting a mega-analysis of a large number of brain structural scans. In this process, the group was confronted with many methodological challenges related to study planning and implementation, between-country transfer of subject-level data, quality control of a considerable amount of imaging data, and choices related to statistical methods and efficient use of resources. This report summarizes the background information and rationale for the various methodological decisions, as well as the approach taken to implement them. The goal is to document the approach and help guide other research groups working with large brain imaging data sets as they develop their own analytic pipelines for mega-analyses., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Published
2022
Full Text
View/download PDF

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

Author

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

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

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

Published
2022
Full Text
View/download PDF

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

Author

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

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

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

Published
2022
Full Text
View/download PDF

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

Author

Tahmasian M, Aleman A, Andreassen OA, Arab Z, Baillet M, Benedetti F, Bresser T, Bright J, Chee MWL, Chylinski D, Cheng W, Deantoni M, Dresler M, Eickhoff SB, Eickhoff CR, Elvsåshagen T, Feng J, Foster-Dingley JC, Ganjgahi H, Grabe HJ, Groenewold NA, Ho TC, Bong Hong S, Houenou J, Irungu B, Jahanshad N, Khazaie H, Kim H, Koshmanova E, Kocevska D, Kochunov P, Lakbila-Kamal O, Leerssen J, Li M, Luik AI, Muto V, Narbutas J, Nilsonne G, O'Callaghan VS, Olsen A, Osorio RS, Poletti S, Poudel G, Reesen JE, Reneman L, Reyt M, Riemann D, Rosenzweig I, Rostampour M, Saberi A, Schiel J, Schmidt C, Schrantee A, Sciberras E, Silk TJ, Sim K, Smevik H, Soares JC, Spiegelhalder K, Stein DJ, Talwar P, Tamm S, Teresi GL, Valk SL, Van Someren E, Vandewalle G, Van Egroo M, Völzke H, Walter M, Wassing R, Weber FD, Weihs A, Westlye LT, Wright MJ, Wu MJ, Zak N, and Zarei M

Subjects
Humans, Neuroimaging, Sample Size, Sleep Deprivation, Brain diagnostic imaging
Abstract

Neuroimaging and genetics studies have advanced our understanding of the neurobiology of sleep and its disorders. However, individual studies usually have limitations to identifying consistent and reproducible effects, including modest sample sizes, heterogeneous clinical characteristics and varied methodologies. These issues call for a large-scale multi-centre effort in sleep research, in order to increase the number of samples, and harmonize the methods of data collection, preprocessing and analysis using pre-registered well-established protocols. The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium provides a powerful collaborative framework for combining datasets across individual sites. Recently, we have launched the ENIGMA-Sleep working group with the collaboration of several institutes from 15 countries to perform large-scale worldwide neuroimaging and genetics studies for better understanding the neurobiology of impaired sleep quality in population-based healthy individuals, the neural consequences of sleep deprivation, pathophysiology of sleep disorders, as well as neural correlates of sleep disturbances across various neuropsychiatric disorders. In this introductory review, we describe the details of our currently available datasets and our ongoing projects in the ENIGMA-Sleep group, and discuss both the potential challenges and opportunities of a collaborative initiative in sleep medicine., (© 2021 European Sleep Research Society.)

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results