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1. Altered cortical synaptic lipid signaling leads to intermediate phenotypes of mental disorders

Author

Tüscher, Oliver, Muthuraman, Muthuraman, Horstmann, Johann-Philipp, Horta, Guilherme, Radyushkin, Konstantin, Baumgart, Jan, Sigurdsson, Torfi, Endle, Heiko, Ji, Haichao, Kuhnhäuser, Prisca, Götz, Jan, Kepser, Lara-Jane, Lotze, Martin, Grabe, Hans J., Völzke, Henry, Leehr, Elisabeth J., Meinert, Susanne, Opel, Nils, Richers, Sebastian, Stroh, Albrecht, Daun, Silvia, Tittgemeyer, Marc, Uphaus, Timo, Steffen, Falk, Zipp, Frauke, Groß, Joachim, Groppa, Sergiu, Dannlowski, Udo, Nitsch, Robert, and Vogt, Johannes

Published
2024
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2. Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries

Author

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

Published
2024
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3. Brain aging patterns in a large and diverse cohort of 49,482 individuals

Author

Yang, Zhijian, Wen, Junhao, Erus, Guray, Govindarajan, Sindhuja T., Melhem, Randa, Mamourian, Elizabeth, Cui, Yuhan, Srinivasan, Dhivya, Abdulkadir, Ahmed, Parmpi, Paraskevi, Wittfeld, Katharina, Grabe, Hans J., Bülow, Robin, Frenzel, Stefan, Tosun, Duygu, Bilgel, Murat, An, Yang, Yi, Dahyun, Marcus, Daniel S., LaMontagne, Pamela, Benzinger, Tammie L. S., Heckbert, Susan R., Austin, Thomas R., Waldstein, Shari R., Evans, Michele K., Zonderman, Alan B., Launer, Lenore J., Sotiras, Aristeidis, Espeland, Mark A., Masters, Colin L., Maruff, Paul, Fripp, Jurgen, Toga, Arthur W., O’Bryant, Sid, Chakravarty, Mallar M., Villeneuve, Sylvia, Johnson, Sterling C., Morris, John C., Albert, Marilyn S., Yaffe, Kristine, Völzke, Henry, Ferrucci, Luigi, Nick Bryan, R., Shinohara, Russell T., Fan, Yong, Habes, Mohamad, Lalousis, Paris Alexandros, Koutsouleris, Nikolaos, Wolk, David A., Resnick, Susan M., Shou, Haochang, Nasrallah, Ilya M., and Davatzikos, Christos

Published
2024
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4. Genetische Diagnostik bei psychischen Erkrankungen im Erwachsenenalter

Author

Kilarski, Laura L., Claus, Isabelle, Binder, Elisabeth B., Degenhardt, Franziska, Domschke, Katharina, Forstner, Andreas J., Grabe, Hans J., Heilbronner, Urs, Müller, Daniel, Nöthen, Markus M., Radtke, Franziska, Rietschel, Marcella, Schulze, Thomas G., Streit, Fabian, Tebartz van Elst, Ludger, Tüscher, Oliver, Deckert, Jürgen, and Schulte, Eva C.

Published
2024
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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. Association analysis between an epigenetic alcohol risk score and blood pressure

Author

Bui, Helena, Keshawarz, Amena, Wang, Mengyao, Lee, Mikyeong, Ratliff, Scott M, Lin, Lisha, Birditt, Kira S, Faul, Jessica D, Peters, Annette, Gieger, Christian, Delerue, Thomas, Kardia, Sharon LR, Zhao, Wei, Guo, Xiuqing, Yao, Jie, Rotter, Jerome I, Li, Yi, Liu, Xue, Liu, Dan, Tavares, Juliana F, Pehlivan, Gökhan, Breteler, Monique MB, Karabegovic, Irma, Ochoa-Rosales, Carolina, Voortman, Trudy, Ghanbari, Mohsen, van Meurs, Joyce BJ, Nasr, Mohamed Kamal, Dörr, Marcus, Grabe, Hans J, London, Stephanie J, Teumer, Alexander, Waldenberger, Melanie, Weir, David R, Smith, Jennifer A, Levy, Daniel, Ma, Jiantao, and Liu, Chunyu

Subjects
Biological Sciences, Genetics, Alcoholism, Alcohol Use and Health, Prevention, Human Genome, Cardiovascular, Hypertension, Clinical Research, Substance Misuse, Good Health and Well Being, Humans, Epigenesis, Genetic, Alcohol Drinking, Blood Pressure, Female, Male, DNA Methylation, Middle Aged, Cross-Sectional Studies, Genome-Wide Association Study, Risk Factors, CpG Islands, Aged, Adult, Epigenetic risk score, DNA methylation, Blood pressure, Alcohol, Clinical Sciences, Paediatrics and Reproductive Medicine
Abstract

BackgroundEpigenome-wide association studies have identified multiple DNA methylation sites (CpGs) associated with alcohol consumption, an important lifestyle risk factor for cardiovascular diseases. This study aimed to test the hypothesis that an alcohol consumption epigenetic risk score (ERS) is associated with blood pressure (BP) traits.ResultsWe implemented an ERS based on a previously reported epigenetic signature of 144 alcohol-associated CpGs in meta-analysis of participants of European ancestry. We found a one-unit increment of ERS was associated with eleven drinks of alcohol consumed per day, on average, across several cohorts (p

Published
2024

7. 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. Genetic and clinical correlates of two neuroanatomical AI dimensions in the Alzheimer’s disease continuum

Author

Wen, Junhao, Yang, Zhijian, Nasrallah, Ilya M., Cui, Yuhan, Erus, Guray, Srinivasan, Dhivya, Abdulkadir, Ahmed, Mamourian, Elizabeth, Hwang, Gyujoon, Singh, Ashish, Bergman, Mark, Bao, Jingxuan, Varol, Erdem, Zhou, Zhen, Boquet-Pujadas, Aleix, Chen, Jiong, Toga, Arthur W., Saykin, Andrew J., Hohman, Timothy J., Thompson, Paul M., Villeneuve, Sylvia, Gollub, Randy, Sotiras, Aristeidis, Wittfeld, Katharina, Grabe, Hans J., Tosun, Duygu, Bilgel, Murat, An, Yang, Marcus, Daniel S., LaMontagne, Pamela, Benzinger, Tammie L., Heckbert, Susan R., Austin, Thomas R., Launer, Lenore J., Espeland, Mark, Masters, Colin L., Maruff, Paul, Fripp, Jurgen, Johnson, Sterling C., Morris, John C., Albert, Marilyn S., Bryan, R. Nick, Resnick, Susan M., Ferrucci, Luigi, Fan, Yong, Habes, Mohamad, Wolk, David, Shen, Li, Shou, Haochang, and Davatzikos, Christos

Published
2024
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10. Validation of the predictive value of BDNF -87 methylation for antidepressant treatment success in severely depressed patients—a randomized rater-blinded trial

Author

Maier, Hannah Benedictine, Neyazi, Alexandra, Bundies, Gabriel L., Meyer-Bockenkamp, Fiona, Bleich, Stefan, Pathak, Hansi, Ziert, Yvonne, Neuhaus, Barbara, Müller, Franz-Josef, Pollmann, Iris, Illig, Thomas, Mücke, Stefanie, Müller, Meike, Möller, Brinja Kira, Oeltze-Jafra, Steffen, Kacprowski, Tim, Voges, Jan, Müntefering, Fabian, Scheiber, Josef, Reif, Andreas, Aichholzer, Mareike, Reif-Leonhard, Christine, Schmidt-Kassow, Maren, Hegerl, Ulrich, Reich, Hanna, Unterecker, Stefan, Weber, Heike, Deckert, Jürgen, Bössel-Debbert, Nicole, Grabe, Hans J., Lucht, Michael, and Frieling, Helge

Published
2024
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12. Multi-omics and pathway analyses of genome-wide associations implicate regulation and immunity in verbal declarative memory performance

Author

Mei, Hao, Simino, Jeannette, Li, Lianna, Jiang, Fan, Bis, Joshua C., Davies, Gail, Hill, W David, Xia, Charley, Gudnason, Vilmundur, Yang, Qiong, Lahti, Jari, Smith, Jennifer A., Kirin, Mirna, De Jager, Philip, Armstrong, Nicola J., Ghanbari, Mohsen, Kolcic, Ivana, Moran, Christopher, Teumer, Alexander, Sargurupremraj, Murali, Mahmud, Shamsed, Fornage, Myriam, Zhao, Wei, Satizabal, Claudia L., Polasek, Ozren, Räikkönen, Katri, Liewald, David C., Homuth, Georg, Callisaya, Michele, Mather, Karen A., Windham, B. Gwen, Zemunik, Tatijana, Palotie, Aarno, Pattie, Alison, van der Auwera, Sandra, Thalamuthu, Anbupalam, Knopman, David S., Rudan, Igor, Starr, John M., Wittfeld, Katharina, Kochan, Nicole A., Griswold, Michael E., Vitart, Veronique, Brodaty, Henry, Gottesman, Rebecca, Cox, Simon R., Psaty, Bruce M., Boerwinkle, Eric, Chasman, Daniel I., Grodstein, Francine, Sachdev, Perminder S., Srikanth, Velandai, Hayward, Caroline, Wilson, James F., Eriksson, Johan G., Kardia, Sharon L. R., Grabe, Hans J., Bennett, David A., Ikram, M. Arfan, Deary, Ian J., van Duijn, Cornelia M., Launer, Lenore, Fitzpatrick, Annette L., Seshadri, Sudha, Bressler, Jan, Debette, Stephanie, and Mosley, Jr, Thomas H.

Published
2024
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13. 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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14. Exploring the burden of past trauma in East Germany

Author

Altweck, Laura, primary, Hahm, Stefanie, additional, Metsch, Miriam, additional, Schmidt, Silke, additional, Ulke, Christine, additional, Fleischer, Toni, additional, Helmert, Claudia, additional, Speerforck, Sven, additional, Schomerus, Georg, additional, Grabe, Hans J., additional, Klinger-König, Johanna, additional, Völzke, Henry, additional, Beutel, Manfred E., additional, Brähler, Elmar, additional, and Muehlan, Holger, additional

Published
2024
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16. Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning

Author

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

Published
2024
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18. 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

19. Epigenetic and integrative cross-omics analyses of cerebral white matter hyperintensities on MRI

Author

Yang, Yunju, Knol, Maria J, Wang, Ruiqi, Mishra, Aniket, Liu, Dan, Luciano, Michelle, Teumer, Alexander, Armstrong, Nicola, Bis, Joshua C, Jhun, Min A, Li, Shuo, Adams, Hieab HH, Aziz, Nasir Ahmad, Bastin, Mark E, Bourgey, Mathieu, Brody, Jennifer A, Frenzel, Stefan, Gottesman, Rebecca F, Hosten, Norbert, Hou, Lifang, Kardia, Sharon LR, Lohner, Valerie, Marquis, Pascale, Maniega, Susana Muñoz, Satizabal, Claudia L, Sorond, Farzaneh A, Valdés Hernández, Maria C, van Duijn, Cornelia M, Vernooij, Meike W, Wittfeld, Katharina, Yang, Qiong, Zhao, Wei, Boerwinkle, Eric, Levy, Daniel, Deary, Ian J, Jiang, Jiyang, Mather, Karen A, Mosley, Thomas H, Psaty, Bruce M, Sachdev, Perminder S, Smith, Jennifer A, Sotoodehnia, Nona, DeCarli, Charles S, Breteler, Monique MB, Ikram, M Arfan, Grabe, Hans J, Wardlaw, Joanna, Longstreth, WT, Launer, Lenore J, Seshadri, Sudha, Debette, Stephanie, and Fornage, Myriam

Subjects
Health Sciences, Genetics, Aging, Brain Disorders, Neurosciences, Human Genome, Biotechnology, Aetiology, 2.1 Biological and endogenous factors, Middle Aged, Humans, Aged, White Matter, Genome-Wide Association Study, Brain, DNA Methylation, Magnetic Resonance Imaging, Epigenesis, Genetic, Protein-Arginine N-Methyltransferases, Repressor Proteins, epigenome-wide association study, white matter hyperintensities, cerebral small vessel disease, integrative cross-omics analysis, blood-brain barrier dysfunction, blood–brain barrier dysfunction, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences, Psychology
Abstract

Cerebral white matter hyperintensities on MRI are markers of cerebral small vessel disease, a major risk factor for dementia and stroke. Despite the successful identification of multiple genetic variants associated with this highly heritable condition, its genetic architecture remains incompletely understood. More specifically, the role of DNA methylation has received little attention. We investigated the association between white matter hyperintensity burden and DNA methylation in blood at ∼450 000 cytosine-phosphate-guanine (CpG) sites in 9732 middle-aged to older adults from 14 community-based studies. Single CpG and region-based association analyses were carried out. Functional annotation and integrative cross-omics analyses were performed to identify novel genes underlying the relationship between DNA methylation and white matter hyperintensities. We identified 12 single CpG and 46 region-based DNA methylation associations with white matter hyperintensity burden. Our top discovery single CpG, cg24202936 (P = 7.6 × 10-8), was associated with F2 expression in blood (P = 6.4 × 10-5) and co-localized with FOLH1 expression in brain (posterior probability = 0.75). Our top differentially methylated regions were in PRMT1 and in CCDC144NL-AS1, which were also represented in single CpG associations (cg17417856 and cg06809326, respectively). Through Mendelian randomization analyses cg06809326 was putatively associated with white matter hyperintensity burden (P = 0.03) and expression of CCDC144NL-AS1 possibly mediated this association. Differentially methylated region analysis, joint epigenetic association analysis and multi-omics co-localization analysis consistently identified a role of DNA methylation near SH3PXD2A, a locus previously identified in genome-wide association studies of white matter hyperintensities. Gene set enrichment analyses revealed functions of the identified DNA methylation loci in the blood-brain barrier and in the immune response. Integrative cross-omics analysis identified 19 key regulatory genes in two networks related to extracellular matrix organization, and lipid and lipoprotein metabolism. A drug-repositioning analysis indicated antihyperlipidaemic agents, more specifically peroxisome proliferator-activated receptor-alpha, as possible target drugs for white matter hyperintensities. Our epigenome-wide association study and integrative cross-omics analyses implicate novel genes influencing white matter hyperintensity burden, which converged on pathways related to the immune response and to a compromised blood-brain barrier possibly due to disrupted cell-cell and cell-extracellular matrix interactions. The results also suggest that antihyperlipidaemic therapy may contribute to lowering risk for white matter hyperintensities possibly through protection against blood-brain barrier disruption.

Published
2023

20. Genome-wide meta-analyses reveal novel loci for verbal short-term memory and learning

Author

Lahti, Jari, Tuominen, Samuli, Yang, Qiong, Pergola, Giulio, Ahmad, Shahzad, Amin, Najaf, Armstrong, Nicola J, Beiser, Alexa, Bey, Katharina, Bis, Joshua C, Boerwinkle, Eric, Bressler, Jan, Campbell, Archie, Campbell, Harry, Chen, Qiang, Corley, Janie, Cox, Simon R, Davies, Gail, De Jager, Philip L, Derks, Eske M, Faul, Jessica D, Fitzpatrick, Annette L, Fohner, Alison E, Ford, Ian, Fornage, Myriam, Gerring, Zachary, Grabe, Hans J, Grodstein, Francine, Gudnason, Vilmundur, Simonsick, Eleanor, Holliday, Elizabeth G, Joshi, Peter K, Kajantie, Eero, Kaprio, Jaakko, Karell, Pauliina, Kleineidam, Luca, Knol, Maria J, Kochan, Nicole A, Kwok, John B, Leber, Markus, Lam, Max, Lee, Teresa, Li, Shuo, Loukola, Anu, Luck, Tobias, Marioni, Riccardo E, Mather, Karen A, Medland, Sarah, Mirza, Saira S, Nalls, Mike A, Nho, Kwangsik, O’Donnell, Adrienne, Oldmeadow, Christopher, Painter, Jodie, Pattie, Alison, Reppermund, Simone, Risacher, Shannon L, Rose, Richard J, Sadashivaiah, Vijay, Scholz, Markus, Satizabal, Claudia L, Schofield, Peter W, Schraut, Katharina E, Scott, Rodney J, Simino, Jeannette, Smith, Albert V, Smith, Jennifer A, Stott, David J, Surakka, Ida, Teumer, Alexander, Thalamuthu, Anbupalam, Trompet, Stella, Turner, Stephen T, van der Lee, Sven J, Villringer, Arno, Völker, Uwe, Wilson, Robert S, Wittfeld, Katharina, Vuoksimaa, Eero, Xia, Rui, Yaffe, Kristine, Yu, Lei, Zare, Habil, Zhao, Wei, Ames, David, Attia, John, Bennett, David A, Brodaty, Henry, Chasman, Daniel I, Goldman, Aaron L, Hayward, Caroline, Ikram, M Arfan, Jukema, J Wouter, Kardia, Sharon LR, Lencz, Todd, Loeffler, Markus, Mattay, Venkata S, Palotie, Aarno, Psaty, Bruce M, and Ramirez, Alfredo

Subjects
Biological Psychology, Psychology, Genetics, Human Genome, Dementia, Behavioral and Social Science, Brain Disorders, Acquired Cognitive Impairment, Mental Health, Biotechnology, Aging, Clinical Research, Neurosciences, Basic Behavioral and Social Science, Aetiology, 2.1 Biological and endogenous factors, Neurological, Mental health, Memory, Short-Term, Learning, Verbal Learning, Multifactorial Inheritance, Brain, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

Understanding the genomic basis of memory processes may help in combating neurodegenerative disorders. Hence, we examined the associations of common genetic variants with verbal short-term memory and verbal learning in adults without dementia or stroke (N = 53,637). We identified novel loci in the intronic region of CDH18, and at 13q21 and 3p21.1, as well as an expected signal in the APOE/APOC1/TOMM40 region. These results replicated in an independent sample. Functional and bioinformatic analyses supported many of these loci and further implicated POC1. We showed that polygenic score for verbal learning associated with brain activation in right parieto-occipital region during working memory task. Finally, we showed genetic correlations of these memory traits with several neurocognitive and health outcomes. Our findings suggest a role of several genomic loci in verbal memory processes.

Published
2022

22. 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

23. 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

27. Depression, cardiometabolic disease, and their co-occurrence after childhood maltreatment: an individual participant data meta-analysis including over 200,000 participants

Author

Souama, Camille, Lamers, Femke, Milaneschi, Yuri, Vinkers, Christiaan H., Defina, Serena, Garvert, Linda, Stein, Frederike, Woofenden, Tom, Brosch, Katharina, Dannlowski, Udo, Galenkamp, Henrike, de Graaf, Ron, Jaddoe, Vincent W. V., Lok, Anja, van Rijn, Bas B., Völzke, Henry, Cecil, Charlotte A. M., Felix, Janine F., Grabe, Hans J., Kircher, Tilo, Lekadir, Karim, Have, Margreet ten, Walton, Esther, and Penninx, Brenda W. J. H.

Published
2023
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29. Circulating Metabolome and White Matter Hyperintensities in Women and Men

Author

Sliz, Eeva, Shin, Jean, Ahmad, Shahzad, Williams, Dylan M, Frenzel, Stefan, Gauß, Friederike, Harris, Sarah E, Henning, Ann-Kristin, Hernandez, Maria Valdes, Hu, Yi-Han, Jiménez, Beatriz, Sargurupremraj, Muralidharan, Sudre, Carole, Wang, Ruiqi, Wittfeld, Katharina, Yang, Qiong, Wardlaw, Joanna M, Völzke, Henry, Vernooij, Meike W, Schott, Jonathan M, Richards, Marcus, Proitsi, Petroula, Nauck, Matthias, Lewis, Matthew R, Launer, Lenore, Hosten, Norbert, Grabe, Hans J, Ghanbari, Mohsen, Deary, Ian J, Cox, Simon R, Chaturvedi, Nishi, Barnes, Josephine, Rotter, Jerome I, Debette, Stephanie, Ikram, M Arfan, Fornage, Myriam, Paus, Tomas, Seshadri, Sudha, Pausova, Zdenka, and Group, for the NeuroCHARGE Working

Subjects
Epidemiology, Biomedical and Clinical Sciences, Health Sciences, Prevention, Clinical Research, Aging, Aged, Brain, Diabetes Mellitus, Type 2, Female, Humans, Magnetic Resonance Imaging, Male, Metabolome, Middle Aged, White Matter, NeuroCHARGE Working Group, brain, glucuronic acid, hydroxyphenylpyruvate, lipid ratios, lipidomics, lipids, lysophosphatidylcholines, metabolomics, sphingomyelins, white matter, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Public Health and Health Services, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences, Sports science and exercise
Abstract

BackgroundWhite matter hyperintensities (WMH), identified on T2-weighted magnetic resonance images of the human brain as areas of enhanced brightness, are a major risk factor of stroke, dementia, and death. There are no large-scale studies testing associations between WMH and circulating metabolites.MethodsWe studied up to 9290 individuals (50.7% female, average age 61 years) from 15 populations of 8 community-based cohorts. WMH volume was quantified from T2-weighted or fluid-attenuated inversion recovery images or as hypointensities on T1-weighted images. Circulating metabolomic measures were assessed with mass spectrometry and nuclear magnetic resonance spectroscopy. Associations between WMH and metabolomic measures were tested by fitting linear regression models in the pooled sample and in sex-stratified and statin treatment-stratified subsamples. Our basic models were adjusted for age, sex, age×sex, and technical covariates, and our fully adjusted models were also adjusted for statin treatment, hypertension, type 2 diabetes, smoking, body mass index, and estimated glomerular filtration rate. Population-specific results were meta-analyzed using the fixed-effect inverse variance-weighted method. Associations with false discovery rate (FDR)-adjusted P values (PFDR)

Published
2022

32. 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

33. Insulin-Like Growth Factor, Inflammation, and MRI Markers of Alzheimer’s Disease in Predominantly Middle-Aged Adults

Author

Wittfeld, Katharina, Raman, Mekala R, Conner, Sarah C, Aslam, Asra, Teumer, Alexander, Nauck, Matthias, Hosten, Norbert, Habes, Mohamad, DeCarli, Charles, Vasan, Ramachandran S, Beiser, Alexa S, Himali, Jayandra J, Seshadri, Sudha, Grabe, Hans J, and Satizabal, Claudia L

Subjects
Paediatrics, Biomedical and Clinical Sciences, Clinical Sciences, Neurosciences, Brain Disorders, Neurodegenerative, Aging, Cardiovascular, Alzheimer's Disease, Dementia, Cerebrovascular, Acquired Cognitive Impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Clinical Research, 2.1 Biological and endogenous factors, Neurological, Adult, Alzheimer Disease, Biomarkers, C-Reactive Protein, Female, Humans, Inflammation, Insulin-Like Growth Factor Binding Protein 3, Insulin-Like Growth Factor I, Magnetic Resonance Imaging, Male, Middle Aged, Alzheimer's disease endophenotype, C-reactive protein, cohort study, epidemiology, hippocampus, insulin-like growth factor, neuroimaging, white matter hyperintensity, Alzheimer’s disease endophenotype, Cognitive Sciences, Neurology & Neurosurgery, Clinical sciences, Biological psychology
Abstract

BackgroundInsulin-like growth factor 1 (IGF-1) signaling has been implicated in Alzheimer's disease pathogenesis, and further evidence suggests inflammation can be a moderator of this association. However, most research to date has been conducted on older adults.ObjectiveTo investigate the association of serum IGF-1 and IGF binding protein 3 (IGFBP-3) concentrations with MRI markers of Alzheimer's disease in predominantly middle-aged adults, and further assess moderation by chronic inflammation.MethodsWe included participants from the Framingham Heart Study (n = 1,852, mean age 46±8, 46% men) and the Study of Health in Pomerania (n = 674, mean age 50±13, 42% men) with available serum IGF-1, IFGBP-3, as well as brain MRI. IGF-1 and IFGBP-3 were related to MRI outcomes (i.e., total brain, cortical gray matter, white matter, white matter hyperintensities (WMH), and hippocampal volumes) using multivariable regression models adjusting for potential confounders. Subgroup analyses by C-reactive protein (CRP) concentrations were also performed. Cohort-specific summary statistics were meta-analyzed using random-effects models and corrected for multiple comparisons.ResultsMeta-analysis results revealed that higher IGF-1 concentrations were associated with lower WMH (estimate [β] [95% CI], -0.05 [-0.09, -0.02], p = 0.006) and larger hippocampal volumes (0.07 [0.02, 0.12], p = 0.01), independent of vascular risk factors. These associations occurred predominantly in individuals with CRP concentrations

Published
2022

34. 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

35. Alexithymic but Not Autistic Traits Impair Prosocial Behavior

Author

Lischke, Alexander, Freyberger, Harald J., Grabe, Hans J., Mau-Moeller, Anett, and Pahnke, Rike

Abstract

Social impairments are a core feature of autism-spectrum disorders. However, there is a considerable variability in these impairments. Most autistic individuals show large impairments in social functioning but some autistic individuals show small impairments in social functioning. The variability of these impairments has been attributed to the presence or absence of alexithymia. To address this issue, we capitalized on the fact that alexithymic and autistic traits are broadly distributed in the population. This allowed us to investigate how alexithymic and autistic traits affect social functioning in healthy individuals. Healthy individuals showed impairments on a resource-allocation task that were due to alexithymic but not autistic traits. These findings suggest that alexithymic rather than autistic traits impair prosocial behavior across the autism-spectrum.

Published
2022
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36. Multi-ancestry genome-wide gene–sleep interactions identify novel loci for blood pressure

Author

Wang, Heming, Noordam, Raymond, Cade, Brian E, Schwander, Karen, Winkler, Thomas W, Lee, Jiwon, Sung, Yun Ju, Bentley, Amy R, Manning, Alisa K, Aschard, Hugues, Kilpeläinen, Tuomas O, Ilkov, Marjan, Brown, Michael R, Horimoto, Andrea R, Richard, Melissa, Bartz, Traci M, Vojinovic, Dina, Lim, Elise, Nierenberg, Jovia L, Liu, Yongmei, Chitrala, Kumaraswamynaidu, Rankinen, Tuomo, Musani, Solomon K, Franceschini, Nora, Rauramaa, Rainer, Alver, Maris, Zee, Phyllis C, Harris, Sarah E, van der Most, Peter J, Nolte, Ilja M, Munroe, Patricia B, Palmer, Nicholette D, Kühnel, Brigitte, Weiss, Stefan, Wen, Wanqing, Hall, Kelly A, Lyytikäinen, Leo-Pekka, O’Connell, Jeff, Eiriksdottir, Gudny, Launer, Lenore J, de Vries, Paul S, Arking, Dan E, Chen, Han, Boerwinkle, Eric, Krieger, Jose E, Schreiner, Pamela J, Sidney, Stephen, Shikany, James M, Rice, Kenneth, Chen, Yii-Der Ida, Gharib, Sina A, Bis, Joshua C, Luik, Annemarie I, Ikram, M Arfan, Uitterlinden, André G, Amin, Najaf, Xu, Hanfei, Levy, Daniel, He, Jiang, Lohman, Kurt K, Zonderman, Alan B, Rice, Treva K, Sims, Mario, Wilson, Gregory, Sofer, Tamar, Rich, Stephen S, Palmas, Walter, Yao, Jie, Guo, Xiuqing, Rotter, Jerome I, Biermasz, Nienke R, Mook-Kanamori, Dennis O, Martin, Lisa W, Barac, Ana, Wallace, Robert B, Gottlieb, Daniel J, Komulainen, Pirjo, Heikkinen, Sami, Mägi, Reedik, Milani, Lili, Metspalu, Andres, Starr, John M, Milaneschi, Yuri, Waken, RJ, Gao, Chuan, Waldenberger, Melanie, Peters, Annette, Strauch, Konstantin, Meitinger, Thomas, Roenneberg, Till, Völker, Uwe, Dörr, Marcus, Shu, Xiao-Ou, Mukherjee, Sutapa, Hillman, David R, Kähönen, Mika, Wagenknecht, Lynne E, Gieger, Christian, Grabe, Hans J, and Zheng, Wei

Subjects
Human Genome, Sleep Research, Clinical Research, Biotechnology, Hypertension, Genetics, Aetiology, 2.1 Biological and endogenous factors, Cardiovascular, Blood Pressure, Genetic Loci, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide, Sleep, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Long and short sleep duration are associated with elevated blood pressure (BP), possibly through effects on molecular pathways that influence neuroendocrine and vascular systems. To gain new insights into the genetic basis of sleep-related BP variation, we performed genome-wide gene by short or long sleep duration interaction analyses on four BP traits (systolic BP, diastolic BP, mean arterial pressure, and pulse pressure) across five ancestry groups in two stages using 2 degree of freedom (df) joint test followed by 1df test of interaction effects. Primary multi-ancestry analysis in 62,969 individuals in stage 1 identified three novel gene by sleep interactions that were replicated in an additional 59,296 individuals in stage 2 (stage 1 + 2 Pjoint

Published
2021

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

Author

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

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

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

Published
2021

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

Author

Boen, Rune, Kaufmann, Tobias, van der Meer, Dennis, Frei, Oleksandr, Agartz, Ingrid, Ames, David, Andersson, Micael, Armstrong, Nicola J., Artiges, Eric, Atkins, Joshua R., Bauer, Jochen, Benedetti, Francesco, Boomsma, Dorret I., Brodaty, Henry, Brosch, Katharina, Buckner, Randy L., Cairns, Murray J., Calhoun, Vince, Caspers, Svenja, Cichon, Sven, Corvin, Aiden P., Crespo-Facorro, Benedicto, Dannlowski, Udo, David, Friederike S., de Geus, Eco J.C., de Zubicaray, Greig I., Desrivières, Sylvane, Doherty, Joanne L., Donohoe, Gary, Ehrlich, Stefan, Eising, Else, Espeseth, Thomas, Fisher, Simon E., Forstner, Andreas J., Fortaner-Uyà, Lidia, Frouin, Vincent, Fukunaga, Masaki, Ge, Tian, Glahn, David C., Goltermann, Janik, Grabe, Hans J., Green, Melissa J., Groenewold, Nynke A., Grotegerd, Dominik, Grøntvedt, Gøril Rolfseng, Hahn, Tim, Hashimoto, Ryota, Hehir-Kwa, Jayne Y., Henskens, Frans A., Holmes, Avram J., Håberg, Asta K., Haavik, Jan, Jacquemont, Sebastien, Jansen, Andreas, Jockwitz, Christiane, Jönsson, Erik G., Kikuchi, Masataka, Kircher, Tilo, Kumar, Kuldeep, Le Hellard, Stephanie, Leu, Costin, Linden, David E., Liu, Jingyu, Loughnan, Robert, Mather, Karen A., McMahon, Katie L., McRae, Allan F., Medland, Sarah E., Meinert, Susanne, Moreau, Clara A., Morris, Derek W., Mowry, Bryan J., Mühleisen, Thomas W., Nenadić, Igor, Nöthen, Markus M., Nyberg, Lars, Ophoff, Roel A., Owen, Michael J., Pantelis, Christos, Paolini, Marco, Paus, Tomas, Pausova, Zdenka, Persson, Karin, Quidé, Yann, Marques, Tiago Reis, Sachdev, Perminder S., Sando, Sigrid B., Schall, Ulrich, Scott, Rodney J., Selbæk, Geir, Shumskaya, Elena, Silva, Ana I., Sisodiya, Sanjay M., Stein, Frederike, Stein, Dan J., Straube, Benjamin, Streit, Fabian, Strike, Lachlan T., Teumer, Alexander, Teutenberg, Lea, Thalamuthu, Anbupalam, Tooney, Paul A., Tordesillas-Gutierrez, Diana, Trollor, Julian N., van ’t Ent, Dennis, van den Bree, Marianne B.M., van Haren, Neeltje E.M., Vázquez-Bourgon, Javier, Völzke, Henry, Wen, Wei, Wittfeld, Katharina, Ching, Christopher R.K., Westlye, Lars T., Thompson, Paul M., Bearden, Carrie E., Selmer, Kaja K., Alnæs, Dag, Andreassen, Ole A., and Sønderby, Ida E.

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

Author

Groenewold, Nynke A., Bas-Hoogendam, Janna Marie, Amod, Alyssa R., Laansma, Max A., Van Velzen, Laura S., Aghajani, Moji, Hilbert, Kevin, Oh, Hyuntaek, Salas, Ramiro, Jackowski, Andrea P., Pan, Pedro M., Salum, Giovanni A., Blair, James R., Blair, Karina S., Hirsch, Joy, Pantazatos, Spiro P., Schneier, Franklin R., Talati, Ardesheer, Roelofs, Karin, Volman, Inge, Blanco-Hinojo, Laura, Cardoner, Narcís, Pujol, Jesus, Beesdo-Baum, Katja, Ching, Christopher R. K., Thomopoulos, Sophia I., 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 C., Wu, Mon-Ju, Bayram, Ali, Canli, Mesut, Tükel, Raşit, Westenberg, P. Michiel, Heeren, Alexandre, Cremers, Henk R., Hofmann, David, Straube, Thomas, Doruyter, Alexander G. G., Lochner, Christine, Peterburs, Jutta, Van Tol, Marie-José, Gur, Raquel E., Kaczkurkin, Antonia N., Larsen, Bart, Satterthwaite, Theodore D., Filippi, Courtney A., Gold, Andrea L., Harrewijn, Anita, Zugman, André, Bülow, Robin, Grabe, Hans J., Völzke, Henry, Wittfeld, Katharina, Böhnlein, Joscha, Dohm, Katharina, Kugel, Harald, Schrammen, Elisabeth, Zwanzger, Peter, Leehr, Elisabeth J., Sindermann, Lisa, Ball, Tali M., Fonzo, Gregory A., Paulus, Martin P., Simmons, Alan, Stein, Murray B., Klumpp, Heide, Phan, K. Luan, Furmark, Tomas, Månsson, Kristoffer N. T., Manzouri, Amirhossein, Avery, Suzanne N., Blackford, Jennifer Urbano, Clauss, Jacqueline A., Feola, Brandee, Harper, Jennifer C., Sylvester, Chad M., Lueken, Ulrike, Veltman, Dick J., Winkler, Anderson M., Jahanshad, Neda, Pine, Daniel S., Thompson, Paul M., Stein, Dan J., and Van der Wee, Nic J. A.

Published
2023
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41. Medical Image Harmonization Using Deep Learning Based Canonical Mapping: Toward Robust and Generalizable Learning in Imaging

Author

Bashyam, Vishnu M., Doshi, Jimit, Erus, Guray, Srinivasan, Dhivya, Abdulkadir, Ahmed, Habes, Mohamad, Fan, Yong, Masters, Colin L., Maruff, Paul, Zhuo, Chuanjun, Völzke, Henry, Johnson, Sterling C., Fripp, Jurgen, Koutsouleris, Nikolaos, Satterthwaite, Theodore D., Wolf, Daniel H., Gur, Raquel E., Gur, Ruben C., Morris, John C., Albert, Marilyn S., Grabe, Hans J., Resnick, Susan M., Bryan, R. Nick, Wolk, David A., Shou, Haochang, Nasrallah, Ilya M., and Davatzikos, Christos

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

Conventional and deep learning-based methods have shown great potential in the medical imaging domain, as means for deriving diagnostic, prognostic, and predictive biomarkers, and by contributing to precision medicine. However, these methods have yet to see widespread clinical adoption, in part due to limited generalization performance across various imaging devices, acquisition protocols, and patient populations. In this work, we propose a new paradigm in which data from a diverse range of acquisition conditions are "harmonized" to a common reference domain, where accurate model learning and prediction can take place. By learning an unsupervised image to image canonical mapping from diverse datasets to a reference domain using generative deep learning models, we aim to reduce confounding data variation while preserving semantic information, thereby rendering the learning task easier in the reference domain. We test this approach on two example problems, namely MRI-based brain age prediction and classification of schizophrenia, leveraging pooled cohorts of neuroimaging MRI data spanning 9 sites and 9701 subjects. Our results indicate a substantial improvement in these tasks in out-of-sample data, even when training is restricted to a single site.

Published
2020

43. 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

46. 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

47. The Brain Chart of Aging: Machine‐learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans

Author

Habes, Mohamad, Pomponio, Raymond, Shou, Haochang, Doshi, Jimit, Mamourian, Elizabeth, Erus, Guray, Nasrallah, Ilya, Launer, Lenore J, Rashid, Tanweer, Bilgel, Murat, Fan, Yong, Toledo, Jon B, Yaffe, Kristine, Sotiras, Aristeidis, Srinivasan, Dhivya, Espeland, Mark, Masters, Colin, Maruff, Paul, Fripp, Jurgen, Völzk, Henry, Johnson, Sterling C, Morris, John C, Albert, Marilyn S, Miller, Michael I, Bryan, R Nick, Grabe, Hans J, Resnick, Susan M, Wolk, David A, Davatzikos, Christos, and for the iSTAGING consortium, the Preclinical AD consortium

Subjects
Biological Psychology, Psychology, Biomedical Imaging, Aging, Neurosciences, Alzheimer's Disease, Dementia, Clinical Research, Acquired Cognitive Impairment, Brain Disorders, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurodegenerative, Aetiology, 2.1 Biological and endogenous factors, Mental health, Neurological, Adult, Aged, Aged, 80 and over, Amyloid beta-Peptides, Atrophy, Biomarkers, Brain, Cerebral Small Vessel Diseases, Cognitive Dysfunction, Disease Progression, Female, Humans, Image Processing, Computer-Assisted, Machine Learning, Magnetic Resonance Imaging, Male, Middle Aged, Neuropsychological Tests, White Matter, Young Adult, Alzheimer's disease pathology, beta-amyloid, brain aging, brain signatures, cognitive testing, harmonized neuroimaging cohorts, MRI, Neuroimaging, PET, preclinical Alzheimer's disease, small vessel ischemic disease, tau, iSTAGING consortium, the Preclinical AD consortium, the ADNI, and the CARDIA studies, Clinical Sciences, Geriatrics, Clinical sciences, Biological psychology
Abstract

IntroductionRelationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects).MethodsThree brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD.ResultsWMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD.DiscussionA Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.

Published
2021

49. 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

50. 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

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