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1. Brain aging patterns in a large and diverse cohort of 49,482 individuals.

Author

Yang, Zhijian, Wen, Junhao, Erus, Guray, Govindarajan, Sindhuja, Melhem, Randa, Mamourian, Elizabeth, Cui, Yuhan, Srinivasan, Dhivya, Abdulkadir, Ahmed, Parmpi, Paraskevi, Wittfeld, Katharina, Grabe, Hans, Bülow, Robin, Frenzel, Stefan, Tosun, Duygu, Bilgel, Murat, An, Yang, Yi, Dahyun, Marcus, Daniel, LaMontagne, Pamela, Benzinger, Tammie, Heckbert, Susan, Austin, Thomas, Waldstein, Shari, Evans, Michele, Zonderman, Alan, Launer, Lenore, Sotiras, Aristeidis, Espeland, Mark, Masters, Colin, Maruff, Paul, Fripp, Jurgen, Toga, Arthur, OBryant, Sid, Chakravarty, Mallar, Villeneuve, Sylvia, Johnson, Sterling, Morris, John, Albert, Marilyn, Yaffe, Kristine, Völzke, Henry, Ferrucci, Luigi, Nick Bryan, R, Shinohara, Russell, Fan, Yong, Habes, Mohamad, Lalousis, Paris, Koutsouleris, Nikolaos, Wolk, David, Resnick, Susan, Shou, Haochang, Nasrallah, Ilya, and Davatzikos, Christos

Subjects
Humans, Brain, Aging, Magnetic Resonance Imaging, Male, Female, Aged, Cohort Studies, Middle Aged, Atrophy, Life Style, Adult, Aged, 80 and over
Abstract

Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis.

Published
2024

2. TotalVibeSegmentator: Full Body MRI Segmentation for the NAKO and UK Biobank

Author

Graf, Robert, Platzek, Paul-Sören, Riedel, Evamaria Olga, Ramschütz, Constanze, Starck, Sophie, Möller, Hendrik Kristian, Atad, Matan, Völzke, Henry, Bülow, Robin, Schmidt, Carsten Oliver, Rüdebusch, Julia, Jung, Matthias, Reisert, Marco, Weiss, Jakob, Löffler, Maximilian, Bamberg, Fabian, Wiestler, Bene, Paetzold, Johannes C., Rueckert, Daniel, and Kirschke, Jan Stefan

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Objectives: To present a publicly available torso segmentation network for large epidemiology datasets on volumetric interpolated breath-hold examination (VIBE) images. Materials & Methods: We extracted preliminary segmentations from TotalSegmentator, spine, and body composition networks for VIBE images, then improved them iteratively and retrained a nnUNet network. Using subsets of NAKO (85 subjects) and UK Biobank (16 subjects), we evaluated with Dice-score on a holdout set (12 subjects) and existing organ segmentation approach (1000 subjects), generating 71 semantic segmentation types for VIBE images. We provide an additional network for the vertebra segments 22 individual vertebra types. Results: We achieved an average Dice score of 0.89 +- 0.07 overall 71 segmentation labels. We scored > 0.90 Dice-score on the abdominal organs except for the pancreas with a Dice of 0.70. Conclusion: Our work offers a detailed and refined publicly available full torso segmentation on VIBE images., Comment: https://github.com/robert-graf/TotalVibeSegmentator

Published
2024

3. Generating synthetic high-resolution spinal STIR and T1w images from T2w FSE and low-resolution axial Dixon

Author

Graf, Robert, Platzek, Paul-Sören, Riedel, Evamaria Olga, Kim, Su Hwan, Lenhart, Nicolas, Ramschütz, Constanze, Paprottka, Karolin Johanna, Kertels, Olivia Ruriko, Möller, Hendrik Kristian, Atad, Matan, Bülow, Robin, Werner, Nicole, Völzke, Henry, Schmidt, Carsten Oliver, Wiestler, Benedikt, Paetzold, Johannes C., Rueckert, Daniel, and Kirschke, Jan Stefan

Published
2024
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4. Genetic variants for head size share genes and pathways with cancer

Author

Knol, Maria J, Poot, Raymond A, Evans, Tavia E, Satizabal, Claudia L, Mishra, Aniket, Sargurupremraj, Muralidharan, van der Auwera, Sandra, Duperron, Marie-Gabrielle, Jian, Xueqiu, Hostettler, Isabel C, van Dam-Nolen, Dianne HK, Lamballais, Sander, Pawlak, Mikolaj A, Lewis, Cora E, Carrion-Castillo, Amaia, van Erp, Theo GM, Reinbold, Céline S, Shin, Jean, Scholz, Markus, Håberg, Asta K, Kämpe, Anders, Li, Gloria HY, Avinun, Reut, Atkins, Joshua R, Hsu, Fang-Chi, Amod, Alyssa R, Lam, Max, Tsuchida, Ami, Teunissen, Mariël WA, Aygün, Nil, Patel, Yash, Liang, Dan, Beiser, Alexa S, Beyer, Frauke, Bis, Joshua C, Bos, Daniel, Bryan, R Nick, Bülow, Robin, Caspers, Svenja, Catheline, Gwenaëlle, Cecil, Charlotte AM, Dalvie, Shareefa, Dartigues, Jean-François, DeCarli, Charles, Enlund-Cerullo, Maria, Ford, Judith M, Franke, Barbara, Freedman, Barry I, Friedrich, Nele, Green, Melissa J, Haworth, Simon, Helmer, Catherine, Hoffmann, Per, Homuth, Georg, Ikram, M Kamran, Jack, Clifford R, Jahanshad, Neda, Jockwitz, Christiane, Kamatani, Yoichiro, Knodt, Annchen R, Li, Shuo, Lim, Keane, Longstreth, WT, Macciardi, Fabio, Consortium, The Cohorts for Heart and Aging Research in Genomic Epidemiology, Amouyel, Philippe, Arfanakis, Konstantinos, Aribisala, Benjamin S, Bastin, Mark E, Chauhan, Ganesh, Chen, Christopher, Cheng, Ching-Yu, de Jager, Philip L, Deary, Ian J, Fleischman, Debra A, Gottesman, Rebecca F, Gudnason, Vilmundur, Hilal, Saima, Hofer, Edith, Janowitz, Deborah, Jukema, J Wouter, Liewald, David CM, Lopez, Lorna M, Lopez, Oscar, Luciano, Michelle, Martinez, Oliver, Niessen, Wiro J, Nyquist, Paul, Rotter, Jerome I, Rundek, Tatjana, Sacco, Ralph L, Schmidt, Helena, Tiemeier, Henning, Trompet, Stella, van der Grond, Jeroen, Völzke, Henry, Wardlaw, Joanna M, Yanek, Lisa, Yang, Jingyun, and Consortium, The Enhancing NeuroImaging Genetics through Meta-Analysis

Subjects
Biomedical and Clinical Sciences, Biotechnology, Genetics, Cancer, Human Genome, Stem Cell Research, Neurosciences, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Neurological, Humans, Genome-Wide Association Study, Head, Neoplasms, Female, Male, Polymorphism, Single Nucleotide, Genetic Variation, Organ Size, Signal Transduction, Adult, Genetic Predisposition to Disease, Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium, cancer, genetics, genome-wide association study, head circumference, head size, intracranial volume, meta-analysis, Biomedical and clinical sciences
Abstract

The size of the human head is highly heritable, but genetic drivers of its variation within the general population remain unmapped. We perform a genome-wide association study on head size (N = 80,890) and identify 67 genetic loci, of which 50 are novel. Neuroimaging studies show that 17 variants affect specific brain areas, but most have widespread effects. Gene set enrichment is observed for various cancers and the p53, Wnt, and ErbB signaling pathways. Genes harboring lead variants are enriched for macrocephaly syndrome genes (37-fold) and high-fidelity cancer genes (9-fold), which is not seen for human height variants. Head size variants are also near genes preferentially expressed in intermediate progenitor cells, neural cells linked to evolutionary brain expansion. Our results indicate that genes regulating early brain and cranial growth incline to neoplasia later in life, irrespective of height. This warrants investigation of clinical implications of the link between head size and cancer.

Published
2024

5. 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

6. Genetic risk factors underlying white matter hyperintensities and cortical atrophy

Author

Patel, Yash, Shin, Jean, Sliz, Eeva, Tang, Ariana, Mishra, Aniket, Xia, Rui, Hofer, Edith, Rajula, Hema Sekhar Reddy, Wang, Ruiqi, Beyer, Frauke, Horn, Katrin, Riedl, Max, Yu, Jing, Völzke, Henry, Bülow, Robin, Völker, Uwe, Frenzel, Stefan, Wittfeld, Katharina, Van der Auwera, Sandra, Mosley, Thomas H., Bouteloup, Vincent, Lambert, Jean-Charles, Chêne, Geneviève, Dufouil, Carole, Tzourio, Christophe, Mangin, Jean-François, Gottesman, Rebecca F., Fornage, Myriam, Schmidt, Reinhold, Yang, Qiong, Witte, Veronica, Scholz, Markus, Loeffler, Markus, Roshchupkin, Gennady V., Ikram, M. Arfan, Grabe, Hans J., Seshadri, Sudha, Debette, Stephanie, Paus, Tomas, and Pausova, Zdenka

Published
2024
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8. Prednisolone Versus Colchicine for Acute Gout in Primary Care: statistical analysis plan for the pragmatic, multicenter, randomized, and double-blinded COPAGO non-inferiority trial

Author

Richter, Adrian, Truthmann, Julia, Hummers, Eva, Pereira, Julia Freyer Martins, Gágyor, Ildikó, Schuster, Franziska, Witte, Amelie, Böhm, Susanne, Greser, Alexandra, Kamin, Petra, Stracke, Sylvia, Dörr, Marcus, Bülow, Robin, Engeli, Stefan, Chenot, Jean François, and Ittermann, Till

Published
2024
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9. 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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10. 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

12. 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

13. Genetic and clinical determinants of abdominal aortic diameter: genome-wide association studies, exome array data and Mendelian randomization study

Author

Portilla-Fernandez, Eliana, Klarin, Derek, Hwang, Shih-Jen, Biggs, Mary L, Bis, Joshua C, Weiss, Stefan, Rospleszcz, Susanne, Natarajan, Pradeep, Hoffmann, Udo, Rogers, Ian S, Truong, Quynh A, Völker, Uwe, Dörr, Marcus, Bülow, Robin, Criqui, Michael H, Allison, Matthew, Ganesh, Santhi K, Yao, Jie, Waldenberger, Melanie, Bamberg, Fabian, Rice, Kenneth M, Essers, Jeroen, Kapteijn, Daniek MC, van der Laan, Sander W, de Knegt, Rob J, Ghanbari, Mohsen, Felix, Janine F, Ikram, M Arfan, Kavousi, Maryam, Uitterlinden, Andre G, Roks, Anton JM, Danser, AH Jan, Tsao, Philip S, Damrauer, Scott M, Guo, Xiuqing, Rotter, Jerome I, Psaty, Bruce M, Kathiresan, Sekar, Völzke, Henry, Peters, Annette, Johnson, Craig, Strauch, Konstantin, Meitinger, Thomas, O’Donnell, Christopher J, Dehghan, Abbas, and Program, VA Million Veteran

Subjects
Prevention, Human Genome, Cardiovascular, Genetics, Clinical Research, 2.1 Biological and endogenous factors, Aetiology, Exome, Genome-Wide Association Study, Humans, Mendelian Randomization Analysis, Polymorphism, Single Nucleotide, Triglycerides, VA Million Veteran Program, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

Progressive dilation of the infrarenal aortic diameter is a consequence of the ageing process and is considered the main determinant of abdominal aortic aneurysm (AAA). We aimed to investigate the genetic and clinical determinants of abdominal aortic diameter (AAD). We conducted a meta-analysis of genome-wide association studies in 10 cohorts (n = 13 542) imputed to the 1000 Genome Project reference panel including 12 815 subjects in the discovery phase and 727 subjects [Partners Biobank cohort 1 (PBIO)] as replication. Maximum anterior-posterior diameter of the infrarenal aorta was used as AAD. We also included exome array data (n = 14 480) from seven epidemiologic studies. Single-variant and gene-based associations were done using SeqMeta package. A Mendelian randomization analysis was applied to investigate the causal effect of a number of clinical risk factors on AAD. In genome-wide association study (GWAS) on AAD, rs74448815 in the intronic region of LDLRAD4 reached genome-wide significance (beta = -0.02, SE = 0.004, P-value = 2.10 × 10-8). The association replicated in the PBIO1 cohort (P-value = 8.19 × 10-4). In exome-array single-variant analysis (P-value threshold = 9 × 10-7), the lowest P-value was found for rs239259 located in SLC22A20 (beta = 0.007, P-value = 1.2 × 10-5). In the gene-based analysis (P-value threshold = 1.85 × 10-6), PCSK5 showed an association with AAD (P-value = 8.03 × 10-7). Furthermore, in Mendelian randomization analyses, we found evidence for genetic association of pulse pressure (beta = -0.003, P-value = 0.02), triglycerides (beta = -0.16, P-value = 0.008) and height (beta = 0.03, P-value

Published
2022

15. Sphingosine-1-phosphate levels are inversely associated with left ventricular and atrial chamber volume and cardiac mass in men: The Study of Health in Pomerania (SHIP)

Author

Schielke, Jan, Ittermann, Till, Groß, Stefan, Moritz, Eileen, Nauck, Matthias, Friedrich, Nele, Schwedhelm, Edzard, Rauch, Bernhard H., Völzke, Henry, Bülow, Robin, Chamling, Bishwas, Felix, Stephan Burkhard, Bahls, Martin, Dörr, Marcus, and Markus, Marcello Ricardo Paulista

Published
2023
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17. 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 HD, 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, and Lawrie, Stephen

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Neurosciences, Genetics, Mental Health, Serious Mental Illness, Mental Illness, Women's Health, Brain Disorders, Preterm, Low Birth Weight and Health of the Newborn, Pregnancy, Pediatric, Perinatal Period - Conditions Originating in Perinatal Period, Behavioral and Social Science, Prevention, Schizophrenia, 2.1 Biological and endogenous factors, 2.3 Psychological, social and economic factors, Neurological, Reproductive health and childbirth, Mental health, Good Health and Well Being, Attention Deficit Disorder with Hyperactivity, Autism Spectrum Disorder, Bipolar Disorder, Cerebral Cortex, Child, Depressive Disorder, Major, Female, Humans, Infant, Newborn, Magnetic Resonance Imaging, Premature Birth, Cortical growth, Cortical surface area, Mental illness, Neurodevelopment, Neurogenesis, Psychiatric disorders, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biological sciences, Biomedical and clinical sciences
Abstract

BackgroundMorphology of the human cerebral cortex differs across psychiatric disorders, with neurobiology and developmental origins mostly undetermined. Deviations in the tangential growth of the cerebral cortex during pre/perinatal periods may be reflected in individual variations in cortical surface area later in life.MethodsInterregional profiles of group differences in surface area between cases and controls were generated using T1-weighted magnetic resonance imaging from 27,359 individuals including those with attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia, and high general psychopathology (through the Child Behavior Checklist). Similarity of interregional profiles of group differences in surface area and prenatal cell-specific gene expression was assessed.ResultsAcross the 11 cortical regions, group differences in cortical area for attention-deficit/hyperactivity disorder, schizophrenia, and Child Behavior Checklist were dominant in multimodal association cortices. The same interregional profiles were also associated with interregional profiles of (prenatal) gene expression specific to proliferative cells, namely radial glia and intermediate progenitor cells (greater expression, larger difference), as well as differentiated cells, namely excitatory neurons and endothelial and mural cells (greater expression, smaller difference). Finally, these cell types were implicated in known pre/perinatal risk factors for psychosis. Genes coexpressed with radial glia were enriched with genes implicated in congenital abnormalities, birth weight, hypoxia, and starvation. Genes coexpressed with endothelial and mural genes were enriched with genes associated with maternal hypertension and preterm birth.ConclusionsOur findings support a neurodevelopmental model of vulnerability to mental illness whereby prenatal risk factors acting through cell-specific processes lead to deviations from typical brain development during pregnancy.

Published
2022

18. Gene-mapping study of extremes of cerebral small vessel disease reveals TRIM47 as a strong candidate

Author

Mishra, Aniket, Duplaà, Cécile, Vojinovic, Dina, Suzuki, Hideaki, Sargurupremraj, Muralidharan, Zilhão, Nuno R, Li, Shuo, Bartz, Traci M, Jian, Xueqiu, Zhao, Wei, Hofer, Edith, Wittfeld, Katharina, Harris, Sarah E, van der Auwera-Palitschka, Sandra, Luciano, Michelle, Bis, Joshua C, Adams, Hieab HH, Satizabal, Claudia L, Gottesman, Rebecca F, Gampawar, Piyush G, Bülow, Robin, Weiss, Stefan, Yu, Miao, Bastin, Mark E, Lopez, Oscar L, Vernooij, Meike W, Beiser, Alexa S, Völker, Uwe, Kacprowski, Tim, Soumare, Aicha, Smith, Jennifer A, Knopman, David S, Morris, Zoe, Zhu, Yicheng, Rotter, Jerome I, Dufouil, Carole, Hernández, Maria Valdés, Maniega, Susana Muñoz, Lathrop, Mark, Boerwinkle, Erik, Schmidt, Reinhold, Ihara, Masafumi, Mazoyer, Bernard, Yang, Qiong, Joutel, Anne, Tournier-Lasserve, Elizabeth, Launer, Lenore J, Deary, Ian J, Mosley, Thomas H, Amouyel, Philippe, DeCarli, Charles S, Psaty, Bruce M, Tzourio, Christophe, Kardia, Sharon LR, Grabe, Hans J, Teumer, Alexander, van Duijn, Cornelia M, Schmidt, Helena, Wardlaw, Joanna M, Ikram, M Arfan, Fornage, Myriam, Gudnason, Vilmundur, Seshadri, Sudha, Matthews, Paul M, Longstreth, William T, Couffinhal, Thierry, and Debette, Stephanie

Subjects
Epidemiology, Health Sciences, Acquired Cognitive Impairment, Genetics, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Brain Disorders, Stroke, Neurosciences, Human Genome, Biotechnology, Clinical Research, Dementia, Aging, 2.1 Biological and endogenous factors, Aetiology, Neurological, Cardiovascular, Animals, Brain Ischemia, Cerebral Small Vessel Diseases, Endothelial Cells, Genome-Wide Association Study, Mice, cerebral small vessel disease, endothelial cells, GWAS, TRIM47, whole-exome association study, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences, Psychology
Abstract

Cerebral small vessel disease is a leading cause of stroke and a major contributor to cognitive decline and dementia, but our understanding of specific genes underlying the cause of sporadic cerebral small vessel disease is limited. We report a genome-wide association study and a whole-exome association study on a composite extreme phenotype of cerebral small vessel disease derived from its most common MRI features: white matter hyperintensities and lacunes. Seventeen population-based cohorts of older persons with MRI measurements and genome-wide genotyping (n = 41 326), whole-exome sequencing (n = 15 965), or exome chip (n = 5249) data contributed 13 776 and 7079 extreme small vessel disease samples for the genome-wide association study and whole-exome association study, respectively. The genome-wide association study identified significant association of common variants in 11 loci with extreme small vessel disease, of which the chr12q24.11 locus was not previously reported to be associated with any MRI marker of cerebral small vessel disease. The whole-exome association study identified significant associations of extreme small vessel disease with common variants in the 5' UTR region of EFEMP1 (chr2p16.1) and one probably damaging common missense variant in TRIM47 (chr17q25.1). Mendelian randomization supports the causal association of extensive small vessel disease severity with increased risk of stroke and Alzheimer's disease. Combined evidence from summary-based Mendelian randomization studies and profiling of human loss-of-function allele carriers showed an inverse relation between TRIM47 expression in the brain and blood vessels and extensive small vessel disease severity. We observed significant enrichment of Trim47 in isolated brain vessel preparations compared to total brain fraction in mice, in line with the literature showing Trim47 enrichment in brain endothelial cells at single cell level. Functional evaluation of TRIM47 by small interfering RNAs-mediated knockdown in human brain endothelial cells showed increased endothelial permeability, an important hallmark of cerebral small vessel disease pathology. Overall, our comprehensive gene-mapping study and preliminary functional evaluation suggests a putative role of TRIM47 in the pathophysiology of cerebral small vessel disease, making it an important candidate for extensive in vivo explorations and future translational work.

Published
2022

19. Cardiovascular Magnetic Resonance Reference Ranges From the Healthy Hearts Consortium

Author

Raisi-Estabragh, Zahra, Szabo, Liliana, McCracken, Celeste, Bülow, Robin, Aquaro, Giovanni Donato, Andre, Florian, Le, Thu-Thao, Suchá, Dominika, Condurache, Dorina-Gabriela, Salih, Ahmed M., Chadalavada, Sucharitha, Aung, Nay, Lee, Aaron Mark, Harvey, Nicholas C., Leiner, Tim, Chin, Calvin W.L., Friedrich, Matthias G., Barison, Andrea, Dörr, Marcus, and Petersen, Steffen E.

Published
2024
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20. Automated image quality assessment for selecting among multiple magnetic resonance image acquisitions in the German National Cohort study

Author

Schuppert, Christopher, Rospleszcz, Susanne, Hirsch, Jochen G., Hoinkiss, Daniel C., Köhn, Alexander, von Krüchten, Ricarda, Russe, Maximilian F., Keil, Thomas, Krist, Lilian, Schmidt, Börge, Michels, Karin B., Schipf, Sabine, Brenner, Hermann, Kröncke, Thomas J., Pischon, Tobias, Niendorf, Thoralf, Schulz-Menger, Jeanette, Forsting, Michael, Völzke, Henry, Hosten, Norbert, Bülow, Robin, Zaitsev, Maxim, Kauczor, Hans-Ulrich, Bamberg, Fabian, Günther, Matthias, and Schlett, Christopher L.

Published
2023
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21. Prednisolone Versus Colchicine for Acute Gout in Primary Care (COPAGO): protocol for a two-arm multicentre, pragmatic, prospective, randomized, double-blind, controlled clinical trial of prednisolone and colchicine for non-inferiority with a parallel group design

Author

Truthmann, Julia, Freyer Martins Pereira, Julia, Richter, Adrian, Schuster, Franziska, Witte, Amelie, Böhm, Susanne, Greser, Alexandra, Kamin, Petra, Stracke, Sylvia, Dörr, Marcus, Bülow, Robin, Engeli, Stefan, Gágyor, Ildikó, Hummers, Eva, and Chenot, Jean-François

Published
2023
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22. Genetic variants associated with longitudinal changes in brain structure across the lifespan

Author

Brouwer, Rachel M, Klein, Marieke, Grasby, Katrina L, Schnack, Hugo G, Jahanshad, Neda, Teeuw, Jalmar, Thomopoulos, Sophia I, Sprooten, Emma, Franz, Carol E, Gogtay, Nitin, Kremen, William S, Panizzon, Matthew S, Olde Loohuis, Loes M, Whelan, Christopher D, Aghajani, Moji, Alloza, Clara, Alnæs, Dag, Artiges, Eric, Ayesa-Arriola, Rosa, Barker, Gareth J, Bastin, Mark E, Blok, Elisabet, Bøen, Erlend, Breukelaar, Isabella A, Bright, Joanna K, Buimer, Elizabeth EL, Bülow, Robin, Cannon, Dara M, Ciufolini, Simone, Crossley, Nicolas A, Damatac, Christienne G, Dazzan, Paola, de Mol, Casper L, de Zwarte, Sonja MC, Desrivières, Sylvane, Díaz-Caneja, Covadonga M, Doan, Nhat Trung, Dohm, Katharina, Fröhner, Juliane H, Goltermann, Janik, Grigis, Antoine, Grotegerd, Dominik, Han, Laura KM, Harris, Mathew A, Hartman, Catharina A, Heany, Sarah J, Heindel, Walter, Heslenfeld, Dirk J, Hohmann, Sarah, Ittermann, Bernd, Jansen, Philip R, Janssen, Joost, Jia, Tianye, Jiang, Jiyang, Jockwitz, Christiane, Karali, Temmuz, Keeser, Daniel, Koevoets, Martijn GJC, Lenroot, Rhoshel K, Malchow, Berend, Mandl, René CW, Medel, Vicente, Meinert, Susanne, Morgan, Catherine A, Mühleisen, Thomas W, Nabulsi, Leila, Opel, Nils, de la Foz, Víctor Ortiz-García, Overs, Bronwyn J, Paillère Martinot, Marie-Laure, Redlich, Ronny, Marques, Tiago Reis, Repple, Jonathan, Roberts, Gloria, Roshchupkin, Gennady V, Setiaman, Nikita, Shumskaya, Elena, Stein, Frederike, Sudre, Gustavo, Takahashi, Shun, Thalamuthu, Anbupalam, Tordesillas-Gutiérrez, Diana, van der Lugt, Aad, van Haren, Neeltje EM, Wardlaw, Joanna M, Wen, Wei, Westeneng, Henk-Jan, Wittfeld, Katharina, Zhu, Alyssa H, Zugman, Andre, Armstrong, Nicola J, Bonfiglio, Gaia, Bralten, Janita, Dalvie, Shareefa, Davies, Gail, Di Forti, Marta, Ding, Linda, Donohoe, Gary, Forstner, Andreas J, and Gonzalez-Peñas, Javier

Subjects
Biological Psychology, Psychology, Genetics, Biomedical Imaging, Mental Health, Biotechnology, Human Genome, Prevention, Aging, Brain Disorders, Neurosciences, 2.1 Biological and endogenous factors, Aetiology, Mental health, Neurological, Brain, Genome-Wide Association Study, Humans, Longevity, Magnetic Resonance Imaging, IMAGEN Consortium, Cognitive Sciences, Neurology & Neurosurgery, Biological psychology
Abstract

Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging.

Published
2022

23. An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling

Author

Hahn, Tim, Ernsting, Jan, Winter, Nils R., Holstein, Vincent, Leenings, Ramona, Beisemann, Marie, Fisch, Lukas, Sarink, Kelvin, Emden, Daniel, Opel, Nils, Redlich, Ronny, Repple, Jonathan, Grotegerd, Dominik, Meinert, Susanne, Hirsch, Jochen G., Niendorf, Thoralf, Endemann, Beate, Bamberg, Fabian, Kröncke, Thomas, Bülow, Robin, Völzke, Henry, von Stackelberg, Oyunbileg, Sowade, Ramona Felizitas, Umutlu, Lale, Schmidt, Börge, Caspers, Svenja, Consortium, German National Cohort Study Center, Kugel, Harald, Kircher, Tilo, Risse, Benjamin, Gaser, Christian, Cole, James H., Dannlowski, Udo, and Berger, Klaus

Subjects
Computer Science - Machine Learning, Quantitative Biology - Populations and Evolution
Abstract

The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk-marker of cross-disorder brain changes, growing into a cornerstone of biological age-research. However, Machine Learning models underlying the field do not consider uncertainty, thereby confounding results with training data density and variability. Also, existing models are commonly based on homogeneous training sets, often not independently validated, and cannot be shared due to data protection issues. Here, we introduce an uncertainty-aware, shareable, and transparent Monte-Carlo Dropout Composite-Quantile-Regression (MCCQR) Neural Network trained on N=10,691 datasets from the German National Cohort. The MCCQR model provides robust, distribution-free uncertainty quantification in high-dimensional neuroimaging data, achieving lower error rates compared to existing models across ten recruitment centers and in three independent validation samples (N=4,004). In two examples, we demonstrate that it prevents spurious associations and increases power to detect accelerated brain-aging. We make the pre-trained model publicly available.

Published
2021

26. Associations of Pulmonary Function with MRI Brain Volumes: A Coordinated Multi-Study Analysis

Author

Frenzel, Stefan, Bis, Joshua C, Gudmundsson, Elias F, O’Donnell, Adrienne, Simino, Jeannette, Yaqub, Amber, Bartz, Traci M, Brusselle, Guy GO, Bülow, Robin, DeCarli, Charles S, Ewert, Ralf, Gharib, Sina A, Ghosh, Saptaparni, Gireud-Goss, Monica, Gottesman, Rebecca F, Ikram, M Arfan, Knopman, David S, Launer, Lenore J, London, Stephanie J, Longstreth, WT, Lopez, Oscar L, van Lent, Debora Melo, O’Connor, George, Satizabal, Claudia L, Shrestha, Srishti, Sigurdsson, Sigurdur, Stubbe, Beate, Talluri, Rajesh, Vasan, Ramachandran S, Vernooij, Meike W, Völzke, Henry, Wiggins, Kerri L, Yu, Bing, Beiser, Alexa S, Gudnason, Vilmundur, Mosley, Thomas, Psaty, Bruce M, Wolters, Frank J, Grabe, Hans J, and Seshadri, Sudha

Subjects
Brain Disorders, Lung, Neurosciences, Clinical Research, Aging, Humans, Aged, Forced Expiratory Volume, Cross-Sectional Studies, Magnetic Resonance Imaging, Brain, Dementia, epidemiology, magnetic resonance imaging, respiratory function tests, Clinical Sciences, Cognitive Sciences, Neurology & Neurosurgery
Abstract

BackgroundPrevious studies suggest poor pulmonary function is associated with increased burden of cerebral white matter hyperintensities and brain atrophy among elderly individuals, but the results are inconsistent.ObjectiveTo study the cross-sectional associations of pulmonary function with structural brain variables.MethodsData from six large community-based samples (N = 11,091) were analyzed. Spirometric measurements were standardized with respect to age, sex, height, and ethnicity using reference equations of the Global Lung Function Initiative. Associations of forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), and their ratio FEV1/FVC with brain volume, gray matter volume, hippocampal volume, and volume of white matter hyperintensities were investigated using multivariable linear regressions for each study separately and then combined using random-effect meta-analyses.ResultsFEV1 and FVC were positively associated with brain volume, gray matter volume, and hippocampal volume, and negatively associated with white matter hyperintensities volume after multiple testing correction, with little heterogeneity present between the studies. For instance, an increase of FVC by one unit was associated with 3.5 ml higher brain volume (95% CI: [2.2, 4.9]). In contrast, results for FEV1/FVC were more heterogeneous across studies, with significant positive associations with brain volume, gray matter volume, and hippocampal volume, but not white matter hyperintensities volume. Associations of brain variables with both FEV1 and FVC were consistently stronger than with FEV1/FVC, specifically with brain volume and white matter hyperintensities volume.ConclusionIn cross-sectional analyses, worse pulmonary function is associated with smaller brain volumes and higher white matter hyperintensities burden.

Published
2022

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

Author

Sønderby, Ida E, Ching, Christopher RK, Thomopoulos, Sophia I, van der Meer, Dennis, Sun, Daqiang, Villalon‐Reina, Julio E, Agartz, Ingrid, Amunts, Katrin, Arango, Celso, Armstrong, Nicola J, Ayesa‐Arriola, Rosa, Bakker, Geor, Bassett, Anne S, Boomsma, Dorret I, Bülow, Robin, Butcher, Nancy J, Calhoun, Vince D, Caspers, Svenja, Chow, Eva WC, Cichon, Sven, Ciufolini, Simone, Craig, Michael C, Crespo‐Facorro, Benedicto, Cunningham, Adam C, Dale, Anders M, Dazzan, Paola, de Zubicaray, Greig I, Djurovic, Srdjan, Doherty, Joanne L, Donohoe, Gary, Draganski, Bogdan, Durdle, Courtney A, Ehrlich, Stefan, Emanuel, Beverly S, Espeseth, Thomas, Fisher, Simon E, Ge, Tian, Glahn, David C, Grabe, Hans J, Gur, Raquel E, Gutman, Boris A, Haavik, Jan, Håberg, Asta K, Hansen, Laura A, Hashimoto, Ryota, Hibar, Derrek P, Holmes, Avram J, Hottenga, Jouke‐Jan, Pol, Hilleke E Hulshoff, Jalbrzikowski, Maria, Knowles, Emma EM, Kushan, Leila, Linden, David EJ, Liu, Jingyu, Lundervold, Astri J, Martin‐Brevet, Sandra, Martínez, Kenia, Mather, Karen A, Mathias, Samuel R, McDonald‐McGinn, Donna M, McRae, Allan F, Medland, Sarah E, Moberget, Torgeir, Modenato, Claudia, Sánchez, Jennifer Monereo, Moreau, Clara A, Mühleisen, Thomas W, Paus, Tomas, Pausova, Zdenka, Prieto, Carlos, Ragothaman, Anjanibhargavi, Reinbold, Céline S, Marques, Tiago Reis, Repetto, Gabriela M, Reymond, Alexandre, Roalf, David R, Rodriguez‐Herreros, Borja, Rucker, James J, Sachdev, Perminder S, Schmitt, James E, Schofield, Peter R, Silva, Ana I, Stefansson, Hreinn, Stein, Dan J, Tamnes, Christian K, Tordesillas‐Gutiérrez, Diana, Ulfarsson, Magnus O, Vajdi, Ariana, van 't Ent, Dennis, van den Bree, Marianne BM, Vassos, Evangelos, Vázquez‐Bourgon, Javier, Vila‐Rodriguez, Fidel, Walters, G Bragi, Wen, Wei, Westlye, Lars T, Wittfeld, Katharina, Zackai, Elaine H, Stefánsson, Kári, and Jacquemont, Sebastien

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Mental Health, Clinical Research, Pediatric, Genetics, Basic Behavioral and Social Science, Human Genome, Prevention, Brain Disorders, Behavioral and Social Science, Neurosciences, Aetiology, 2.1 Biological and endogenous factors, Mental health, Neurological, Brain, DNA Copy Number Variations, Humans, Magnetic Resonance Imaging, Mental Disorders, Multicenter Studies as Topic, Neurodevelopmental Disorders, Neuroimaging, brain structural imaging, copy number variant, diffusion tensor imaging, evolution, genetics-first approach, neurodevelopmental disorders, psychiatric disorders, ENIGMA-CNV Working Group, ENIGMA 22q11.2 Deletion Syndrome Working Group, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

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

Published
2022

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

Author

Kong, Xiang‐Zhen, Mathias, Samuel R, Guadalupe, Tulio, Abé, Christoph, Agartz, Ingrid, Akudjedu, Theophilus N, Aleman, Andre, Alhusaini, Saud, Allen, Nicholas B, Ames, David, Andreassen, Ole A, Vasquez, Alejandro Arias, Armstrong, Nicola J, Asherson, Phil, Bergo, Felipe, Bastin, Mark E, Batalla, Albert, Bauer, Jochen, Baune, Bernhard T, Baur‐Streubel, Ramona, Biederman, Joseph, Blaine, Sara K, Boedhoe, Premika, Bøen, Erlend, Bose, Anushree, Bralten, Janita, Brandeis, Daniel, Brem, Silvia, Brodaty, Henry, Yüksel, Dilara, Brooks, Samantha J, Buitelaar, Jan, Bürger, Christian, Bülow, Robin, Calhoun, Vince, Calvo, Anna, Canales‐Rodríguez, Erick Jorge, Cannon, Dara M, Caparelli, Elisabeth C, Castellanos, Francisco X, Cendes, Fernando, Chaim‐Avancini, Tiffany Moukbel, Chantiluke, Kaylita, Chen, Qun‐lin, Chen, Xiayu, Cheng, Yuqi, Christakou, Anastasia, Clark, Vincent P, Coghill, David, Connolly, Colm G, Conzelmann, Annette, Córdova‐Palomera, Aldo, Cousijn, Janna, Crow, Tim, Cubillo, Ana, Dannlowski, Udo, de Bruttopilo, Sara Ambrosino, de Zeeuw, Patrick, Deary, Ian J, Demeter, Damion V, Di Martino, Adriana, Dickie, Erin W, Dietsche, Bruno, Doan, Nhat Trung, Doherty, Colin P, Doyle, Alysa, Durston, Sarah, Earl, Eric, Ehrlich, Stefan, Ekman, Carl Johan, Elvsåshagen, Torbjørn, Epstein, Jeffery N, Fair, Damien A, Faraone, Stephen V, Fernández, Guillén, Flint, Claas, Filho, Geraldo Busatto, Förster, Katharina, Fouche, Jean‐Paul, Foxe, John J, Frodl, Thomas, Fuentes‐Claramonte, Paola, Fullerton, Janice M, Garavan, Hugh, do Santos Garcia, Danielle, Gotlib, Ian H, Goudriaan, Anna E, Grabe, Hans Jörgen, Groenewold, Nynke A, Grotegerd, Dominik, Gruber, Oliver, Gurholt, Tiril, Haavik, Jan, Hahn, Tim, Hansell, Narelle K, Harris, Mathew A, Hartman, Catharina A, del Carmen Valdés Hernández, Maria, and Heslenfeld, Dirk

Subjects
Biological Psychology, Psychology, Neurosciences, Neurological, Adolescent, Adult, Aged, Brain Cortical Thickness, Cerebral Cortex, Datasets as Topic, Humans, Magnetic Resonance Imaging, Middle Aged, Multicenter Studies as Topic, Neuroimaging, Publication Bias, Reproducibility of Results, Young Adult, ENIGMA Laterality Working Group, P-hacking, multisite collaboration, publication bias, reproducibility, team science, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

The problem of poor reproducibility of scientific findings has received much attention over recent years, in a variety of fields including psychology and neuroscience. The problem has been partly attributed to publication bias and unwanted practices such as p-hacking. Low statistical power in individual studies is also understood to be an important factor. In a recent multisite collaborative study, we mapped brain anatomical left-right asymmetries for regional measures of surface area and cortical thickness, in 99 MRI datasets from around the world, for a total of over 17,000 participants. In the present study, we revisited these hemispheric effects from the perspective of reproducibility. Within each dataset, we considered that an effect had been reproduced when it matched the meta-analytic effect from the 98 other datasets, in terms of effect direction and significance threshold. In this sense, the results within each dataset were viewed as coming from separate studies in an "ideal publishing environment," that is, free from selective reporting and p hacking. We found an average reproducibility rate of 63.2% (SD = 22.9%, min = 22.2%, max = 97.0%). As expected, reproducibility was higher for larger effects and in larger datasets. Reproducibility was not obviously related to the age of participants, scanner field strength, FreeSurfer software version, cortical regional measurement reliability, or regional size. These findings constitute an empirical illustration of reproducibility in the absence of publication bias or p hacking, when assessing realistic biological effects in heterogeneous neuroscience data, and given typically-used sample sizes.

Published
2022

29. Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural Networks

Author

Fisch, Lukas, Ernsting, Jan, Winter, Nils R., Holstein, Vincent, Leenings, Ramona, Beisemann, Marie, Sarink, Kelvin, Emden, Daniel, Opel, Nils, Redlich, Ronny, Repple, Jonathan, Grotegerd, Dominik, Meinert, Susanne, Wulms, Niklas, Minnerup, Heike, Hirsch, Jochen G., Niendorf, Thoralf, Endemann, Beate, Bamberg, Fabian, Kröncke, Thomas, Peters, Annette, Bülow, Robin, Völzke, Henry, von Stackelberg, Oyunbileg, Sowade, Ramona Felizitas, Umutlu, Lale, Schmidt, Börge, Caspers, Svenja, Consortium, German National Cohort Study Center, Kugel, Harald, Baune, Bernhard T., Kircher, Tilo, Risse, Benjamin, Dannlowski, Udo, Berger, Klaus, and Hahn, Tim

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Age prediction based on Magnetic Resonance Imaging (MRI) data of the brain is a biomarker to quantify the progress of brain diseases and aging. Current approaches rely on preparing the data with multiple preprocessing steps, such as registering voxels to a standardized brain atlas, which yields a significant computational overhead, hampers widespread usage and results in the predicted brain-age to be sensitive to preprocessing parameters. Here we describe a 3D Convolutional Neural Network (CNN) based on the ResNet architecture being trained on raw, non-registered T$_ 1$-weighted MRI data of N=10,691 samples from the German National Cohort and additionally applied and validated in N=2,173 samples from three independent studies using transfer learning. For comparison, state-of-the-art models using preprocessed neuroimaging data are trained and validated on the same samples. The 3D CNN using raw neuroimaging data predicts age with a mean average deviation of 2.84 years, outperforming the state-of-the-art brain-age models using preprocessed data. Since our approach is invariant to preprocessing software and parameter choices, it enables faster, more robust and more accurate brain-age modeling.

Published
2021

30. Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease

Author

Duperron, Marie-Gabrielle, Knol, Maria J., Le Grand, Quentin, Evans, Tavia E., Mishra, Aniket, Tsuchida, Ami, Roshchupkin, Gennady, Konuma, Takahiro, Trégouët, David-Alexandre, Romero, Jose Rafael, Frenzel, Stefan, Luciano, Michelle, Hofer, Edith, Bourgey, Mathieu, Dueker, Nicole D., Delgado, Pilar, Hilal, Saima, Tankard, Rick M., Dubost, Florian, Shin, Jean, Saba, Yasaman, Armstrong, Nicola J., Bordes, Constance, Bastin, Mark E., Beiser, Alexa, Brodaty, Henry, Bülow, Robin, Carrera, Caty, Chen, Christopher, Cheng, Ching-Yu, Deary, Ian J., Gampawar, Piyush G., Himali, Jayandra J., Jiang, Jiyang, Kawaguchi, Takahisa, Li, Shuo, Macalli, Melissa, Marquis, Pascale, Morris, Zoe, Muñoz Maniega, Susana, Miyamoto, Susumu, Okawa, Masakazu, Paradise, Matthew, Parva, Pedram, Rundek, Tatjana, Sargurupremraj, Muralidharan, Schilling, Sabrina, Setoh, Kazuya, Soukarieh, Omar, Tabara, Yasuharu, Teumer, Alexander, Thalamuthu, Anbupalam, Trollor, Julian N., Valdés Hernández, Maria C., Vernooij, Meike W., Völker, Uwe, Wittfeld, Katharina, Wong, Tien Yin, Wright, Margaret J., Zhang, Junyi, Zhao, Wanting, Zhu, Yi-Cheng, Schmidt, Helena, Sachdev, Perminder S., Wen, Wei, Yoshida, Kazumichi, Joutel, Anne, Satizabal, Claudia L., Sacco, Ralph L., Bourque, Guillaume, Lathrop, Mark, Paus, Tomas, Fernandez-Cadenas, Israel, Yang, Qiong, Mazoyer, Bernard, Boutinaud, Philippe, Okada, Yukinori, Grabe, Hans J., Mather, Karen A., Schmidt, Reinhold, Joliot, Marc, Ikram, M. Arfan, Matsuda, Fumihiko, Tzourio, Christophe, Wardlaw, Joanna M., Seshadri, Sudha, Adams, Hieab H. H., and Debette, Stéphanie

Published
2023
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33. 1q21.1 distal copy number variants are associated with cerebral and cognitive alterations in humans.

Author

Sønderby, Ida E, van der Meer, Dennis, Moreau, Clara, Kaufmann, Tobias, Walters, G Bragi, Ellegaard, Maria, Abdellaoui, Abdel, Ames, David, Amunts, Katrin, Andersson, Micael, Armstrong, Nicola J, Bernard, Manon, Blackburn, Nicholas B, Blangero, John, Boomsma, Dorret I, Brodaty, Henry, Brouwer, Rachel M, Bülow, Robin, Bøen, Rune, Cahn, Wiepke, Calhoun, Vince D, Caspers, Svenja, Ching, Christopher RK, Cichon, Sven, Ciufolini, Simone, Crespo-Facorro, Benedicto, Curran, Joanne E, Dale, Anders M, Dalvie, Shareefa, Dazzan, Paola, de Geus, Eco JC, de Zubicaray, Greig I, de Zwarte, Sonja MC, Desrivieres, Sylvane, Doherty, Joanne L, Donohoe, Gary, Draganski, Bogdan, Ehrlich, Stefan, Eising, Else, Espeseth, Thomas, Fejgin, Kim, Fisher, Simon E, Fladby, Tormod, Frei, Oleksandr, Frouin, Vincent, Fukunaga, Masaki, Gareau, Thomas, Ge, Tian, Glahn, David C, Grabe, Hans J, Groenewold, Nynke A, Gústafsson, Ómar, Haavik, Jan, Haberg, Asta K, Hall, Jeremy, Hashimoto, Ryota, Hehir-Kwa, Jayne Y, Hibar, Derrek P, Hillegers, Manon HJ, Hoffmann, Per, Holleran, Laurena, Holmes, Avram J, Homuth, Georg, Hottenga, Jouke-Jan, Hulshoff Pol, Hilleke E, Ikeda, Masashi, Jahanshad, Neda, Jockwitz, Christiane, Johansson, Stefan, Jönsson, Erik G, Jørgensen, Niklas R, Kikuchi, Masataka, Knowles, Emma EM, Kumar, Kuldeep, Le Hellard, Stephanie, Leu, Costin, Linden, David EJ, Liu, Jingyu, Lundervold, Arvid, Lundervold, Astri Johansen, Maillard, Anne M, Martin, Nicholas G, Martin-Brevet, Sandra, Mather, Karen A, Mathias, Samuel R, McMahon, Katie L, McRae, Allan F, Medland, Sarah E, Meyer-Lindenberg, Andreas, Moberget, Torgeir, Modenato, Claudia, Sánchez, Jennifer Monereo, Morris, Derek W, Mühleisen, Thomas W, Murray, Robin M, Nielsen, Jacob, Nordvik, Jan E, Nyberg, Lars, Loohuis, Loes M Olde, and Ophoff, Roel A

Subjects
ENIGMA-CNV working group, Clinical Sciences, Public Health and Health Services, Psychology
Abstract

Low-frequency 1q21.1 distal deletion and duplication copy number variant (CNV) carriers are predisposed to multiple neurodevelopmental disorders, including schizophrenia, autism and intellectual disability. Human carriers display a high prevalence of micro- and macrocephaly in deletion and duplication carriers, respectively. The underlying brain structural diversity remains largely unknown. We systematically called CNVs in 38 cohorts from the large-scale ENIGMA-CNV collaboration and the UK Biobank and identified 28 1q21.1 distal deletion and 22 duplication carriers and 37,088 non-carriers (48% male) derived from 15 distinct magnetic resonance imaging scanner sites. With standardized methods, we compared subcortical and cortical brain measures (all) and cognitive performance (UK Biobank only) between carrier groups also testing for mediation of brain structure on cognition. We identified positive dosage effects of copy number on intracranial volume (ICV) and total cortical surface area, with the largest effects in frontal and cingulate cortices, and negative dosage effects on caudate and hippocampal volumes. The carriers displayed distinct cognitive deficit profiles in cognitive tasks from the UK Biobank with intermediate decreases in duplication carriers and somewhat larger in deletion carriers-the latter potentially mediated by ICV or cortical surface area. These results shed light on pathobiological mechanisms of neurodevelopmental disorders, by demonstrating gene dose effect on specific brain structures and effect on cognitive function.

Published
2021

34. 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, Biomedical Imaging, Neurosciences, Brain Disorders, Women's Health, Behavioral and Social Science, 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

36. Cerebral small vessel disease genomics and its implications across the lifespan.

Author

Sargurupremraj, Muralidharan, Suzuki, Hideaki, Jian, Xueqiu, Sarnowski, Chloé, Evans, Tavia E, Bis, Joshua C, Eiriksdottir, Gudny, Sakaue, Saori, Terzikhan, Natalie, Habes, Mohamad, Zhao, Wei, Armstrong, Nicola J, Hofer, Edith, Yanek, Lisa R, Hagenaars, Saskia P, Kumar, Rajan B, van den Akker, Erik B, McWhirter, Rebekah E, Trompet, Stella, Mishra, Aniket, Saba, Yasaman, Satizabal, Claudia L, Beaudet, Gregory, Petit, Laurent, Tsuchida, Ami, Zago, Laure, Schilling, Sabrina, Sigurdsson, Sigurdur, Gottesman, Rebecca F, Lewis, Cora E, Aggarwal, Neelum T, Lopez, Oscar L, Smith, Jennifer A, Valdés Hernández, Maria C, van der Grond, Jeroen, Wright, Margaret J, Knol, Maria J, Dörr, Marcus, Thomson, Russell J, Bordes, Constance, Le Grand, Quentin, Duperron, Marie-Gabrielle, Smith, Albert V, Knopman, David S, Schreiner, Pamela J, Evans, Denis A, Rotter, Jerome I, Beiser, Alexa S, Maniega, Susana Muñoz, Beekman, Marian, Trollor, Julian, Stott, David J, Vernooij, Meike W, Wittfeld, Katharina, Niessen, Wiro J, Soumaré, Aicha, Boerwinkle, Eric, Sidney, Stephen, Turner, Stephen T, Davies, Gail, Thalamuthu, Anbupalam, Völker, Uwe, van Buchem, Mark A, Bryan, R Nick, Dupuis, Josée, Bastin, Mark E, Ames, David, Teumer, Alexander, Amouyel, Philippe, Kwok, John B, Bülow, Robin, Deary, Ian J, Schofield, Peter R, Brodaty, Henry, Jiang, Jiyang, Tabara, Yasuharu, Setoh, Kazuya, Miyamoto, Susumu, Yoshida, Kazumichi, Nagata, Manabu, Kamatani, Yoichiro, Matsuda, Fumihiko, Psaty, Bruce M, Bennett, David A, De Jager, Philip L, Mosley, Thomas H, Sachdev, Perminder S, Schmidt, Reinhold, Warren, Helen R, Evangelou, Evangelos, Trégouët, David-Alexandre, International Network against Thrombosis (INVENT) Consortium, International Headache Genomics Consortium (IHGC), Ikram, Mohammad A, Wen, Wei, DeCarli, Charles, Srikanth, Velandai K, Jukema, J Wouter, Slagboom, Eline P, and Kardia, Sharon LR

Subjects
International Network against Thrombosis (INVENT) Consortium, International Headache Genomics Consortium, Humans, Alzheimer Disease, Hypertension, Medical History Taking, Risk Assessment, Risk Factors, Adult, Aged, Aged, 80 and over, Middle Aged, Female, Male, Stroke, Genome-Wide Association Study, Young Adult, Diffusion Tensor Imaging, Genetic Loci, Mendelian Randomization Analysis, Cerebral Small Vessel Diseases, White Matter, and over
Abstract

White matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide loci for WMH-volume in a cohort of 50,970 older individuals, accounting for modification/confounding by hypertension. Aggregated WMH risk variants were associated with altered white matter integrity (p = 2.5×10-7) in brain images from 1,738 young healthy adults, providing insight into the lifetime impact of SVD genetic risk. Mendelian randomization suggested causal association of increasing WMH-volume with stroke, Alzheimer-type dementia, and of increasing blood pressure (BP) with larger WMH-volume, notably also in persons without clinical hypertension. Transcriptome-wide colocalization analyses showed association of WMH-volume with expression of 39 genes, of which four encode known drug targets. Finally, we provide insight into BP-independent biological pathways underlying SVD and suggest potential for genetic stratification of high-risk individuals and for genetically-informed prioritization of drug targets for prevention trials.

Published
2020

39. Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults.

Author

Hofer, Edith, Roshchupkin, Gennady V, Adams, Hieab HH, Knol, Maria J, Lin, Honghuang, Li, Shuo, Zare, Habil, Ahmad, Shahzad, Armstrong, Nicola J, Satizabal, Claudia L, Bernard, Manon, Bis, Joshua C, Gillespie, Nathan A, Luciano, Michelle, Mishra, Aniket, Scholz, Markus, Teumer, Alexander, Xia, Rui, Jian, Xueqiu, Mosley, Thomas H, Saba, Yasaman, Pirpamer, Lukas, Seiler, Stephan, Becker, James T, Carmichael, Owen, Rotter, Jerome I, Psaty, Bruce M, Lopez, Oscar L, Amin, Najaf, van der Lee, Sven J, Yang, Qiong, Himali, Jayandra J, Maillard, Pauline, Beiser, Alexa S, DeCarli, Charles, Karama, Sherif, Lewis, Lindsay, Harris, Mat, Bastin, Mark E, Deary, Ian J, Veronica Witte, A, Beyer, Frauke, Loeffler, Markus, Mather, Karen A, Schofield, Peter R, Thalamuthu, Anbupalam, Kwok, John B, Wright, Margaret J, Ames, David, Trollor, Julian, Jiang, Jiyang, Brodaty, Henry, Wen, Wei, Vernooij, Meike W, Hofman, Albert, Uitterlinden, André G, Niessen, Wiro J, Wittfeld, Katharina, Bülow, Robin, Völker, Uwe, Pausova, Zdenka, Bruce Pike, G, Maingault, Sophie, Crivello, Fabrice, Tzourio, Christophe, Amouyel, Philippe, Mazoyer, Bernard, Neale, Michael C, Franz, Carol E, Lyons, Michael J, Panizzon, Matthew S, Andreassen, Ole A, Dale, Anders M, Logue, Mark, Grasby, Katrina L, Jahanshad, Neda, Painter, Jodie N, Colodro-Conde, Lucía, Bralten, Janita, Hibar, Derrek P, Lind, Penelope A, Pizzagalli, Fabrizio, Stein, Jason L, Thompson, Paul M, Medland, Sarah E, ENIGMA consortium, Sachdev, Perminder S, Kremen, William S, Wardlaw, Joanna M, Villringer, Arno, van Duijn, Cornelia M, Grabe, Hans J, Longstreth, William T, Fornage, Myriam, Paus, Tomas, Debette, Stephanie, Ikram, M Arfan, Schmidt, Helena, Schmidt, Reinhold, and Seshadri, Sudha

Subjects
ENIGMA consortium, Brain, Chromosome Structures, Humans, Neurodegenerative Diseases, Cognition, Mental Disorders, Genomics, Aging, Phenotype, Polymorphism, Single Nucleotide, Adult, Aged, Aged, 80 and over, Middle Aged, Female, Male, Genome-Wide Association Study, Genetics, Neurosciences, Human Genome, Brain Disorders, Biotechnology, 1.1 Normal biological development and functioning, 2.1 Biological and endogenous factors, Neurological
Abstract

Cortical thickness, surface area and volumes vary with age and cognitive function, and in neurological and psychiatric diseases. Here we report heritability, genetic correlations and genome-wide associations of these cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprises 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. We identify genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There is enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging.

Published
2020

40. 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

41. The genetic architecture of the human cerebral cortex

Author

Grasby, Katrina L, Jahanshad, Neda, Painter, Jodie N, Colodro-Conde, Lucía, Bralten, Janita, Hibar, Derrek P, Lind, Penelope A, Pizzagalli, Fabrizio, Ching, Christopher RK, McMahon, Mary Agnes B, Shatokhina, Natalia, Zsembik, Leo CP, Thomopoulos, Sophia I, Zhu, Alyssa H, Strike, Lachlan T, Agartz, Ingrid, Alhusaini, Saud, Almeida, Marcio AA, Alnæs, Dag, Amlien, Inge K, Andersson, Micael, Ard, Tyler, Armstrong, Nicola J, Ashley-Koch, Allison, Atkins, Joshua R, Bernard, Manon, Brouwer, Rachel M, Buimer, Elizabeth EL, Bülow, Robin, Bürger, Christian, Cannon, Dara M, Chakravarty, Mallar, Chen, Qiang, Cheung, Joshua W, Couvy-Duchesne, Baptiste, Dale, Anders M, Dalvie, Shareefa, de Araujo, Tânia K, de Zubicaray, Greig I, de Zwarte, Sonja MC, Braber, Anouk den, Doan, Nhat Trung, Dohm, Katharina, Ehrlich, Stefan, Engelbrecht, Hannah-Ruth, Erk, Susanne, Fan, Chun Chieh, Fedko, Iryna O, Foley, Sonya F, Ford, Judith M, Fukunaga, Masaki, Garrett, Melanie E, Ge, Tian, Giddaluru, Sudheer, Goldman, Aaron L, Green, Melissa J, Groenewold, Nynke A, Grotegerd, Dominik, Gurholt, Tiril P, Gutman, Boris A, Hansell, Narelle K, Harris, Mathew A, Harrison, Marc B, Haswell, Courtney C, Hauser, Michael, Herms, Stefan, Heslenfeld, Dirk J, Ho, New Fei, Hoehn, David, Hoffmann, Per, Holleran, Laurena, Hoogman, Martine, Hottenga, Jouke-Jan, Ikeda, Masashi, Janowitz, Deborah, Jansen, Iris E, Jia, Tianye, Jockwitz, Christiane, Kanai, Ryota, Karama, Sherif, Kasperaviciute, Dalia, Kaufmann, Tobias, Kelly, Sinead, Kikuchi, Masataka, Klein, Marieke, Knapp, Michael, Knodt, Annchen R, Krämer, Bernd, Lam, Max, Lancaster, Thomas M, Lee, Phil H, Lett, Tristram A, Lewis, Lindsay B, Lopes-Cendes, Iscia, Luciano, Michelle, Macciardi, Fabio, Marquand, Andre F, Mathias, Samuel R, Melzer, Tracy R, and Milaneschi, Yuri

Subjects
Biological Sciences, Genetics, Neurosciences, Mental Health, Brain Disorders, Stem Cell Research, 2.1 Biological and endogenous factors, Aetiology, Neurological, Attention Deficit Disorder with Hyperactivity, Brain Mapping, Cerebral Cortex, Cognition, Genetic Loci, Genetic Variation, Genome-Wide Association Study, Humans, Magnetic Resonance Imaging, Organ Size, Parkinson Disease, Alzheimer’s Disease Neuroimaging Initiative, CHARGE Consortium, EPIGEN Consortium, IMAGEN Consortium, SYS Consortium, Parkinson’s Progression Markers Initiative, Enhancing NeuroImaging Genetics through Meta-Analysis Consortium (ENIGMA)—Genetics working group, General Science & Technology
Abstract

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.

Published
2020

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

Author

Thompson, Paul M, Jahanshad, Neda, Ching, Christopher RK, Salminen, Lauren E, Thomopoulos, Sophia I, Bright, Joanna, Baune, Bernhard T, Bertolín, Sara, Bralten, Janita, Bruin, Willem B, Bülow, Robin, Chen, Jian, Chye, Yann, Dannlowski, Udo, de Kovel, Carolien GF, Donohoe, Gary, Eyler, Lisa T, Faraone, Stephen V, Favre, Pauline, Filippi, Courtney A, Frodl, Thomas, Garijo, Daniel, Gil, Yolanda, Grabe, Hans J, Grasby, Katrina L, Hajek, Tomas, Han, Laura KM, Hatton, Sean N, Hilbert, Kevin, Ho, Tiffany C, Holleran, Laurena, Homuth, Georg, Hosten, Norbert, Houenou, Josselin, Ivanov, Iliyan, Jia, Tianye, Kelly, Sinead, Klein, Marieke, Kwon, Jun Soo, Laansma, Max A, Leerssen, Jeanne, Lueken, Ulrike, Nunes, Abraham, Neill, Joseph O', Opel, Nils, Piras, Fabrizio, Piras, Federica, Postema, Merel C, Pozzi, Elena, Shatokhina, Natalia, Soriano-Mas, Carles, Spalletta, Gianfranco, Sun, Daqiang, Teumer, Alexander, Tilot, Amanda K, Tozzi, Leonardo, van der Merwe, Celia, Van Someren, Eus JW, van Wingen, Guido A, Völzke, Henry, Walton, Esther, Wang, Lei, Winkler, Anderson M, Wittfeld, Katharina, Wright, Margaret J, Yun, Je-Yeon, Zhang, Guohao, Zhang-James, Yanli, Adhikari, Bhim M, Agartz, Ingrid, Aghajani, Moji, Aleman, André, Althoff, Robert R, Altmann, Andre, Andreassen, Ole A, Baron, David A, Bartnik-Olson, Brenda L, Marie Bas-Hoogendam, Janna, Baskin-Sommers, Arielle R, Bearden, Carrie E, Berner, Laura A, Boedhoe, Premika SW, Brouwer, Rachel M, Buitelaar, Jan K, Caeyenberghs, Karen, Cecil, Charlotte AM, Cohen, Ronald A, Cole, James H, Conrod, Patricia J, De Brito, Stephane A, de Zwarte, Sonja MC, Dennis, Emily L, Desrivieres, Sylvane, Dima, Danai, Ehrlich, Stefan, Esopenko, Carrie, Fairchild, Graeme, Fisher, Simon E, Fouche, Jean-Paul, and Francks, Clyde

Subjects
ENIGMA Consortium, Brain, Humans, Magnetic Resonance Imaging, Reproducibility of Results, Depressive Disorder, Major, Neuroimaging, Neurosciences, Clinical Research, Mental Health, Brain Disorders, Behavioral and Social Science, Genetics, Basic Behavioral and Social Science, Prevention, 2.1 Biological and endogenous factors, 2.3 Psychological, social and economic factors, Mental health, Neurological, Clinical Sciences, Public Health and Health Services, Psychology
Abstract

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.

Published
2020

43. Dose response of the 16p11.2 distal copy number variant on intracranial volume and basal ganglia

Author

Sønderby, Ida E, Gústafsson, Ómar, Doan, Nhat Trung, Hibar, Derrek P, Martin-Brevet, Sandra, Abdellaoui, Abdel, Ames, David, Amunts, Katrin, Andersson, Michael, Armstrong, Nicola J, Bernard, Manon, Blackburn, Nicholas, Blangero, John, Boomsma, Dorret I, Bralten, Janita, Brattbak, Hans-Richard, Brodaty, Henry, Brouwer, Rachel M, Bülow, Robin, Calhoun, Vince, Caspers, Svenja, Cavalleri, Gianpiero, Chen, Chi-Hua, Cichon, Sven, Ciufolini, Simone, Corvin, Aiden, Crespo-Facorro, Benedicto, Curran, Joanne E, Dale, Anders M, Dalvie, Shareefa, Dazzan, Paola, de Geus, Eco JC, de Zubicaray, Greig I, de Zwarte, Sonja MC, Delanty, Norman, den Braber, Anouk, Desrivières, Sylvane, Donohoe, Gary, Draganski, Bogdan, Ehrlich, Stefan, Espeseth, Thomas, Fisher, Simon E, Franke, Barbara, Frouin, Vincent, Fukunaga, Masaki, Gareau, Thomas, Glahn, David C, Grabe, Hans, Groenewold, Nynke A, Haavik, Jan, Håberg, Asta, Hashimoto, Ryota, Hehir-Kwa, Jayne Y, Heinz, Andreas, Hillegers, Manon HJ, Hoffmann, Per, Holleran, Laurena, Hottenga, Jouke-Jan, Hulshoff, Hilleke E, Ikeda, Masashi, Jahanshad, Neda, Jernigan, Terry, Jockwitz, Christiane, Johansson, Stefan, Jonsdottir, Gudrun A, Jönsson, Erik G, Kahn, Rene, Kaufmann, Tobias, Kelly, Sinead, Kikuchi, Masataka, Knowles, Emma EM, Kolskår, Knut K, Kwok, John B, Hellard, Stephanie Le, Leu, Costin, Liu, Jingyu, Lundervold, Astri J, Lundervold, Arvid, Martin, Nicholas G, Mather, Karen, Mathias, Samuel R, McCormack, Mark, McMahon, Katie L, McRae, Allan, Milaneschi, Yuri, Moreau, Clara, Morris, Derek, Mothersill, David, Mühleisen, Thomas W, Murray, Robin, Nordvik, Jan E, Nyberg, Lars, Olde Loohuis, Loes M, Ophoff, Roel, Paus, Tomas, Pausova, Zdenka, Penninx, Brenda, Peralta, Juan M, Pike, Bruce, and Prieto, Carlos

Subjects
Intellectual and Developmental Disabilities (IDD), Neurosciences, Brain Disorders, Autism, Biomedical Imaging, Mental Health, Mental health, Adult, Autism Spectrum Disorder, Autistic Disorder, Basal Ganglia, Brain, Chromosome Deletion, Chromosome Disorders, Chromosome Duplication, Chromosomes, Human, Pair 16, DNA Copy Number Variations, Databases, Factual, Female, Globus Pallidus, Humans, Image Processing, Computer-Assisted, Intellectual Disability, Magnetic Resonance Imaging, Male, Middle Aged, Neurodevelopmental Disorders, Organ Size, Putamen, Schizophrenia, 16p11.2 European Consortium, for the ENIGMA-CNV working group, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Carriers of large recurrent copy number variants (CNVs) have a higher risk of developing neurodevelopmental disorders. The 16p11.2 distal CNV predisposes carriers to e.g., autism spectrum disorder and schizophrenia. We compared subcortical brain volumes of 12 16p11.2 distal deletion and 12 duplication carriers to 6882 non-carriers from the large-scale brain Magnetic Resonance Imaging collaboration, ENIGMA-CNV. After stringent CNV calling procedures, and standardized FreeSurfer image analysis, we found negative dose-response associations with copy number on intracranial volume and on regional caudate, pallidum and putamen volumes (β = -0.71 to -1.37; P

Published
2020

44. Correction: Dose response of the 16p11.2 distal copy number variant on intracranial volume and basal ganglia.

Author

Sønderby, Ida E, Gústafsson, Ómar, Doan, Nhat Trung, Hibar, Derrek P, Martin-Brevet, Sandra, Abdellaoui, Abdel, Ames, David, Amunts, Katrin, Andersson, Michael, Armstrong, Nicola J, Bernard, Manon, Blackburn, Nicholas, Blangero, John, Boomsma, Dorret I, Bralten, Janita, Brattbak, Hans-Richard, Brodaty, Henry, Brouwer, Rachel M, Bülow, Robin, Calhoun, Vince, Caspers, Svenja, Cavalleri, Gianpiero, Chen, Chi-Hua, Cichon, Sven, Ciufolini, Simone, Corvin, Aiden, Crespo-Facorro, Benedicto, Curran, Joanne E, Dale, Anders M, Dalvie, Shareefa, Dazzan, Paola, de Geus, Eco JC, de Zubicaray, Greig I, de Zwarte, Sonja MC, Delanty, Norman, den Braber, Anouk, Desrivières, Sylvane, Donohoe, Gary, Draganski, Bogdan, Ehrlich, Stefan, Espeseth, Thomas, Fisher, Simon E, Franke, Barbara, Frouin, Vincent, Fukunaga, Masaki, Gareau, Thomas, Glahn, David C, Grabe, Hans, Groenewold, Nynke A, Haavik, Jan, Håberg, Asta, Hashimoto, Ryota, Hehir-Kwa, Jayne Y, Heinz, Andreas, Hillegers, Manon HJ, Hoffmann, Per, Holleran, Laurena, Hottenga, Jouke-Jan, Hulshoff, Hilleke E, Ikeda, Masashi, Jahanshad, Neda, Jernigan, Terry, Jockwitz, Christiane, Johansson, Stefan, Jonsdottir, Gudrun A, Jönsson, Erik G, Kahn, Rene, Kaufmann, Tobias, Kelly, Sinead, Kikuchi, Masataka, Knowles, Emma EM, Kolskår, Knut K, Kwok, John B, Hellard, Stephanie Le, Leu, Costin, Liu, Jingyu, Lundervold, Astri J, Lundervold, Arvid, Martin, Nicholas G, Mather, Karen, Mathias, Samuel R, McCormack, Mark, McMahon, Katie L, McRae, Allan, Milaneschi, Yuri, Moreau, Clara, Morris, Derek, Mothersill, David, Mühleisen, Thomas W, Murray, Robin, Nordvik, Jan E, Nyberg, Lars, Olde Loohuis, Loes M, Ophoff, Roel, Paus, Tomas, Pausova, Zdenka, Penninx, Brenda, Peralta, Juan M, Pike, Bruce, and Prieto, Carlos

Subjects
16p11.2 European Consortium, for the ENIGMA-CNV working group, Neurosciences, Psychiatry, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences
Abstract

Prior to and following the publication of this article the authors noted that the complete list of authors was not included in the main article and was only present in Supplementary Table 1. The author list in the original article has now been updated to include all authors, and Supplementary Table 1 has been removed. All other supplementary files have now been updated accordingly. Furthermore, in Table 1 of this Article, the replication cohort for the row Close relative in data set, n (%) was incorrect. All values have now been corrected to 0(0%). The publishers would like to apologise for this error and the inconvenience it may have caused.

Published
2020

45. Framework and baseline examination of the German National Cohort (NAKO)

Author

Peters, Annette, Peters, Annette, Greiser, Karin Halina, Göttlicher, Susanne, Ahrens, Wolfgang, Albrecht, Maren, Bamberg, Fabian, Bärnighausen, Till, Becher, Heiko, Berger, Klaus, Beule, Achim, Boeing, Heiner, Bohn, Barbara, Bohnert, Kerstin, Braun, Bettina, Brenner, Hermann, Bülow, Robin, Castell, Stefanie, Damms-Machado, Antje, Dörr, Marcus, Ebert, Nina, Ecker, Margit, Emmel, Carina, Fischer, Beate, Franzke, Claus-Werner, Gastell, Sylvia, Giani, Guido, Günther, Matthias, Günther, Kathrin, Günther, Klaus-Peter, Haerting, Johannes, Haug, Ulrike, Heid, Iris M., Heier, Margit, Heinemeyer, Diana, Hendel, Thomas, Herbolsheimer, Florian, Hirsch, Jochen, Hoffmann, Wolfgang, Holleczek, Bernd, Hölling, Heike, Hörlein, Andreas, Jöckel, Karl-Heinz, Kaaks, Rudolf, Karch, André, Karrasch, Stefan, Kartschmit, Nadja, Kauczor, Hans-Ulrich, Keil, Thomas, Kemmling, Yvonne, Klee, Bianca, Klüppelholz, Birgit, Kluttig, Alexander, Kofink, Lisa, Köttgen, Anna, Kraft, Daniel, Krause, Gérard, Kretz, Lisa, Krist, Lilian, Kühnisch, Jan, Kuß, Oliver, Legath, Nicole, Lehnich, Anna-Therese, Leitzmann, Michael, Lieb, Wolfgang, Linseisen, Jakob, Loeffler, Markus, Macdonald, Anke, Maier-Hein, Klaus H., Mangold, Nina, Meinke-Franze, Claudia, Meisinger, Christa, Melzer, Juliane, Mergarten, Björn, Michels, Karin B., Mikolajczyk, Rafael, Moebus, Susanne, Mueller, Ulrich, Nauck, Matthias, Niendorf, Thoralf, Nikolaou, Konstantin, Obi, Nadia, Ostrzinski, Stefan, Panreck, Leo, Pigeot, Iris, Pischon, Tobias, Pschibul-Thamm, Irene, Rathmann, Wolfgang, Reineke, Achim, Roloff, Stefanie, Rujescu, Dan, Rupf, Stefan, Sander, Oliver, Schikowski, Tamara, Schipf, Sabine, Schirmacher, Peter, Schlett, Christopher L., Schmidt, Börge, Schmidt, Georg, Schmidt, Martin, Schöne, Gina, Schulz, Holger, Schulze, Matthias B., Schweig, Alexandra, Sedlmeier, Anja M., Selder, Sonja, Six-Merker, Julia, Sowade, Ramona, Stang, Andreas, Stegle, Oliver, Steindorf, Karen, Stübs, Gunthard, Swart, Enno, Teismann, Henning, Thiele, Inke, Thierry, Sigrid, Ueffing, Marius, Völzke, Henry, Waniek, Sabina, Weber, Andrea, Werner, Nicole, Wichmann, H.-Erich, Willich, Stefan N., Wirkner, Kerstin, Wolf, Kathrin, Wolff, Robert, Zeeb, Hajo, Zinkhan, Melanie, and Zschocke, Johannes

Published
2022
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47. 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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