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1. Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm.

Author: Jiang, Yuchao, Luo, Cheng, Wang, Jijun, Palaniyappan, Lena, Chang, Xiao, Xiang, Shitong, Zhang, Jie, Duan, Mingjun, Huang, Huan, Gaser, Christian, Nemoto, Kiyotaka, Miura, Kenichiro, Hashimoto, Ryota, Westlye, Lars, Richard, Genevieve, Fernandez-Cabello, Sara, Parker, Nadine, Andreassen, Ole, Kircher, Tilo, Nenadić, Igor, Stein, Frederike, Thomas-Odenthal, Florian, Teutenberg, Lea, Usemann, Paula, Dannlowski, Udo, Hahn, Tim, Grotegerd, Dominik, Meinert, Susanne, Lencer, Rebekka, Tang, Yingying, Zhang, Tianhong, Li, Chunbo, Yue, Weihua, Zhang, Yuyanan, Yu, Xin, Zhou, Enpeng, Lin, Ching-Po, Tsai, Shih-Jen, Rodrigue, Amanda, Glahn, David, Pearlson, Godfrey, Blangero, John, Karuk, Andriana, Pomarol-Clotet, Edith, Salvador, Raymond, Fuentes-Claramonte, Paola, Garcia-León, María, Spalletta, Gianfranco, Piras, Fabrizio, Vecchio, Daniela, Banaj, Nerisa, Cheng, Jingliang, Liu, Zhening, Yang, Jie, Gonul, Ali, Uslu, Ozgul, Burhanoglu, Birce, Uyar Demir, Aslihan, Rootes-Murdy, Kelly, Calhoun, Vince, Sim, Kang, Green, Melissa, Quidé, Yann, Chung, Young, Kim, Woo-Sung, Sponheim, Scott, Demro, Caroline, Ramsay, Ian, Iasevoli, Felice, de Bartolomeis, Andrea, Barone, Annarita, Ciccarelli, Mariateresa, Brunetti, Arturo, Cocozza, Sirio, Pontillo, Giuseppe, Tranfa, Mario, Park, Min, Kirschner, Matthias, Georgiadis, Foivos, Kaiser, Stefan, Van Rheenen, Tamsyn, Rossell, Susan, Hughes, Matthew, Woods, William, Carruthers, Sean, Sumner, Philip, Ringin, Elysha, Spaniel, Filip, Skoch, Antonin, Tomecek, David, Homan, Philipp, Homan, Stephanie, Omlor, Wolfgang, Cecere, Giacomo, Nguyen, Dana, Preda, Adrian, Thomopoulos, Sophia, Jahanshad, Neda, Cui, Long-Biao, and Yao, Dezhong
Subjects: Humans, Schizophrenia, Male, Female, Adult, Algorithms, Magnetic Resonance Imaging, Gray Matter, Machine Learning, Middle Aged, Brain, Cross-Sectional Studies, Europe, Neuroimaging, Reproducibility of Results, North America, Hippocampus
Abstract: Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.
Published: 2024

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

Author: Yu, Yuetong, Cui, Hao‐Qi, Haas, Shalaila S, New, Faye, Sanford, Nicole, Yu, Kevin, Zhan, Denghuang, Yang, Guoyuan, Gao, Jia‐Hong, Wei, Dongtao, Qiu, Jiang, Banaj, Nerisa, Boomsma, Dorret I, Breier, Alan, Brodaty, Henry, Buckner, Randy L, Buitelaar, Jan K, Cannon, Dara M, Caseras, Xavier, Clark, Vincent P, Conrod, Patricia J, Crivello, Fabrice, Crone, Eveline A, Dannlowski, Udo, Davey, Christopher G, de Haan, Lieuwe, de Zubicaray, Greig I, Di Giorgio, Annabella, Fisch, Lukas, Fisher, Simon E, Franke, Barbara, Glahn, David C, Grotegerd, Dominik, Gruber, Oliver, Gur, Raquel E, Gur, Ruben C, Hahn, Tim, Harrison, Ben J, Hatton, Sean, Hickie, Ian B, Pol, Hilleke E Hulshoff, Jamieson, Alec J, Jernigan, Terry L, Jiang, Jiyang, Kalnin, Andrew J, Kang, Sim, Kochan, Nicole A, Kraus, Anna, Lagopoulos, Jim, Lazaro, Luisa, McDonald, Brenna C, McDonald, Colm, McMahon, Katie L, Mwangi, Benson, Piras, Fabrizio, Rodriguez‐Cruces, Raul, Royer, Jessica, Sachdev, Perminder S, Satterthwaite, Theodore D, Saykin, Andrew J, Schumann, Gunter, Sevaggi, Pierluigi, Smoller, Jordan W, Soares, Jair C, Spalletta, Gianfranco, Tamnes, Christian K, Trollor, Julian N, Ent, Dennis Van't, Vecchio, Daniela, Walter, Henrik, Wang, Yang, Weber, Bernd, Wen, Wei, Wierenga, Lara M, Williams, Steven CR, Wu, Mon‐Ju, Zunta‐Soares, Giovana B, Bernhardt, Boris, Thompson, Paul, Frangou, Sophia, Ge, Ruiyang, and Group, ENIGMA‐Lifespan Working
Subjects: Biological Psychology, Psychology, Aging, Clinical Research, Neurosciences, Neurological, Mental health, Humans, Adolescent, Female, Aged, Adult, Child, Young Adult, Male, Brain, Aged, 80 and over, Child, Preschool, Middle Aged, Magnetic Resonance Imaging, Neuroimaging, Sample Size, benchmarking, brain aging, brainAGE, ENIGMA‐Lifespan Working Group, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract: Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.
Published: 2024

3. Feasibility and Utility of mHealth for the Remote Monitoring of Parkinson Disease: Ancillary Study of the PD_manager Randomized Controlled Trial

Author: Gatsios, Dimitris, Antonini, Angelo, Gentile, Giovanni, Marcante, Andrea, Pellicano, Clelia, Macchiusi, Lucia, Assogna, Francesca, Spalletta, Gianfranco, Gage, Heather, Touray, Morro, Timotijevic, Lada, Hodgkins, Charo, Chondrogiorgi, Maria, Rigas, George, Fotiadis, Dimitrios I, and Konitsiotis, Spyridon
Subjects: Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract: BackgroundMobile health, predominantly wearable technology and mobile apps, have been considered in Parkinson disease to provide valuable ecological data between face-to-face visits and improve monitoring of motor symptoms remotely. ObjectiveWe explored the feasibility of using a technology-based mHealth platform comprising a smartphone in combination with a smartwatch and a pair of smart insoles, described in this study as the PD_manager system, to collect clinically meaningful data. We also explored outcomes and disease-related factors that are important determinants to establish feasibility. Finally, we further validated a tremor evaluation method with data collected while patients performed their daily activities. MethodsPD_manager trial was an open-label parallel group randomized study.The mHealth platform consists of a wristband, a pair of sensor insoles, a smartphone (with dedicated mobile Android apps) and a knowledge platform serving as the cloud backend. Compliance was assessed with statistical analysis and the factors affecting it using appropriate regression analysis. The correlation of the scores of our previous algorithm for tremor evaluation and the respective Unified Parkinson’s Disease Rating Scale estimations by clinicians were explored. ResultsOf the 75 study participants, 65 (87%) completed the protocol. They used the PD_manager system for a median 11.57 (SD 3.15) days. Regression analysis suggests that the main factor associated with high use was caregivers’ burden. Motor Aspects of Experiences of Daily Living and patients’ self-rated health status also influence the system’s use. Our algorithm provided clinically meaningful data for the detection and evaluation of tremor. ConclusionsWe found that PD patients, regardless of their demographics and disease characteristics, used the system for 11 to 14 days. The study further supports that mHealth can be an effective tool for the ecologically valid, passive, unobtrusive monitoring and evaluation of symptoms. Future studies will be required to demonstrate that an mHealth platform can improve disease management and care. Trial RegistrationISRCTN Registry ISRCTN17396879; http://www.isrctn.com/ISRCTN17396879 International Registered Report Identifier (IRRID)RR2-10.1186/s13063-018-2767-4
Published: 2020
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4. Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study

Author: Georgiadis, Foivos, Larivière, Sara, Glahn, David, Hong, L. Elliot, Kochunov, Peter, Mowry, Bryan, Loughland, Carmel, Pantelis, Christos, Henskens, Frans A., Green, Melissa J., Cairns, Murray J., Michie, Patricia T., Rasser, Paul E., Catts, Stanley, Tooney, Paul, Scott, Rodney J., Schall, Ulrich, Carr, Vaughan, Quidé, Yann, Krug, Axel, Stein, Frederike, Nenadić, Igor, Brosch, Katharina, Kircher, Tilo, Gur, Raquel, Gur, Ruben, Satterthwaite, Theodore D., Karuk, Andriana, Pomarol- Clotet, Edith, Radua, Joaquim, Fuentes-Claramonte, Paola, Salvador, Raymond, Spalletta, Gianfranco, Voineskos, Aristotle, Sim, Kang, Crespo-Facorro, Benedicto, Tordesillas Gutiérrez, Diana, Ehrlich, Stefan, Crossley, Nicolas, Grotegerd, Dominik, Repple, Jonathan, Lencer, Rebekka, Dannlowski, Udo, Calhoun, Vince, Rootes-Murdy, Kelly, Demro, Caroline, Ramsay, Ian S., Sponheim, Scott R., Schmidt, Andre, Borgwardt, Stefan, Tomyshev, Alexander, Lebedeva, Irina, Höschl, Cyril, Spaniel, Filip, Preda, Adrian, Nguyen, Dana, Uhlmann, Anne, Stein, Dan J., Howells, Fleur, Temmingh, Henk S., Diaz Zuluaga, Ana M., López Jaramillo, Carlos, Iasevoli, Felice, Ji, Ellen, Homan, Stephanie, Omlor, Wolfgang, Homan, Philipp, Kaiser, Stefan, Seifritz, Erich, Misic, Bratislav, Valk, Sofie L., Thompson, Paul, van Erp, Theo G. M., Turner, Jessica A., Bernhardt, Boris, and Kirschner, Matthias
Published: 2024
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5. White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group

Author: Kim, Bo-Gyeom, Kim, Gakyung, Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie, Anticevic, Alan, Arnold, Paul D., Balachander, Srinivas, Banaj, Nerisa, Bargalló, Nuria, Batistuzzo, Marcelo C., Benedetti, Francesco, Bertolín, Sara, Beucke, Jan Carl, Bollettini, Irene, Brem, Silvia, Brennan, Brian P., Buitelaar, Jan K., Calvo, Rosa, Castelo-Branco, Miguel, Cheng, Yuqi, Chhatkuli, Ritu Bhusal, Ciullo, Valentina, Coelho, Ana, Couto, Beatriz, Dallaspezia, Sara, Ely, Benjamin A., Ferreira, Sónia, Fontaine, Martine, Fouche, Jean-Paul, Grazioplene, Rachael, Gruner, Patricia, Hagen, Kristen, Hansen, Bjarne, Hanna, Gregory L., Hirano, Yoshiyuki, Höxter, Marcelo Q., Hough, Morgan, Hu, Hao, Huyser, Chaim, Ikuta, Toshikazu, Jahanshad, Neda, James, Anthony, Jaspers-Fayer, Fern, Kasprzak, Selina, Kathmann, Norbert, Kaufmann, Christian, Kim, Minah, Koch, Kathrin, Kvale, Gerd, Kwon, Jun Soo, Lazaro, Luisa, Lee, Junhee, Lochner, Christine, Lu, Jin, Manrique, Daniela Rodriguez, Martínez-Zalacaín, Ignacio, Masuda, Yoshitada, Matsumoto, Koji, Maziero, Maria Paula, Menchón, Jose M., Minuzzi, Luciano, Moreira, Pedro Silva, Morgado, Pedro, Narayanaswamy, Janardhanan C., Narumoto, Jin, Ortiz, Ana E., Ota, Junko, Pariente, Jose C., Perriello, Chris, Picó-Pérez, Maria, Pittenger, Christopher, Poletti, Sara, Real, Eva, Reddy, Y. C. Janardhan, van Rooij, Daan, Sakai, Yuki, Sato, João Ricardo, Segalas, Cinto, Shavitt, Roseli G., Shen, Zonglin, Shimizu, Eiji, Shivakumar, Venkataram, Soreni, Noam, Soriano-Mas, Carles, Sousa, Nuno, Sousa, Mafalda Machado, Spalletta, Gianfranco, Stern, Emily R., Stewart, S. Evelyn, Szeszko, Philip R., Thomas, Rajat, Thomopoulos, Sophia I., Vecchio, Daniela, Venkatasubramanian, Ganesan, Vriend, Chris, Walitza, Susanne, Wang, Zhen, Watanabe, Anri, Wolters, Lidewij, Xu, Jian, Yamada, Kei, Yun, Je-Yeon, Zarei, Mojtaba, Zhao, Qing, Zhu, Xi, Thompson, Paul M., Bruin, Willem B., van Wingen, Guido A., Piras, Federica, Piras, Fabrizio, Stein, Dan J., van den Heuvel, Odile A., Simpson, Helen Blair, Marsh, Rachel, and Cha, Jiook
Published: 2024
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6. Cortical morphology in patients with the deficit and non-deficit syndrome of schizophrenia: a worldwide meta- and mega-analyses.

Author: Banaj, Nerisa, Vecchio, Daniela, Piras, Fabrizio, De Rossi, Pietro, Bustillo, Juan, Ciufolini, Simone, Dazzan, Paola, Di Forti, Marta, Dickie, Erin, Ford, Judith, Fuentes-Claramonte, Paola, Gruber, Oliver, Guerrero-Pedraza, Amalia, Hamilton, Holly, Howells, Fleur, Kraemer, Bernd, Lawrie, Stephen, Radua, Joaquim, Richter, Anja, Salvador, Raymond, Sawa, Akira, Scheffler, Freda, Sim, Kang, Spaniel, Filip, Stein, Dan, Temmingh, Henk, Thomopoulos, Sophia, Tomecek, David, Uhlmann, Anne, Voineskos, Aristotle, Yang, Kun, Jahanshad, Neda, Thompson, Paul, Murray, Robin, Pomarol-Clotet, Edith, Turner, Jessica, Spalletta, Gianfranco, Piras, Federica, Mathalon, Daniel, Potkin, Steven, Van Erp, Theodorus, and Preda, Adrian
Subjects: Humans, Schizophrenia, Magnetic Resonance Imaging, Neuroimaging, Parietal Lobe, Syndrome, Cerebral Cortex
Abstract: Converging evidence suggests that schizophrenia (SZ) with primary, enduring negative symptoms (i.e., Deficit SZ (DSZ)) represents a distinct entity within the SZ spectrum while the neurobiological underpinnings remain undetermined. In the largest dataset of DSZ and Non-Deficit (NDSZ), we conducted a meta-analysis of data from 1560 individuals (168 DSZ, 373 NDSZ, 1019 Healthy Controls (HC)) and a mega-analysis of a subsampled data from 944 individuals (115 DSZ, 254 NDSZ, 575 HC) collected across 9 worldwide research centers of the ENIGMA SZ Working Group (8 in the mega-analysis), to clarify whether they differ in terms of cortical morphology. In the meta-analysis, sites computed effect sizes for differences in cortical thickness and surface area between SZ and control groups using a harmonized pipeline. In the mega-analysis, cortical values of individuals with schizophrenia and control participants were analyzed across sites using mixed-model ANCOVAs. The meta-analysis of cortical thickness showed a converging pattern of widespread thinner cortex in fronto-parietal regions of the left hemisphere in both DSZ and NDSZ, when compared to HC. However, DSZ have more pronounced thickness abnormalities than NDSZ, mostly involving the right fronto-parietal cortices. As for surface area, NDSZ showed differences in fronto-parietal-temporo-occipital cortices as compared to HC, and in temporo-occipital cortices as compared to DSZ. Although DSZ and NDSZ show widespread overlapping regions of thinner cortex as compared to HC, cortical thinning seems to better typify DSZ, being more extensive and bilateral, while surface area alterations are more evident in NDSZ. Our findings demonstrate for the first time that DSZ and NDSZ are characterized by different neuroimaging phenotypes, supporting a nosological distinction between DSZ and NDSZ and point toward the separate disease hypothesis.
Published: 2023

7. The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium.

Author: Bruin, Willem, Abe, Yoshinari, Alonso, Pino, Anticevic, Alan, Backhausen, Lea, Balachander, Srinivas, Bargallo, Nuria, Batistuzzo, Marcelo, Benedetti, Francesco, Bertolin Triquell, Sara, Brem, Silvia, Calesella, Federico, Couto, Beatriz, Denys, Damiaan, Echevarria, Marco, Eng, Goi, Ferreira, Sónia, Feusner, Jamie, Grazioplene, Rachael, Gruner, Patricia, Guo, Joyce, Hagen, Kristen, Hansen, Bjarne, Hirano, Yoshiyuki, Hoexter, Marcelo, Jahanshad, Neda, Jaspers-Fayer, Fern, Kasprzak, Selina, Kim, Minah, Koch, Kathrin, Bin Kwak, Yoo, Kwon, Jun, Lazaro, Luisa, Li, Chiang-Shan, Lochner, Christine, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Menchon, Jose, Moreira, Pedro, Morgado, Pedro, Nakagawa, Akiko, Nakao, Tomohiro, Narayanaswamy, Janardhanan, Nurmi, Erika, Zorrilla, Jose, Picó-Pérez, Maria, Piras, Fabrizio, Piras, Federica, Pittenger, Christopher, Reddy, Janardhan, Rodriguez-Manrique, Daniela, Sakai, Yuki, Shimizu, Eiji, Shivakumar, Venkataram, Simpson, Blair, Soriano-Mas, Carles, Sousa, Nuno, Spalletta, Gianfranco, Stern, Emily, Evelyn Stewart, S, Szeszko, Philip, Tang, Jinsong, Thomopoulos, Sophia, Thorsen, Anders, Yoshida, Tokiko, Tomiyama, Hirofumi, Vai, Benedetta, Veer, Ilya, Venkatasubramanian, Ganesan, Vetter, Nora, Vriend, Chris, Walitza, Susanne, Waller, Lea, Wang, Zhen, Watanabe, Anri, Wolff, Nicole, Yun, Je-Yeon, Zhao, Qing, van Leeuwen, Wieke, van Marle, Hein, van de Mortel, Laurens, van der Straten, Anouk, van der Werf, Ysbrand, Thompson, Paul, Stein, Dan, van den Heuvel, Odile, van Wingen, Guido, and Piacentini, John
Subjects: Humans, Connectome, Brain Mapping, Magnetic Resonance Imaging, Brain, Obsessive-Compulsive Disorder, Biomarkers, Neural Pathways
Abstract: Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohens d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohens d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.
Published: 2023

8. A worldwide study of white matter microstructural alterations in people living with Parkinson’s disease

Author: Owens-Walton, Conor, Nir, Talia M., Al-Bachari, Sarah, Ambrogi, Sonia, Anderson, Tim J., Aventurato, Ítalo Karmann, Cendes, Fernando, Chen, Yao-Liang, Ciullo, Valentina, Cook, Phil, Dalrymple-Alford, John C., Dirkx, Michiel F., Druzgal, Jason, Emsley, Hedley C. A., Guimarães, Rachel, Haroon, Hamied A., Helmich, Rick C., Hu, Michele T., Johansson, Martin E., Kim, Ho Bin, Klein, Johannes C., Laansma, Max, Lawrence, Katherine E., Lochner, Christine, Mackay, Clare, McMillan, Corey T., Melzer, Tracy R., Nabulsi, Leila, Newman, Ben, Opriessnig, Peter, Parkes, Laura M., Pellicano, Clelia, Piras, Fabrizio, Piras, Federica, Pirpamer, Lukas, Pitcher, Toni L., Poston, Kathleen L., Roos, Annerine, Silva, Lucas Scárdua, Schmidt, Reinhold, Schwingenschuh, Petra, Shahid-Besanti, Marian, Spalletta, Gianfranco, Stein, Dan J., Thomopoulos, Sophia I., Tosun, Duygu, Tsai, Chih-Chien, van den Heuvel, Odile A., van Heese, Eva, Vecchio, Daniela, Villalón-Reina, Julio E., Vriend, Chris, Wang, Jiun-Jie, Wu, Yih-Ru, Yasuda, Clarissa Lin, Thompson, Paul M., Jahanshad, Neda, and van der Werf, Ysbrand
Published: 2024
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9. Mapping gray and white matter volume abnormalities in early-onset psychosis: an ENIGMA multicenter voxel-based morphometry study

Author: Si, Shuqing, Bi, Anbreen, Yu, Zhaoying, See, Cheryl, Kelly, Sinead, Ambrogi, Sonia, Arango, Celso, Baeza, Inmaculada, Banaj, Nerisa, Berk, Michael, Castro-Fornieles, Josefina, Crespo-Facorro, Benedicto, Crouse, Jacob J., Díaz-Caneja, Covadonga M., Fett, Anne-Kathrin, Fortea, Adriana, Frangou, Sophia, Goldstein, Benjamin I., Hickie, Ian B., Janssen, Joost, Kennedy, Kody G., Krabbendam, Lydia, Kyriakopoulos, Marinos, MacIntosh, Bradley J., Morgado, Pedro, Nerland, Stener, Pascual-Diaz, Saül, Picó-Pérez, Maria, Piras, Fabrizio, Rund, Bjørn Rishovd, de la Serna, Elena, Spalletta, Gianfranco, Sugranyes, Gisela, Suo, Chao, Tordesillas-Gutiérrez, Diana, Vecchio, Daniela, Radua, Joaquim, McGuire, Philip, Thomopoulos, Sophia I., Jahanshad, Neda, Thompson, Paul M., Barth, Claudia, Agartz, Ingrid, James, Anthony, and Kempton, Matthew J.
Published: 2024
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10. Large-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium.

Author: Schijven, Dick, Postema, Merel C, Fukunaga, Masaki, Matsumoto, Junya, Miura, Kenichiro, de Zwarte, Sonja MC, van Haren, Neeltje EM, Cahn, Wiepke, Hulshoff Pol, Hilleke E, Kahn, René S, Ayesa-Arriola, Rosa, Ortiz-García de la Foz, Víctor, Tordesillas-Gutierrez, Diana, Vázquez-Bourgon, Javier, Crespo-Facorro, Benedicto, Alnæs, Dag, Dahl, Andreas, Westlye, Lars T, Agartz, Ingrid, Andreassen, Ole A, Jönsson, Erik G, Kochunov, Peter, Bruggemann, Jason M, Catts, Stanley V, Michie, Patricia T, Mowry, Bryan J, Quidé, Yann, Rasser, Paul E, Schall, Ulrich, Scott, Rodney J, Carr, Vaughan J, Green, Melissa J, Henskens, Frans A, Loughland, Carmel M, Pantelis, Christos, Weickert, Cynthia Shannon, Weickert, Thomas W, de Haan, Lieuwe, Brosch, Katharina, Pfarr, Julia-Katharina, Ringwald, Kai G, Stein, Frederike, Jansen, Andreas, Kircher, Tilo TJ, Nenadić, Igor, Krämer, Bernd, Gruber, Oliver, Satterthwaite, Theodore D, Bustillo, Juan, Mathalon, Daniel H, Preda, Adrian, Calhoun, Vince D, Ford, Judith M, Potkin, Steven G, Chen, Jingxu, Tan, Yunlong, Wang, Zhiren, Xiang, Hong, Fan, Fengmei, Bernardoni, Fabio, Ehrlich, Stefan, Fuentes-Claramonte, Paola, Garcia-Leon, Maria Angeles, Guerrero-Pedraza, Amalia, Salvador, Raymond, Sarró, Salvador, Pomarol-Clotet, Edith, Ciullo, Valentina, Piras, Fabrizio, Vecchio, Daniela, Banaj, Nerisa, Spalletta, Gianfranco, Michielse, Stijn, van Amelsvoort, Therese, Dickie, Erin W, Voineskos, Aristotle N, Sim, Kang, Ciufolini, Simone, Dazzan, Paola, Murray, Robin M, Kim, Woo-Sung, Chung, Young-Chul, Andreou, Christina, Schmidt, André, Borgwardt, Stefan, McIntosh, Andrew M, Whalley, Heather C, Lawrie, Stephen M, du Plessis, Stefan, Luckhoff, Hilmar K, Scheffler, Freda, Emsley, Robin, Grotegerd, Dominik, Lencer, Rebekka, Dannlowski, Udo, Edmond, Jesse T, Rootes-Murdy, Kelly, Stephen, Julia M, Mayer, Andrew R, and Antonucci, Linda A
Subjects: Brain, Cerebral Cortex, Humans, Magnetic Resonance Imaging, Case-Control Studies, Schizophrenia, Female, Male, Functional Laterality, asymmetry, brain imaging, cortical, subcortical, Clinical Research, Mental Health, Neurosciences, Brain Disorders, Mental health
Abstract: Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.
Published: 2023

11. In vivo white matter microstructure in adolescents with early-onset psychosis: a multi-site mega-analysis

Author: Barth, Claudia, Kelly, Sinead, Nerland, Stener, Jahanshad, Neda, Alloza, Clara, Ambrogi, Sonia, Andreassen, Ole A, Andreou, Dimitrios, Arango, Celso, Baeza, Inmaculada, Banaj, Nerisa, Bearden, Carrie E, Berk, Michael, Bohman, Hannes, Castro-Fornieles, Josefina, Chye, Yann, Crespo-Facorro, Benedicto, de la Serna, Elena, Díaz-Caneja, Covadonga M, Gurholt, Tiril P, Hegarty, Catherine E, James, Anthony, Janssen, Joost, Johannessen, Cecilie, Jönsson, Erik G, Karlsgodt, Katherine H, Kochunov, Peter, Lois, Noemi G, Lundberg, Mathias, Myhre, Anne M, Pascual-Diaz, Saül, Piras, Fabrizio, Smelror, Runar E, Spalletta, Gianfranco, Stokkan, Therese S, Sugranyes, Gisela, Suo, Chao, Thomopoulos, Sophia I, Tordesillas-Gutiérrez, Diana, Vecchio, Daniela, Wedervang-Resell, Kirsten, Wortinger, Laura A, Thompson, Paul M, and Agartz, Ingrid
Subjects: Brain Disorders, Serious Mental Illness, Pediatric, Biomedical Imaging, Clinical Research, Neurosciences, Mental Health, Schizophrenia, Mental health, Good Health and Well Being, Female, Humans, Male, Adolescent, White Matter, Diffusion Tensor Imaging, Brain, Psychotic Disorders, Anisotropy, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract: Emerging evidence suggests brain white matter alterations in adolescents with early-onset psychosis (EOP; age of onset
Published: 2023

12. Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium.

Author: Constantinides, Constantinos, Han, Laura KM, Alloza, Clara, Antonucci, Linda Antonella, Arango, Celso, Ayesa-Arriola, Rosa, Banaj, Nerisa, Bertolino, Alessandro, Borgwardt, Stefan, Bruggemann, Jason, Bustillo, Juan, Bykhovski, Oleg, Calhoun, Vince, Carr, Vaughan, Catts, Stanley, Chung, Young-Chul, Crespo-Facorro, Benedicto, Díaz-Caneja, Covadonga M, Donohoe, Gary, Plessis, Stefan Du, Edmond, Jesse, Ehrlich, Stefan, Emsley, Robin, Eyler, Lisa T, Fuentes-Claramonte, Paola, Georgiadis, Foivos, Green, Melissa, Guerrero-Pedraza, Amalia, Ha, Minji, Hahn, Tim, Henskens, Frans A, Holleran, Laurena, Homan, Stephanie, Homan, Philipp, Jahanshad, Neda, Janssen, Joost, Ji, Ellen, Kaiser, Stefan, Kaleda, Vasily, Kim, Minah, Kim, Woo-Sung, Kirschner, Matthias, Kochunov, Peter, Kwak, Yoo Bin, Kwon, Jun Soo, Lebedeva, Irina, Liu, Jingyu, Mitchie, Patricia, Michielse, Stijn, Mothersill, David, Mowry, Bryan, de la Foz, Víctor Ortiz-García, Pantelis, Christos, Pergola, Giulio, Piras, Fabrizio, Pomarol-Clotet, Edith, Preda, Adrian, Quidé, Yann, Rasser, Paul E, Rootes-Murdy, Kelly, Salvador, Raymond, Sangiuliano, Marina, Sarró, Salvador, Schall, Ulrich, Schmidt, André, Scott, Rodney J, Selvaggi, Pierluigi, Sim, Kang, Skoch, Antonin, Spalletta, Gianfranco, Spaniel, Filip, Thomopoulos, Sophia I, Tomecek, David, Tomyshev, Alexander S, Tordesillas-Gutiérrez, Diana, van Amelsvoort, Therese, Vázquez-Bourgon, Javier, Vecchio, Daniela, Voineskos, Aristotle, Weickert, Cynthia S, Weickert, Thomas, Thompson, Paul M, Schmaal, Lianne, van Erp, Theo GM, Turner, Jessica, Cole, James H, ENIGMA Schizophrenia Consortium, Dima, Danai, and Walton, Esther
Subjects: ENIGMA Schizophrenia Consortium, Brain, Humans, Magnetic Resonance Imaging, Prospective Studies, Schizophrenia, Aging, Adolescent, Adult, Aged, Middle Aged, Female, Male, Young Adult, Mental Health, Serious Mental Illness, Brain Disorders, Neurosciences, Biomedical Imaging, Clinical Research, Behavioral and Social Science, Neurological, Mental health, Good Health and Well Being, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract: Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18-72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18-73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.55 years (95% CI: 2.91, 4.19; I2 = 57.53%) compared to controls, after adjusting for age, sex and site (Cohen's d = 0.48). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions.
Published: 2023

13. Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer’s disease

Author: Holstege, Henne, Hulsman, Marc, Charbonnier, Camille, Grenier-Boley, Benjamin, Quenez, Olivier, Grozeva, Detelina, van Rooij, Jeroen GJ, Sims, Rebecca, Ahmad, Shahzad, Amin, Najaf, Norsworthy, Penny J, Dols-Icardo, Oriol, Hummerich, Holger, Kawalia, Amit, Amouyel, Philippe, Beecham, Gary W, Berr, Claudine, Bis, Joshua C, Boland, Anne, Bossù, Paola, Bouwman, Femke, Bras, Jose, Campion, Dominique, Cochran, J Nicholas, Daniele, Antonio, Dartigues, Jean-François, Debette, Stéphanie, Deleuze, Jean-François, Denning, Nicola, DeStefano, Anita L, Farrer, Lindsay A, Fernández, Maria Victoria, Fox, Nick C, Galimberti, Daniela, Genin, Emmanuelle, Gille, Johan JP, Le Guen, Yann, Guerreiro, Rita, Haines, Jonathan L, Holmes, Clive, Ikram, M Arfan, Ikram, M Kamran, Jansen, Iris E, Kraaij, Robert, Lathrop, Marc, Lemstra, Afina W, Lleó, Alberto, Luckcuck, Lauren, Mannens, Marcel MAM, Marshall, Rachel, Martin, Eden R, Masullo, Carlo, Mayeux, Richard, Mecocci, Patrizia, Meggy, Alun, Mol, Merel O, Morgan, Kevin, Myers, Richard M, Nacmias, Benedetta, Naj, Adam C, Napolioni, Valerio, Pasquier, Florence, Pastor, Pau, Pericak-Vance, Margaret A, Raybould, Rachel, Redon, Richard, Reinders, Marcel JT, Richard, Anne-Claire, Riedel-Heller, Steffi G, Rivadeneira, Fernando, Rousseau, Stéphane, Ryan, Natalie S, Saad, Salha, Sanchez-Juan, Pascual, Schellenberg, Gerard D, Scheltens, Philip, Schott, Jonathan M, Seripa, Davide, Seshadri, Sudha, Sie, Daoud, Sistermans, Erik A, Sorbi, Sandro, van Spaendonk, Resie, Spalletta, Gianfranco, Tesi, Niccolo’, Tijms, Betty, Uitterlinden, André G, van der Lee, Sven J, Visser, Pieter Jelle, Wagner, Michael, Wallon, David, Wang, Li-San, Zarea, Aline, Clarimon, Jordi, van Swieten, John C, Greicius, Michael D, Yokoyama, Jennifer S, Cruchaga, Carlos, Hardy, John, and Ramirez, Alfredo
Subjects: Biological Sciences, Genetics, Dementia, Alzheimer's Disease, Neurosciences, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Human Genome, Prevention, Acquired Cognitive Impairment, Neurodegenerative, Aging, Clinical Research, Biotechnology, Brain Disorders, 2.1 Biological and endogenous factors, Aetiology, Neurological, Humans, Adenosine Triphosphatases, Alzheimer Disease, ATP Binding Cassette Transporter 1, Genome-Wide Association Study, Risk Factors, Exosomes, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract: Alzheimer's disease (AD), the leading cause of dementia, has an estimated heritability of approximately 70%1. The genetic component of AD has been mainly assessed using genome-wide association studies, which do not capture the risk contributed by rare variants2. Here, we compared the gene-based burden of rare damaging variants in exome sequencing data from 32,558 individuals-16,036 AD cases and 16,522 controls. Next to variants in TREM2, SORL1 and ABCA7, we observed a significant association of rare, predicted damaging variants in ATP8B4 and ABCA1 with AD risk, and a suggestive signal in ADAM10. Additionally, the rare-variant burden in RIN3, CLU, ZCWPW1 and ACE highlighted these genes as potential drivers of respective AD-genome-wide association study loci. Variants associated with the strongest effect on AD risk, in particular loss-of-function variants, are enriched in early-onset AD cases. Our results provide additional evidence for a major role for amyloid-β precursor protein processing, amyloid-β aggregation, lipid metabolism and microglial function in AD.
Published: 2022

14. Correction: White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group

Author: Kim, Bo-Gyeom, Kim, Gakyung, Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie, Anticevic, Alan, Arnold, Paul D., Balachander, Srinivas, Banaj, Nerisa, Bargalló, Nuria, Batistuzzo, Marcelo C., Benedetti, Francesco, Bertolín, Sara, Beucke, Jan Carl, Bollettini, Irene, Brem, Silvia, Brennan, Brian P., Buitelaar, Jan K., Calvo, Rosa, Castelo-Branco, Miguel, Cheng, Yuqi, Chhatkuli, Ritu Bhusal, Ciullo, Valentina, Coelho, Ana, Couto, Beatriz, Dallaspezia, Sara, Ely, Benjamin A., Ferreira, Sónia, Fontaine, Martine, Fouche, Jean-Paul, Grazioplene, Rachael, Gruner, Patricia, Hagen, Kristen, Hansen, Bjarne, Hanna, Gregory L., Hirano, Yoshiyuki, Höxter, Marcelo Q., Hough, Morgan, Hu, Hao, Huyser, Chaim, Ikuta, Toshikazu, Jahanshad, Neda, James, Anthony, Jaspers-Fayer, Fern, Kasprzak, Selina, Kathmann, Norbert, Kaufmann, Christian, Kim, Minah, Koch, Kathrin, Kvale, Gerd, Kwon, Jun Soo, Lazaro, Luisa, Lee, Junhee, Lochner, Christine, Lu, Jin, Manrique, Daniela Rodriguez, Martínez-Zalacaín, Ignacio, Masuda, Yoshitada, Matsumoto, Koji, Maziero, Maria Paula, Menchón, Jose M., Minuzzi, Luciano, Moreira, Pedro Silva, Morgado, Pedro, Narayanaswamy, Janardhanan C., Narumoto, Jin, Ortiz, Ana E., Ota, Junko, Pariente, Jose C., Perriello, Chris, Picó-Pérez, Maria, Pittenger, Christopher, Poletti, Sara, Real, Eva, Reddy, Y. C. Janardhan, van Rooij, Daan, Sakai, Yuki, Sato, João Ricardo, Segalas, Cinto, Shavitt, Roseli G., Shen, Zonglin, Shimizu, Eiji, Shivakumar, Venkataram, Soreni, Noam, Soriano-Mas, Carles, Sousa, Nuno, Sousa, Mafalda Machado, Spalletta, Gianfranco, Stern, Emily R., Stewart, S. Evelyn, Szeszko, Philip R., Thomas, Rajat, Thomopoulos, Sophia I., Vecchio, Daniela, Venkatasubramanian, Ganesan, Vriend, Chris, Walitza, Susanne, Wang, Zhen, Watanabe, Anri, Wolters, Lidewij, Xu, Jian, Yamada, Kei, Yun, Je-Yeon, Zarei, Mojtaba, Zhao, Qing, Zhu, Xi, Thompson, Paul M., Bruin, Willem B., van Wingen, Guido A., Piras, Federica, Piras, Fabrizio, Stein, Dan J., van den Heuvel, Odile A., Simpson, Helen Blair, Marsh, Rachel, and Cha, Jiook
Published: 2024
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15. Targeting NAAA counters dopamine neuron loss and symptom progression in mouse models of parkinsonism.

Author: Palese, Francesca, Pontis, Silvia, Realini, Natalia, Torrens, Alexa, Ahmed, Faizy, Assogna, Francesca, Pellicano, Clelia, Bossù, Paola, Spalletta, Gianfranco, Green, Kim, and Piomelli, Daniele
Subjects: Animals, Humans, Mice, Neuroblastoma, Parkinsonian Disorders, Disease Models, Animal, Nerve Degeneration, Dopamine, Oxidopamine, 1-Methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine, Amidohydrolases, Enzyme Inhibitors, Dopaminergic Neurons, N-Acylethanolamine Acid Amidase, Neurodegeneration, Neuroinflammation, Palmitoylethanolamide, Parkinson's disease, Brain Disorders, Biotechnology, Parkinson's Disease, Neurodegenerative, Neurosciences, Aetiology, 2.1 Biological and endogenous factors, Neurological, Parkinson?s disease, Pharmacology and Pharmaceutical Sciences, Pharmacology & Pharmacy
Abstract: The lysosomal cysteine hydrolase N-acylethanolamine acid amidase (NAAA) deactivates palmitoylethanolamide (PEA), a lipid-derived PPAR-α agonist that is critically involved in the control of pain and inflammation. In this study, we asked whether NAAA-regulated PEA signaling might contribute to dopamine neuron degeneration and parkinsonism induced by the mitochondrial neurotoxins, 6-hydroxydopamine (6-OHDA) and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). In vitro experiments showed that 6-OHDA and MPTP enhanced NAAA expression and lowered PEA content in human SH-SY5Y cells. A similar effect was observed in mouse midbrain dopamine neurons following intra-striatal 6-OHDA injection. Importantly, deletion of the Naaa gene or pharmacological inhibition of NAAA activity substantially attenuated both dopamine neuron death and parkinsonian symptoms in mice treated with 6-OHDA or MPTP. Moreover, NAAA expression was elevated in postmortem brain cortex and premortem blood-derived exosomes from persons with Parkinson's disease compared to age-matched controls. The results identify NAAA-regulated PEA signaling as a molecular control point for dopaminergic neuron survival and a potential target for neuroprotective intervention.
Published: 2022

16. Chronic Stroke Sensorimotor Impairment Is Related to Smaller Hippocampal Volumes: An ENIGMA Analysis

Author: Zavaliangos‐Petropulu, Artemis, Lo, Bethany, Donnelly, Miranda R, Schweighofer, Nicolas, Lohse, Keith, Jahanshad, Neda, Barisano, Giuseppe, Banaj, Nerisa, Borich, Michael R, Boyd, Lara A, Buetefisch, Cathrin M, Byblow, Winston D, Cassidy, Jessica M, Charalambous, Charalambos C, Conforto, Adriana B, DiCarlo, Julie A, Dula, Adrienne N, Egorova‐Brumley, Natalia, Etherton, Mark R, Feng, Wuwei, Fercho, Kelene A, Geranmayeh, Fatemeh, Hanlon, Colleen A, Hayward, Kathryn S, Hordacre, Brenton, Kautz, Steven A, Khlif, Mohamed Salah, Kim, Hosung, Kuceyeski, Amy, Lin, David J, Liu, Jingchun, Lotze, Martin, MacIntosh, Bradley J, Margetis, John L, Mohamed, Feroze B, Piras, Fabrizio, Ramos‐Murguialday, Ander, Revill, Kate P, Roberts, Pamela S, Robertson, Andrew D, Schambra, Heidi M, Seo, Na Jin, Shiroishi, Mark S, Stinear, Cathy M, Soekadar, Surjo R, Spalletta, Gianfranco, Taga, Myriam, Tang, Wai Kwong, Thielman, Gregory T, Vecchio, Daniela, Ward, Nick S, Westlye, Lars T, Werden, Emilio, Winstein, Carolee, Wittenberg, George F, Wolf, Steven L, Wong, Kristin A, Yu, Chunshui, Brodtmann, Amy, Cramer, Steven C, Thompson, Paul M, and Liew, Sook‐Lei
Subjects: Neurosciences, Stroke, Aging, Brain Disorders, Cross-Sectional Studies, Female, Hippocampus, Humans, Male, Quality of Life, Recovery of Function, Stroke Rehabilitation, Upper Extremity, hippocampus, MRI, sensorimotor impairment, stroke, Cardiorespiratory Medicine and Haematology
Abstract: Background Persistent sensorimotor impairments after stroke can negatively impact quality of life. The hippocampus is vulnerable to poststroke secondary degeneration and is involved in sensorimotor behavior but has not been widely studied within the context of poststroke upper-limb sensorimotor impairment. We investigated associations between non-lesioned hippocampal volume and upper limb sensorimotor impairment in people with chronic stroke, hypothesizing that smaller ipsilesional hippocampal volumes would be associated with greater sensorimotor impairment. Methods and Results Cross-sectional T1-weighted magnetic resonance images of the brain were pooled from 357 participants with chronic stroke from 18 research cohorts of the ENIGMA (Enhancing NeuoImaging Genetics through Meta-Analysis) Stroke Recovery Working Group. Sensorimotor impairment was estimated from the FMA-UE (Fugl-Meyer Assessment of Upper Extremity). Robust mixed-effects linear models were used to test associations between poststroke sensorimotor impairment and hippocampal volumes (ipsilesional and contralesional separately; Bonferroni-corrected, P
Published: 2022

17. Safety and efficacy of continuous subcutaneous levodopa–carbidopa infusion (ND0612) for Parkinson's disease with motor fluctuations (BouNDless): a phase 3, randomised, double-blind, double-dummy, multicentre trial

Author: Afshari, Mitra, Amelin, Alexander, Arkadir, David, Badarny, Samih, Balaguer Martinez, Ernest, Bogucki, Andrzej, Boyd, James, Buyan Dent, Laura, Carroll, Camille, Chaudhuri, Kallol Ray, Cooney, Jeffrey, Corbillé, Anne-Gaëlle, Danaila, Teodor, De Pandis, Maria Francesca, Dethy, Sophie, Dhall, Rohit, Djaldetti, Ruth, Durif, Franck, Flitman, Stephen, Freire Alvarez, Eric, Goudreau, John, Grandas Perez, Francisco, Gurevich, Tanya, Isa, Arnaldo, Juncos, Jorge L, Kanchana, Sulada, Klodowska-Duda, Gabriela, Koziorowski, Dariusz, Kulisevsky Bojarski, Jaime, Lopez Lozano, Juan, Luo, Lan, Lytvynenko, Nataliya, Marconi, Roberto, Marques, Ana-Raquel, Martinez Castrillo, Juan Carlos, Martinez Torres, Irene, Mentreddi, Aashoo, Mir Rivera, Pablo, Moskovko, Sergii, Neryanova, Yuliya, Onofrj, Marco, Ostrem, Jill, Pacchetti, Claudio, Pavese, Nicola, Pellicano, Clelia, Revuelta, Gonzalo, Rodrigues, Ana Margarida, Rodriguez, Ramon, Rudzinska, Monika, Sarwar, Nighat, Schwartzbard, Julie, Scorr, Laura, Slevin, John, Slobodin, Tatyana, Spalletta, Gianfranco, Tagliati, Michele, Tai, Yen, Tessitore, Alessandro, Valkovic, Peter, Verhagen, Leo, Vostrikova, Elena, Yahalom, Gilad, Zalyalova, Zuleykha, Zarubova, Katerina, Zhukova, Irina, Espay, Alberto J, Stocchi, Fabrizio, Pahwa, Rajesh, Albanese, Alberto, Ellenbogen, Aaron, Ferreira, Joaquim J, Giladi, Nir, Hassin-Baer, Sharon, Hernandez-Vara, Jorge, Isaacson, Stuart H, Kieburtz, Karl, LeWitt, Peter A, Lopez-Manzanares, Lydia, Olanow, C Warren, Poewe, Werner, Sarva, Harini, Yardeni, Tami, Adar, Liat, Salin, Laurence, Lopes, Nelson, Sasson, Nissim, Case, Ryan, and Rascol, Olivier
Published: 2024
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18. Author Correction: Common variants in Alzheimer’s disease and risk stratification by polygenic risk scores

Author: de Rojas, Itziar, Moreno-Grau, Sonia, Tesi, Niccolo, Grenier-Boley, Benjamin, Andrade, Victor, Jansen, Iris E., Pedersen, Nancy L., Stringa, Najada, Zettergren, Anna, Hernández, Isabel, Montrreal, Laura, Antúnez, Carmen, Antonell, Anna, Tankard, Rick M., Bis, Joshua C., Sims, Rebecca, Bellenguez, Céline, Quintela, Inés, González-Perez, Antonio, Calero, Miguel, Franco-Macías, Emilio, Macías, Juan, Blesa, Rafael, Cervera-Carles, Laura, Menéndez-González, Manuel, Frank-García, Ana, Royo, Jose Luís, Moreno, Fermin, Huerto Vilas, Raquel, Baquero, Miquel, Diez-Fairen, Mónica, Lage, Carmen, García-Madrona, Sebastián, García-González, Pablo, Alarcón-Martín, Emilio, Valero, Sergi, Sotolongo-Grau, Oscar, Ullgren, Abbe, Naj, Adam C., Lemstra, Afina W., Benaque, Alba, Pérez-Cordón, Alba, Benussi, Alberto, Rábano, Alberto, Padovani, Alessandro, Squassina, Alessio, de Mendonça, Alexandre, Arias Pastor, Alfonso, Kok, Almar A. L., Meggy, Alun, Pastor, Ana Belén, Espinosa, Ana, Corma-Gómez, Anaïs, Martín Montes, Angel, Sanabria, Ángela, DeStefano, Anita L., Schneider, Anja, Haapasalo, Annakaisa, Kinhult Ståhlbom, Anne, Tybjærg-Hansen, Anne, Hartmann, Annette M., Spottke, Annika, Corbatón-Anchuelo, Arturo, Rongve, Arvid, Borroni, Barbara, Arosio, Beatrice, Nacmias, Benedetta, Nordestgaard, Børge G., Kunkle, Brian W., Charbonnier, Camille, Abdelnour, Carla, Masullo, Carlo, Martínez Rodríguez, Carmen, Muñoz-Fernandez, Carmen, Dufouil, Carole, Graff, Caroline, Ferreira, Catarina B., Chillotti, Caterina, Reynolds, Chandra A., Fenoglio, Chiara, Van Broeckhoven, Christine, Clark, Christopher, Pisanu, Claudia, Satizabal, Claudia L., Holmes, Clive, Buiza-Rueda, Dolores, Aarsland, Dag, Rujescu, Dan, Alcolea, Daniel, Galimberti, Daniela, Wallon, David, Seripa, Davide, Grünblatt, Edna, Dardiotis, Efthimios, Düzel, Emrah, Scarpini, Elio, Conti, Elisa, Rubino, Elisa, Gelpi, Ellen, Rodriguez-Rodriguez, Eloy, Duron, Emmanuelle, Boerwinkle, Eric, Ferri, Evelyn, Tagliavini, Fabrizio, Küçükali, Fahri, Pasquier, Florence, Sanchez-Garcia, Florentino, Mangialasche, Francesca, Jessen, Frank, Nicolas, Gaël, Selbæk, Geir, Ortega, Gemma, Chêne, Geneviève, Hadjigeorgiou, Georgios, Rossi, Giacomina, Spalletta, Gianfranco, Giaccone, Giorgio, Grande, Giulia, Binetti, Giuliano, Papenberg, Goran, Hampel, Harald, Bailly, Henri, Zetterberg, Henrik, Soininen, Hilkka, Karlsson, Ida K., Alvarez, Ignacio, Appollonio, Ildebrando, Giegling, Ina, Skoog, Ingmar, Saltvedt, Ingvild, Rainero, Innocenzo, Rosas Allende, Irene, Hort, Jakub, Diehl-Schmid, Janine, Van Dongen, Jasper, Vidal, Jean-Sebastien, Lehtisalo, Jenni, Wiltfang, Jens, Thomassen, Jesper Qvist, Kornhuber, Johannes, Haines, Jonathan L., Vogelgsang, Jonathan, Pineda, Juan A., Fortea, Juan, Popp, Julius, Deckert, Jürgen, Buerger, Katharina, Morgan, Kevin, Fließbach, Klaus, Sleegers, Kristel, Molina-Porcel, Laura, Kilander, Lena, Weinhold, Leonie, Farrer, Lindsay A., Wang, Li-San, Kleineidam, Luca, Farotti, Lucia, Parnetti, Lucilla, Tremolizzo, Lucio, Hausner, Lucrezia, Benussi, Luisa, Froelich, Lutz, Ikram, M. Arfan, Deniz-Naranjo, M. Candida, Tsolaki, Magda, Rosende-Roca, Maitée, Löwenmark, Malin, Hulsman, Marc, Spallazzi, Marco, Pericak-Vance, Margaret A., Esiri, Margaret, Bernal Sánchez-Arjona, María, Dalmasso, Maria Carolina, Martínez-Larrad, María Teresa, Arcaro, Marina, Nöthen, Markus M., Fernández-Fuertes, Marta, Dichgans, Martin, Ingelsson, Martin, Herrmann, Martin J., Scherer, Martin, Vyhnalek, Martin, Kosmidis, Mary H., Yannakoulia, Mary, Schmid, Matthias, Ewers, Michael, Heneka, Michael T., Wagner, Michael, Scamosci, Michela, Kivipelto, Miia, Hiltunen, Mikko, Zulaica, Miren, Alegret, Montserrat, Fornage, Myriam, Roberto, Natalia, van Schoor, Natasja M., Seidu, Nazib M., Banaj, Nerisa, Armstrong, Nicola J., Scarmeas, Nikolaos, Scherbaum, Norbert, Goldhardt, Oliver, Hanon, Oliver, Peters, Oliver, Skrobot, Olivia Anna, Quenez, Olivier, Lerch, Ondrej, Bossù, Paola, Caffarra, Paolo, Dionigi Rossi, Paolo, Sakka, Paraskevi, Mecocci, Patrizia, Hoffmann, Per, Holmans, Peter A., Fischer, Peter, Riederer, Peter, Yang, Qiong, Marshall, Rachel, Kalaria, Rajesh N., Mayeux, Richard, Vandenberghe, Rik, Cecchetti, Roberta, Ghidoni, Roberta, Frikke-Schmidt, Ruth, Sorbi, Sandro, Hägg, Sara, Engelborghs, Sebastiaan, Helisalmi, Seppo, Botne Sando, Sigrid, Kern, Silke, Archetti, Silvana, Boschi, Silvia, Fostinelli, Silvia, Gil, Silvia, Mendoza, Silvia, Mead, Simon, Ciccone, Simona, Djurovic, Srdjan, Heilmann-Heimbach, Stefanie, Riedel-Heller, Steffi, Kuulasmaa, Teemu, del Ser, Teodoro, Lebouvier, Thibaud, Polak, Thomas, Ngandu, Tiia, Grimmer, Timo, Bessi, Valentina, Escott-Price, Valentina, Giedraitis, Vilmantas, Deramecourt, Vincent, Maier, Wolfgang, Jian, Xueqiu, Pijnenburg, Yolande A. L., Kehoe, Patrick Gavin, Garcia-Ribas, Guillermo, Sánchez-Juan, Pascual, Pastor, Pau, Pérez-Tur, Jordi, Piñol-Ripoll, Gerard, Lopez de Munain, Adolfo, García-Alberca, Jose María, Bullido, María J., Álvarez, Victoria, Lleó, Alberto, Real, Luis M., Mir, Pablo, Medina, Miguel, Scheltens, Philip, Holstege, Henne, Marquié, Marta, Sáez, María Eugenia, Carracedo, Ángel, Amouyel, Philippe, Schellenberg, Gerard D., Williams, Julie, Seshadri, Sudha, van Duijn, Cornelia M., Mather, Karen A., Sánchez-Valle, Raquel, Serrano-Ríos, Manuel, Orellana, Adelina, Tárraga, Lluís, Blennow, Kaj, Huisman, Martijn, Andreassen, Ole A., Posthuma, Danielle, Clarimón, Jordi, Boada, Mercè, van der Flier, Wiesje M., Ramirez, Alfredo, Lambert, Jean-Charles, van der Lee, Sven J., and Ruiz, Agustín
Published: 2023
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19. ENIGMA-DTI: Translating reproducible white matter deficits into personalized vulnerability metrics in cross-diagnostic psychiatric research.

Author: Kochunov, Peter, Hong, L, Dennis, Emily, Morey, Rajendra, Tate, David, Wilde, Elisabeth, Logue, Mark, Kelly, Sinead, Donohoe, Gary, Favre, Pauline, Houenou, Josselin, Ching, Christopher, Holleran, Laurena, Andreassen, Ole, van Velzen, Laura, Schmaal, Lianne, Villalón-Reina, Julio, Piras, Fabrizio, Spalletta, Gianfranco, van den Heuvel, Odile, Veltman, Dick, Stein, Dan, Ryan, Meghann, Tan, Yunlong, Turner, Jessica, Haddad, Liz, Nir, Talia, Glahn, David, Thompson, Paul, Jahanshad, Neda, Bearden, Carrie, and Van Erp, Theodorus
Subjects: DTI, ENIGMA, RVI, big data, cross-disorder, white matter deficit patterns, Biomedical Research, Diffusion Tensor Imaging, Humans, Mental Disorders, Multicenter Studies as Topic, Psychiatry, White Matter
Abstract: The ENIGMA-DTI (diffusion tensor imaging) workgroup supports analyses that examine the effects of psychiatric, neurological, and developmental disorders on the white matter pathways of the human brain, as well as the effects of normal variation and its genetic associations. The seven ENIGMA disorder-oriented working groups used the ENIGMA-DTI workflow to derive patterns of deficits using coherent and coordinated analyses that model the disease effects across cohorts worldwide. This yielded the largest studies detailing patterns of white matter deficits in schizophrenia spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), traumatic brain injury (TBI), and 22q11 deletion syndrome. These deficit patterns are informative of the underlying neurobiology and reproducible in independent cohorts. We reviewed these findings, demonstrated their reproducibility in independent cohorts, and compared the deficit patterns across illnesses. We discussed translating ENIGMA-defined deficit patterns on the level of individual subjects using a metric called the regional vulnerability index (RVI), a correlation of an individuals brain metrics with the expected pattern for a disorder. We discussed the similarity in white matter deficit patterns among SSD, BD, MDD, and OCD and provided a rationale for using this index in cross-diagnostic neuropsychiatric research. We also discussed the difference in deficit patterns between idiopathic schizophrenia and 22q11 deletion syndrome, which is used as a developmental and genetic model of schizophrenia. Together, these findings highlight the importance of collaborative large-scale research to provide robust and reproducible effects that offer insights into individual vulnerability and cross-diagnosis features.
Published: 2022

20. A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms

Author: Liew, Sook-Lei, Lo, Bethany P, Donnelly, Miranda R, Zavaliangos-Petropulu, Artemis, Jeong, Jessica N, Barisano, Giuseppe, Hutton, Alexandre, Simon, Julia P, Juliano, Julia M, Suri, Anisha, Wang, Zhizhuo, Abdullah, Aisha, Kim, Jun, Ard, Tyler, Banaj, Nerisa, Borich, Michael R, Boyd, Lara A, Brodtmann, Amy, Buetefisch, Cathrin M, Cao, Lei, Cassidy, Jessica M, Ciullo, Valentina, Conforto, Adriana B, Cramer, Steven C, Dacosta-Aguayo, Rosalia, de la Rosa, Ezequiel, Domin, Martin, Dula, Adrienne N, Feng, Wuwei, Franco, Alexandre R, Geranmayeh, Fatemeh, Gramfort, Alexandre, Gregory, Chris M, Hanlon, Colleen A, Hordacre, Brenton G, Kautz, Steven A, Khlif, Mohamed Salah, Kim, Hosung, Kirschke, Jan S, Liu, Jingchun, Lotze, Martin, MacIntosh, Bradley J, Mataró, Maria, Mohamed, Feroze B, Nordvik, Jan E, Park, Gilsoon, Pienta, Amy, Piras, Fabrizio, Redman, Shane M, Revill, Kate P, Reyes, Mauricio, Robertson, Andrew D, Seo, Na Jin, Soekadar, Surjo R, Spalletta, Gianfranco, Sweet, Alison, Telenczuk, Maria, Thielman, Gregory, Westlye, Lars T, Winstein, Carolee J, Wittenberg, George F, Wong, Kristin A, and Yu, Chunshui
Subjects: Biological Sciences, Bioinformatics and Computational Biology, Physical Sciences, Stroke, Neurosciences, Bioengineering, Networking and Information Technology R&D (NITRD), Algorithms, Brain, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Neuroimaging
Abstract: Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research.
Published: 2022

21. The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke

Author: Liew, Sook‐Lei, Zavaliangos‐Petropulu, Artemis, Jahanshad, Neda, Lang, Catherine E, Hayward, Kathryn S, Lohse, Keith R, Juliano, Julia M, Assogna, Francesca, Baugh, Lee A, Bhattacharya, Anup K, Bigjahan, Bavrina, Borich, Michael R, Boyd, Lara A, Brodtmann, Amy, Buetefisch, Cathrin M, Byblow, Winston D, Cassidy, Jessica M, Conforto, Adriana B, Craddock, R Cameron, Dimyan, Michael A, Dula, Adrienne N, Ermer, Elsa, Etherton, Mark R, Fercho, Kelene A, Gregory, Chris M, Hadidchi, Shahram, Holguin, Jess A, Hwang, Darryl H, Jung, Simon, Kautz, Steven A, Khlif, Mohamed Salah, Khoshab, Nima, Kim, Bokkyu, Kim, Hosung, Kuceyeski, Amy, Lotze, Martin, MacIntosh, Bradley J, Margetis, John L, Mohamed, Feroze B, Piras, Fabrizio, Ramos‐Murguialday, Ander, Richard, Geneviève, Roberts, Pamela, Robertson, Andrew D, Rondina, Jane M, Rost, Natalia S, Sanossian, Nerses, Schweighofer, Nicolas, Seo, Na Jin, Shiroishi, Mark S, Soekadar, Surjo R, Spalletta, Gianfranco, Stinear, Cathy M, Suri, Anisha, Tang, Wai Kwong W, Thielman, Gregory T, Vecchio, Daniela, Villringer, Arno, Ward, Nick S, Werden, Emilio, Westlye, Lars T, Winstein, Carolee, Wittenberg, George F, Wong, Kristin A, Yu, Chunshui, Cramer, Steven C, and Thompson, Paul M
Subjects: Stroke, Brain Disorders, Biomedical Imaging, Neurosciences, Behavioral and Social Science, Humans, Magnetic Resonance Imaging, Multicenter Studies as Topic, Neuroimaging, Stroke Rehabilitation, big data, lesions, MRI, neuroinformatics, stroke, Cognitive Sciences, Experimental Psychology
Abstract: The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.
Published: 2022

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

Author: van den Heuvel, Odile A, Boedhoe, Premika SW, Bertolin, Sara, Bruin, Willem B, Francks, Clyde, Ivanov, Iliyan, Jahanshad, Neda, Kong, Xiang‐Zhen, Kwon, Jun Soo, O'Neill, Joseph, Paus, Tomas, Patel, Yash, Piras, Fabrizio, Schmaal, Lianne, Soriano‐Mas, Carles, Spalletta, Gianfranco, van Wingen, Guido A, Yun, Je‐Yeon, Vriend, Chris, Simpson, H Blair, van Rooij, Daan, Hoexter, Marcelo Q, Hoogman, Martine, Buitelaar, Jan K, Arnold, Paul, Beucke, Jan C, Benedetti, Francesco, Bollettini, Irene, Bose, Anushree, Brennan, Brian P, De Nadai, Alessandro S, Fitzgerald, Kate, Gruner, Patricia, Grünblatt, Edna, Hirano, Yoshiyuki, Huyser, Chaim, James, Anthony, Koch, Kathrin, Kvale, Gerd, Lazaro, Luisa, Lochner, Christine, Marsh, Rachel, Mataix‐Cols, David, Morgado, Pedro, Nakamae, Takashi, Nakao, Tomohiro, Narayanaswamy, Janardhanan C, Nurmi, Erika, Pittenger, Christopher, Reddy, YC Janardhan, Sato, João R, Soreni, Noam, Stewart, S Evelyn, Taylor, Stephan F, Tolin, David, Thomopoulos, Sophia I, Veltman, Dick J, Venkatasubramanian, Ganesan, Walitza, Susanne, Wang, Zhen, Thompson, Paul M, Stein, Dan J, Abe, Yoshinari, Alonso, Pino, Assogna, Francesca, Banaj, Nerisa, Batistuzzo, Marcelo C, Brem, Silvia, Ciullo, Valentina, Feusner, Jamie, Martínez‐Zalacaín, Ignacio, Menchón, José M, Miguel, Euripedes C, Piacentini, John, Piras, Federica, Sakai, Yuki, Wolters, Lidewij, and Yamada, Kei
Subjects: Biological Psychology, Psychology, Brain Disorders, Clinical Research, Serious Mental Illness, Pediatric, Neurosciences, Mental Health, Mental health, Neurological, Cerebral Cortex, Humans, Machine Learning, Multicenter Studies as Topic, Neuroimaging, Obsessive-Compulsive Disorder, cortical thickness, ENIGMA, mega-analysis, meta-analysis, MRI, obsessive-compulsive disorder, surface area, volume, ENIGMA-OCD working group, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract: Neuroimaging has played an important part in advancing our understanding of the neurobiology of obsessive-compulsive disorder (OCD). At the same time, neuroimaging studies of OCD have had notable limitations, including reliance on relatively small samples. International collaborative efforts to increase statistical power by combining samples from across sites have been bolstered by the ENIGMA consortium; this provides specific technical expertise for conducting multi-site analyses, as well as access to a collaborative community of neuroimaging scientists. In this article, we outline the background to, development of, and initial findings from ENIGMA's OCD working group, which currently consists of 47 samples from 34 institutes in 15 countries on 5 continents, with a total sample of 2,323 OCD patients and 2,325 healthy controls. Initial work has focused on studies of cortical thickness and subcortical volumes, structural connectivity, and brain lateralization in children, adolescents and adults with OCD, also including the study on the commonalities and distinctions across different neurodevelopment disorders. Additional work is ongoing, employing machine learning techniques. Findings to date have contributed to the development of neurobiological models of OCD, have provided an important model of global scientific collaboration, and have had a number of clinical implications. Importantly, our work has shed new light on questions about whether structural and functional alterations found in OCD reflect neurodevelopmental changes, effects of the disease process, or medication impacts. We conclude with a summary of ongoing work by ENIGMA-OCD, and a consideration of future directions for neuroimaging research on OCD within and beyond ENIGMA.
Published: 2022

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

Author: Gutman, Boris A, Erp, Theo GM, Alpert, Kathryn, Ching, Christopher RK, Isaev, Dmitry, Ragothaman, Anjani, Jahanshad, Neda, Saremi, Arvin, Zavaliangos‐Petropulu, Artemis, Glahn, David C, Shen, Li, Cong, Shan, Alnæs, Dag, Andreassen, Ole Andreas, Doan, Nhat Trung, Westlye, Lars T, Kochunov, Peter, Satterthwaite, Theodore D, Wolf, Daniel H, Huang, Alexander J, Kessler, Charles, Weideman, Andrea, Nguyen, Dana, Mueller, Bryon A, Faziola, Lawrence, Potkin, Steven G, Preda, Adrian, Mathalon, Daniel H, Bustillo, Juan, Calhoun, Vince, Ford, Judith M, Walton, Esther, Ehrlich, Stefan, Ducci, Giuseppe, Banaj, Nerisa, Piras, Fabrizio, Piras, Federica, Spalletta, Gianfranco, Canales‐Rodríguez, Erick J, Fuentes‐Claramonte, Paola, Pomarol‐Clotet, Edith, Radua, Joaquim, Salvador, Raymond, Sarró, Salvador, Dickie, Erin W, Voineskos, Aristotle, Tordesillas‐Gutiérrez, Diana, Crespo‐Facorro, Benedicto, Setién‐Suero, Esther, Son, Jacqueline Mayoral, Borgwardt, Stefan, Schönborn‐Harrisberger, Fabienne, Morris, Derek, Donohoe, Gary, Holleran, Laurena, Cannon, Dara, McDonald, Colm, Corvin, Aiden, Gill, Michael, Filho, Geraldo Busatto, Rosa, Pedro GP, Serpa, Mauricio H, Zanetti, Marcus V, Lebedeva, Irina, Kaleda, Vasily, Tomyshev, Alexander, Crow, Tim, James, Anthony, Cervenka, Simon, Sellgren, Carl M, Fatouros‐Bergman, Helena, Agartz, Ingrid, Howells, Fleur, Stein, Dan J, Temmingh, Henk, Uhlmann, Anne, Zubicaray, Greig I, McMahon, Katie L, Wright, Margie, Cobia, Derin, Csernansky, John G, Thompson, Paul M, Turner, Jessica A, and Wang, Lei
Subjects: Brain Disorders, Mental Health, Neurosciences, Schizophrenia, Mental health, Good Health and Well Being, Amygdala, Corpus Striatum, Hippocampus, Humans, Multicenter Studies as Topic, Neuroimaging, Thalamus, schizophrenia, structure, subcortical shape, Cognitive Sciences, Experimental Psychology
Abstract: Schizophrenia is associated with widespread alterations in subcortical brain structure. While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta-analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between individuals with schizophrenia and healthy control participants. The study analyzed data from 2,833 individuals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippocampus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1-weighted structural MRI scans. Mass univariate meta-analyses revealed more-concave-than-convex shape differences in the hippocampus, amygdala, accumbens, and thalamus in individuals with schizophrenia compared with control participants, more-convex-than-concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippocampus, amygdala, and thalamus in individuals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta-analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia.
Published: 2022

24. Smaller spared subcortical nuclei are associated with worse post-stroke sensorimotor outcomes in 28 cohorts worldwide

Author: Liew, Sook-Lei, Zavaliangos-Petropulu, Artemis, Schweighofer, Nicolas, Jahanshad, Neda, Lang, Catherine E, Lohse, Keith R, Banaj, Nerisa, Barisano, Giuseppe, Baugh, Lee A, Bhattacharya, Anup K, Bigjahan, Bavrina, Borich, Michael R, Boyd, Lara A, Brodtmann, Amy, Buetefisch, Cathrin M, Byblow, Winston D, Cassidy, Jessica M, Charalambous, Charalambos C, Ciullo, Valentina, Conforto, Adriana B, Craddock, Richard C, Dula, Adrienne N, Egorova, Natalia, Feng, Wuwei, Fercho, Kelene A, Gregory, Chris M, Hanlon, Colleen A, Hayward, Kathryn S, Holguin, Jess A, Hordacre, Brenton, Hwang, Darryl H, Kautz, Steven A, Khlif, Mohamed Salah, Kim, Bokkyu, Kim, Hosung, Kuceyeski, Amy, Lo, Bethany, Liu, Jingchun, Lin, David, Lotze, Martin, MacIntosh, Bradley J, Margetis, John L, Mohamed, Feroze B, Nordvik, Jan Egil, Petoe, Matthew A, Piras, Fabrizio, Raju, Sharmila, Ramos-Murguialday, Ander, Revill, Kate P, Roberts, Pamela, Robertson, Andrew D, Schambra, Heidi M, Seo, Na Jin, Shiroishi, Mark S, Soekadar, Surjo R, Spalletta, Gianfranco, Stinear, Cathy M, Suri, Anisha, Tang, Wai Kwong, Thielman, Gregory T, Thijs, Vincent N, Vecchio, Daniela, Ward, Nick S, Westlye, Lars T, Winstein, Carolee J, Wittenberg, George F, Wong, Kristin A, Yu, Chunshui, Wolf, Steven L, Cramer, Steven C, Thompson, Paul M, Baugh, Lee, Gallaguet, Adrià Bermudo, Bhattacharya, Anup, Borich, Michael, Boyd, Lara, Brown, Truman, Buetefisch, Cathrin, Byblow, Winston, Cassidy, Jessica, Charalambous, Charalambos, Cloutier, Alison, Cole, James, Conforto, Adriana, Craddock, Richard, Cramer, Steven, Aguayo, Rosalia Dacosta, DiCarlo, Julie, Dimyan, Michael, Domin, Martin, Donnellly, Miranda, Dula, Adrienne, Edwardson, Matthew, and Ermer, Elsa
Subjects: Biological Psychology, Psychology, Rehabilitation, Stroke, Brain Disorders, Neurosciences, Aetiology, 2.1 Biological and endogenous factors, stroke, rehabilitation, sensorimotor behaviour, MRI, subcortical volumes, ENIGMA Stroke Recovery Working Group, Clinical sciences, Biological psychology
Abstract: Up to two-thirds of stroke survivors experience persistent sensorimotor impairments. Recovery relies on the integrity of spared brain areas to compensate for damaged tissue. Deep grey matter structures play a critical role in the control and regulation of sensorimotor circuits. The goal of this work is to identify associations between volumes of spared subcortical nuclei and sensorimotor behaviour at different timepoints after stroke. We pooled high-resolution T1-weighted MRI brain scans and behavioural data in 828 individuals with unilateral stroke from 28 cohorts worldwide. Cross-sectional analyses using linear mixed-effects models related post-stroke sensorimotor behaviour to non-lesioned subcortical volumes (Bonferroni-corrected, P
Published: 2021

25. Narrative discourse production in Parkinson’s disease: Decoupling the role of cognitive-linguistic and motor speech changes

Author: D’Ascanio, Sara, Piras, Fabrizio, Banaj, Nerisa, Assogna, Francesca, Pellicano, Clelia, Bassi, Andrea, Spalletta, Gianfranco, and Piras, Federica
Published: 2023
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26. Correction: The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium

Author: Bruin, Willem B., Abe, Yoshinari, Alonso, Pino, Anticevic, Alan, Backhausen, Lea L., Balachander, Srinivas, Bargallo, Nuria, Batistuzzo, Marcelo C., Benedetti, Francesco, Bertolin Triquell, Sara, Brem, Silvia, Calesella, Federico, Couto, Beatriz, Denys, Damiaan A. J. P., Echevarria, Marco A. N., Eng, Goi Khia, Ferreira, Sónia, Feusner, Jamie D., Grazioplene, Rachael G., Gruner, Patricia, Guo, Joyce Y., Hagen, Kristen, Hansen, Bjarne, Hirano, Yoshiyuki, Hoexter, Marcelo Q., Jahanshad, Neda, Jaspers-Fayer, Fern, Kasprzak, Selina, Kim, Minah, Koch, Kathrin, Bin Kwak, Yoo, Kwon, Jun Soo, Lazaro, Luisa, Li, Chiang-Shan R., Lochner, Christine, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Menchon, Jose M., Moreira, Pedro S., Morgado, Pedro, Nakagawa, Akiko, Nakao, Tomohiro, Narayanaswamy, Janardhanan C., Nurmi, Erika L., Zorrilla, Jose C. Pariente, Piacentini, John, Picó-Pérez, Maria, Piras, Fabrizio, Piras, Federica, Pittenger, Christopher, Reddy, Janardhan Y. C., Rodriguez-Manrique, Daniela, Sakai, Yuki, Shimizu, Eiji, Shivakumar, Venkataram, Simpson, Blair H., Soriano-Mas, Carles, Sousa, Nuno, Spalletta, Gianfranco, Stern, Emily R., Evelyn Stewart, S., Szeszko, Philip R., Tang, Jinsong, Thomopoulos, Sophia I., Thorsen, Anders L., Yoshida, Tokiko, Tomiyama, Hirofumi, Vai, Benedetta, Veer, Ilya M., Venkatasubramanian, Ganesan, Vetter, Nora C., Vriend, Chris, Walitza, Susanne, Waller, Lea, Wang, Zhen, Watanabe, Anri, Wolff, Nicole, Yun, Je-Yeon, Zhao, Qing, van Leeuwen, Wieke A., van Marle, Hein J. F., van de Mortel, Laurens A., van der Straten, Anouk, van der Werf, Ysbrand D., Thompson, Paul M., Stein, Dan J., van den Heuvel, Odile A., and van Wingen, Guido A.
Published: 2023
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27. Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters.

Author: Bruin, Willem B, Taylor, Luke, Thomas, Rajat M, Shock, Jonathan P, Zhutovsky, Paul, Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie H, Anticevic, Alan, Arnold, Paul D, Assogna, Francesca, Benedetti, Francesco, Beucke, Jan C, Boedhoe, Premika SW, Bollettini, Irene, Bose, Anushree, Brem, Silvia, Brennan, Brian P, Buitelaar, Jan K, Calvo, Rosa, Cheng, Yuqi, Cho, Kang Ik K, Dallaspezia, Sara, Denys, Damiaan, Ely, Benjamin A, Feusner, Jamie D, Fitzgerald, Kate D, Fouche, Jean-Paul, Fridgeirsson, Egill A, Gruner, Patricia, Gürsel, Deniz A, Hauser, Tobias U, Hirano, Yoshiyuki, Hoexter, Marcelo Q, Hu, Hao, Huyser, Chaim, Ivanov, Iliyan, James, Anthony, Jaspers-Fayer, Fern, Kathmann, Norbert, Kaufmann, Christian, Koch, Kathrin, Kuno, Masaru, Kvale, Gerd, Kwon, Jun Soo, Liu, Yanni, Lochner, Christine, Lázaro, Luisa, Marques, Paulo, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Mataix-Cols, David, Menchón, José M, Minuzzi, Luciano, Moreira, Pedro S, Morer, Astrid, Morgado, Pedro, Nakagawa, Akiko, Nakamae, Takashi, Nakao, Tomohiro, Narayanaswamy, Janardhanan C, Nurmi, Erika L, O'Neill, Joseph, Pariente, Jose C, Perriello, Chris, Piacentini, John, Piras, Fabrizio, Piras, Federica, Reddy, YC Janardhan, Rus-Oswald, Oana G, Sakai, Yuki, Sato, João R, Schmaal, Lianne, Shimizu, Eiji, Simpson, H Blair, Soreni, Noam, Soriano-Mas, Carles, Spalletta, Gianfranco, Stern, Emily R, Stevens, Michael C, Stewart, S Evelyn, Szeszko, Philip R, Tolin, David F, Venkatasubramanian, Ganesan, Wang, Zhen, Yun, Je-Yeon, van Rooij, Daan, ENIGMA-OCD Working Group, Thompson, Paul M, van den Heuvel, Odile A, Stein, Dan J, and van Wingen, Guido A
Subjects: ENIGMA-OCD Working Group, Clinical Sciences, Public Health and Health Services, Psychology
Abstract: No diagnostic biomarkers are available for obsessive-compulsive disorder (OCD). Here, we aimed to identify magnetic resonance imaging (MRI) biomarkers for OCD, using 46 data sets with 2304 OCD patients and 2068 healthy controls from the ENIGMA consortium. We performed machine learning analysis of regional measures of cortical thickness, surface area and subcortical volume and tested classification performance using cross-validation. Classification performance for OCD vs. controls using the complete sample with different classifiers and cross-validation strategies was poor. When models were validated on data from other sites, model performance did not exceed chance-level. In contrast, fair classification performance was achieved when patients were grouped according to their medication status. These results indicate that medication use is associated with substantial differences in brain anatomy that are widely distributed, and indicate that clinical heterogeneity contributes to the poor performance of structural MRI as a disease marker.
Published: 2020

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

Author: van Velzen, Laura S., Dauvermann, Maria R., Colic, Lejla, Villa, Luca M., Savage, Hannah S., Toenders, Yara J., Zhu, Alyssa H., Bright, Joanna K., Campos, Adrián I., Salminen, Lauren E., Ambrogi, Sonia, Ayesa-Arriola, Rosa, Banaj, Nerisa, Başgöze, Zeynep, Bauer, Jochen, Blair, Karina, Blair, Robert James, Brosch, Katharina, Cheng, Yuqi, Colle, Romain, Connolly, Colm G., Corruble, Emmanuelle, Couvy-Duchesne, Baptiste, Crespo-Facorro, Benedicto, Cullen, Kathryn R., Dannlowski, Udo, Davey, Christopher G., Dohm, Katharina, Fullerton, Janice M., Gonul, Ali Saffet, Gotlib, Ian H., Grotegerd, Dominik, Hahn, Tim, Harrison, Ben J., He, Mengxin, Hickie, Ian B., Ho, Tiffany C., Iorfino, Frank, Jansen, Andreas, Jollant, Fabrice, Kircher, Tilo, Klimes-Dougan, Bonnie, Klug, Melissa, Leehr, Elisabeth J., Lippard, Elizabeth T. C., McLaughlin, Katie A., Meinert, Susanne, Miller, Adam Bryant, Mitchell, Philip B., Mwangi, Benson, Nenadić, Igor, Ojha, Amar, Overs, Bronwyn J., Pfarr, Julia-Katharina, Piras, Fabrizio, Ringwald, Kai G., Roberts, Gloria, Romer, Georg, Sanches, Marsal, Sheridan, Margaret A., Soares, Jair C., Spalletta, Gianfranco, Stein, Frederike, Teresi, Giana I., Tordesillas-Gutiérrez, Diana, Uyar-Demir, Aslihan, van der Wee, Nic J. A., van der Werff, Steven J., Vermeiren, Robert R. J. M., Winter, Alexandra, Wu, Mon-Ju, Yang, Tony T., Thompson, Paul M., Rentería, Miguel E., Jahanshad, Neda, Blumberg, Hilary P., van Harmelen, Anne-Laura, and Schmaal, Lianne
Published: 2022
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29. The unbalanced reorganization of weaker functional connections induces the altered brain network topology in schizophrenia

Author: Mastrandrea, Rossana, Piras, Fabrizio, Gabrielli, Andrea, Banaj, Nerisa, Caldarelli, Guido, Spalletta, Gianfranco, and Gili, Tommaso
Subjects: Quantitative Biology - Neurons and Cognition, Condensed Matter - Statistical Mechanics, Physics - Biological Physics
Abstract: Network neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in forty-four medicated schizophrenic patients and forty healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones., Comment: 37 pages, 8 figures
Published: 2019

30. Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA

Author: Radua, Joaquim, Vieta, Eduard, Shinohara, Russell, Kochunov, Peter, Quidé, Yann, Green, Melissa J, Weickert, Cynthia S, Weickert, Thomas, Bruggemann, Jason, Kircher, Tilo, Nenadić, Igor, Cairns, Murray J, Seal, Marc, Schall, Ulrich, Henskens, Frans, Fullerton, Janice M, Mowry, Bryan, Pantelis, Christos, Lenroot, Rhoshel, Cropley, Vanessa, Loughland, Carmel, Scott, Rodney, Wolf, Daniel, Satterthwaite, Theodore D, Tan, Yunlong, Sim, Kang, Piras, Fabrizio, Spalletta, Gianfranco, Banaj, Nerisa, Pomarol-Clotet, Edith, Solanes, Aleix, Albajes-Eizagirre, Anton, Canales-Rodríguez, Erick J, Sarro, Salvador, Di Giorgio, Annabella, Bertolino, Alessandro, Stäblein, Michael, Oertel, Viola, Knöchel, Christian, Borgwardt, Stefan, du Plessis, Stefan, Yun, Je-Yeon, Kwon, Jun Soo, Dannlowski, Udo, Hahn, Tim, Grotegerd, Dominik, Alloza, Clara, Arango, Celso, Janssen, Joost, Díaz-Caneja, Covadonga, Jiang, Wenhao, Calhoun, Vince, Ehrlich, Stefan, Yang, Kun, Cascella, Nicola G, Takayanagi, Yoichiro, Sawa, Akira, Tomyshev, Alexander, Lebedeva, Irina, Kaleda, Vasily, Kirschner, Matthias, Hoschl, Cyril, Tomecek, David, Skoch, Antonin, van Amelsvoort, Therese, Bakker, Geor, James, Anthony, Preda, Adrian, Weideman, Andrea, Stein, Dan J, Howells, Fleur, Uhlmann, Anne, Temmingh, Henk, López-Jaramillo, Carlos, Díaz-Zuluaga, Ana, Fortea, Lydia, Martinez-Heras, Eloy, Solana, Elisabeth, Llufriu, Sara, Jahanshad, Neda, Thompson, Paul, Turner, Jessica, van Erp, Theo, collaborators, ENIGMA Consortium, Glahn, David, Pearlson, Godfrey, Hong, Elliot, Krug, Axel, Carr, Vaughan, Tooney, Paul, Cooper, Gavin, Rasser, Paul, Michie, Patricia, Catts, Stanley, Gur, Raquel, Gur, Ruben, Yang, Fude, Fan, Fengmei, Chen, Jingxu, and Guo, Hua
Subjects: Brain Disorders, Biomedical Imaging, Mental Health, Schizophrenia, Neurosciences, Mental health, Adult, Algorithms, Cerebral Cortex, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Meta-Analysis as Topic, Middle Aged, Neuroimaging, Young Adult, Brain, Cortical thickness, Gray matter, Mega-analysis, Volume, ENIGMA Consortium collaborators, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery
Abstract: A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).
Published: 2020

31. The Relationship Between White Matter Microstructure and General Cognitive Ability in Patients With Schizophrenia and Healthy Participants in the ENIGMA Consortium

Author: Holleran, Laurena, Kelly, Sinead, Alloza, Clara, Agartz, Ingrid, Andreassen, Ole A, Arango, Celso, Banaj, Nerisa, Calhoun, Vince, Cannon, Dara, Carr, Vaughan, Corvin, Aiden, Glahn, David C, Gur, Ruben, Hong, Elliot, Hoschl, Cyril, Howells, Fleur M, James, Anthony, Janssen, Joost, Kochunov, Peter, Lawrie, Stephen M, Liu, Jingyu, Martinez, Covadonga, McDonald, Colm, Morris, Derek, Mothersill, David, Pantelis, Christos, Piras, Fabrizio, Potkin, Steven, Rasser, Paul E, Roalf, David, Rowland, Laura, Satterthwaite, Theodore, Schall, Ulrich, Spalletta, Gianfranco, Spaniel, Filip, Stein, Dan J, Uhlmann, Anne, Voineskos, Aristotle, Zalesky, Andrew, van Erp, Theo GM, Turner, Jessica A, Deary, Ian J, Thompson, Paul M, Jahanshad, Neda, and Donohoe, Gary
Subjects: Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Prevention, Neurosciences, Behavioral and Social Science, Clinical Research, Brain Disorders, Serious Mental Illness, Schizophrenia, Mental Health, Mental health, Adult, Anisotropy, Brain, Case-Control Studies, Cognition, Diffusion Tensor Imaging, Factor Analysis, Statistical, Female, Healthy Volunteers, Humans, Intelligence, Male, Middle Aged, Neural Pathways, Principal Component Analysis, Schizophrenic Psychology, Wechsler Scales, White Matter, ENIGMA, Meta-Analysis, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Clinical and health psychology
Abstract: ObjectiveSchizophrenia has recently been associated with widespread white matter microstructural abnormalities, but the functional effects of these abnormalities remain unclear. Widespread heterogeneity of results from studies published to date preclude any definitive characterization of the relationship between white matter and cognitive performance in schizophrenia. Given the relevance of deficits in cognitive function to predicting social and functional outcomes in schizophrenia, the authors carried out a meta-analysis of available data through the ENIGMA Consortium, using a common analysis pipeline, to elucidate the relationship between white matter microstructure and a measure of general cognitive performance, IQ, in patients with schizophrenia and healthy participants.MethodsThe meta-analysis included 760 patients with schizophrenia and 957 healthy participants from 11 participating ENIGMA Consortium sites. For each site, principal component analysis was used to calculate both a global fractional anisotropy component (gFA) and a fractional anisotropy component for six long association tracts (LA-gFA) previously associated with cognition.ResultsMeta-analyses of regression results indicated that gFA accounted for a significant amount of variation in cognition in the full sample (effect size [Hedges' g]=0.27, CI=0.17-0.36), with similar effects sizes observed for both the patient (effect size=0.20, CI=0.05-0.35) and healthy participant groups (effect size=0.32, CI=0.18-0.45). Comparable patterns of association were also observed between LA-gFA and cognition for the full sample (effect size=0.28, CI=0.18-0.37), the patient group (effect size=0.23, CI=0.09-0.38), and the healthy participant group (effect size=0.31, CI=0.18-0.44).ConclusionsThis study provides robust evidence that cognitive ability is associated with global structural connectivity, with higher fractional anisotropy associated with higher IQ. This association was independent of diagnosis; while schizophrenia patients tended to have lower fractional anisotropy and lower IQ than healthy participants, the comparable size of effect in each group suggested a more general, rather than disease-specific, pattern of association.
Published: 2020

32. Mapping Cortical and Subcortical Asymmetry in Obsessive-Compulsive Disorder: Findings From the ENIGMA Consortium

Author: Kong, Xiang-Zhen, Boedhoe, Premika SW, Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie H, Arnold, Paul D, Assogna, Francesca, Baker, Justin T, Batistuzzo, Marcelo C, Benedetti, Francesco, Beucke, Jan C, Bollettini, Irene, Bose, Anushree, Brem, Silvia, Brennan, Brian P, Buitelaar, Jan, Calvo, Rosa, Cheng, Yuqi, Cho, Kang Ik K, Dallaspezia, Sara, Denys, Damiaan, Ely, Benjamin A, Feusner, Jamie, Fitzgerald, Kate D, Fouche, Jean-Paul, Fridgeirsson, Egill A, Glahn, David C, Gruner, Patricia, Gürsel, Deniz A, Hauser, Tobias U, Hirano, Yoshiyuki, Hoexter, Marcelo Q, Hu, Hao, Huyser, Chaim, James, Anthony, Jaspers-Fayer, Fern, Kathmann, Norbert, Kaufmann, Christian, Koch, Kathrin, Kuno, Masaru, Kvale, Gerd, Kwon, Jun Soo, Lazaro, Luisa, Liu, Yanni, Lochner, Christine, Marques, Paulo, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Mataix-Cols, David, Medland, Sarah E, Menchón, José M, Minuzzi, Luciano, Moreira, Pedro S, Morer, Astrid, Morgado, Pedro, Nakagawa, Akiko, Nakamae, Takashi, Nakao, Tomohiro, Narayanaswamy, Janardhanan C, Nurmi, Erika L, O'Neill, Joseph, Pariente, Jose C, Perriello, Chris, Piacentini, John, Piras, Fabrizio, Piras, Federica, Pittenger, Christopher, Reddy, YC Janardhan, Rus-Oswald, Oana Georgiana, Sakai, Yuki, Sato, Joao R, Schmaal, Lianne, Simpson, H Blair, Soreni, Noam, Soriano-Mas, Carles, Spalletta, Gianfranco, Stern, Emily R, Stevens, Michael C, Stewart, S Evelyn, Szeszko, Philip R, Tolin, David F, Tsuchiyagaito, Aki, van Rooij, Daan, van Wingen, Guido A, Venkatasubramanian, Ganesan, Wang, Zhen, Yun, Je-Yeon, Group, ENIGMA OCD Working, Anticevic, Alan, Banaj, Nerisa, and Bargalló, Nuria
Subjects: Biological Psychology, Biomedical and Clinical Sciences, Psychology, Serious Mental Illness, Mental Health, Neurosciences, Brain Disorders, Clinical Research, Anxiety Disorders, Neurological, Mental health, Adult, Brain, Brain Mapping, Child, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Obsessive-Compulsive Disorder, Thalamus, Brain asymmetry, Laterality, Mega-analysis, Obsessive-compulsive disorder, Pallidum, ENIGMA OCD Working Group, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biological sciences, Biomedical and clinical sciences
Abstract: BackgroundLateralized dysfunction has been suggested in obsessive-compulsive disorder (OCD). However, it is currently unclear whether OCD is characterized by abnormal patterns of brain structural asymmetry. Here we carried out what is by far the largest study of brain structural asymmetry in OCD.MethodsWe studied a collection of 16 pediatric datasets (501 patients with OCD and 439 healthy control subjects), as well as 30 adult datasets (1777 patients and 1654 control subjects) from the OCD Working Group within the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Consortium. Asymmetries of the volumes of subcortical structures, and of measures of regional cortical thickness and surface areas, were assessed based on T1-weighted magnetic resonance imaging scans, using harmonized image analysis and quality control protocols. We investigated possible alterations of brain asymmetry in patients with OCD. We also explored potential associations of asymmetry with specific aspects of the disorder and medication status.ResultsIn the pediatric datasets, the largest case-control differences were observed for volume asymmetry of the thalamus (more leftward; Cohen's d = 0.19) and the pallidum (less leftward; d = -0.21). Additional analyses suggested putative links between these asymmetry patterns and medication status, OCD severity, or anxiety and depression comorbidities. No significant case-control differences were found in the adult datasets.ConclusionsThe results suggest subtle changes of the average asymmetry of subcortical structures in pediatric OCD, which are not detectable in adults with the disorder. These findings may reflect altered neurodevelopmental processes in OCD.
Published: 2020

33. Corrigendum

Author: Yun, Je-Yeon, Boedhoe, Premika SW, Vriend, Chris, Jahanshad, Neda, Abe, Yoshinari, Ameis, Stephanie H, Anticevic, Alan, Arnold, Paul D, Batistuzzo, Marcelo C, Benedetti, Francesco, Beucke, Jan C, Bollettini, Irene, Bose, Anushree, Brem, Silvia, Calvo, Anna, Cheng, Yuqi, Cho, Kang Ik K, Ciullo, Valentina, Dallaspezia, Sara, Denys, Damiaan, Feusner, Jamie D, Fouche, Jean-Paul, Gimenez, Monica, Gruner, Patricia, Hibar, Derrek P, Hoexter, Marcelo Q, Hu, Hao, Huyser, Chaim, Ikari, Keisuke, Kathmann, Norbert, Kaufmann, Christian, Koch, Kathrin, Lazaro, Luisa, Lochner, Christine, Marques, Paulo, Marsh, Rachel, Martinez-Zalacain, Ignacio, Mataix-Cols, David, Menchon, Jose M, Minuzzi, Luciano, Morgado, Pedro, Moreira, Pedro, Nakamae, Takashi, Nakao, Tomohiro, Narayanaswamy, Janardhanan C, Nurmi, Erika L, O'Neill, Joseph, Piacentini, John, Piras, Fabrizio, Piras, Federica, Reddy, YC Janardhan, Sato, Joao R, Simpson, H Blair, Soreni, Noam, Soriano-Mas, Carles, Spalletta, Gianfranco, Stevens, Michael C, Szeszko, Philip R, Tolin, David F, Venkatasubramanian, Ganesan, Walitza, Susanne, Wang, Zhen, van Wingen, Guido A, Xu, Jian, Xu, Xiufeng, Zhao, Qing, Thompson, Paul M, Stein, Dan J, van den Heuvel, Odile A, and Kwon, Jun Soo
Subjects: Biomedical and Clinical Sciences, Health Sciences, Psychology, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Published: 2020

34. 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

35. Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium

Author: Yun, Je-Yeon, Boedhoe, Premika SW, Vriend, Chris, Jahanshad, Neda, Abe, Yoshinari, Ameis, Stephanie H, Anticevic, Alan, Arnold, Paul D, Batistuzzo, Marcelo C, Benedetti, Francesco, Beucke, Jan C, Bollettini, Irene, Bose, Anushree, Brem, Silvia, Calvo, Anna, Cheng, Yuqi, Cho, Kang Ik K, Ciullo, Valentina, Dallaspezia, Sara, Denys, Damiaan, Feusner, Jamie D, Fouche, Jean-Paul, Giménez, Mònica, Gruner, Patricia, Hibar, Derrek P, Hoexter, Marcelo Q, Hu, Hao, Huyser, Chaim, Ikari, Keisuke, Kathmann, Norbert, Kaufmann, Christian, Koch, Kathrin, Lazaro, Luisa, Lochner, Christine, Marques, Paulo, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Mataix-Cols, David, Menchón, José M, Minuzzi, Luciano, Morgado, Pedro, Moreira, Pedro, Nakamae, Takashi, Nakao, Tomohiro, Narayanaswamy, Janardhanan C, Nurmi, Erika L, O’Neill, Joseph, Piacentini, John, Piras, Fabrizio, Piras, Federica, Reddy, YC Janardhan, Sato, Joao R, Simpson, H Blair, Soreni, Noam, Soriano-Mas, Carles, Spalletta, Gianfranco, Stevens, Michael C, Szeszko, Philip R, Tolin, David F, Venkatasubramanian, Ganesan, Walitza, Susanne, Wang, Zhen, van Wingen, Guido A, Xu, Jian, Xu, Xiufeng, Zhao, Qing, van den Heuvel, Odile3 A, Stein, Dan J, Thompson, Paul M, Ik, Kang, and Cho, K
Subjects: Mental Health, Brain Disorders, Serious Mental Illness, Clinical Research, Neurosciences, Neurological, Mental health, Adult, Brain, Cerebral Cortex, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Neural Pathways, Obsessive-Compulsive Disorder, brain structural covariance network, graph theory, obsessive-compulsive disorder, pharmacotherapy, illness duration, ENIGMA-OCD working group, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery
Abstract: Brain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect common trajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsive disorder (OCD) may provide clues to the pathophysiology of this neurodevelopmental disorder. Using T1-weighted MRI scans acquired from 1616 individuals with OCD and 1463 healthy controls across 37 datasets participating in the ENIGMA-OCD Working Group, we calculated intra-individual brain structural covariance networks (using the bilaterally-averaged values of 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes), in which edge weights were proportional to the similarity between two brain morphological features in terms of deviation from healthy controls (i.e. z-score transformed). Global networks were characterized using measures of network segregation (clustering and modularity), network integration (global efficiency), and their balance (small-worldness), and their community membership was assessed. Hub profiling of regional networks was undertaken using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measures were integrated across the 37 datasets using a meta-analytical approach. These network measures were summated across the network density range of K = 0.10-0.25 per participant, and were integrated across the 37 datasets using a meta-analytical approach. Compared with healthy controls, at a global level, the structural covariance networks of OCD showed lower clustering (P
Published: 2020

36. Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging

Author: for the ENIGMA consortium, Zhao, Yuji, Laansma, Max A., van Heese, Eva M., Owens-Walton, Conor, Parkes, Laura M., Debove, Ines, Rummel, Christian, Wiest, Roland, Cendes, Fernando, Guimaraes, Rachel, Yasuda, Clarissa Lin, Wang, Jiun-Jie, Anderson, Tim J., Dalrymple-Alford, John C., Melzer, Tracy R., Pitcher, Toni L., Schmidt, Reinhold, Schwingenschuh, Petra, Garraux, Gäetan, Rango, Mario, Squarcina, Letizia, Al-Bachari, Sarah, Emsley, Hedley C. A., Klein, Johannes C., Mackay, Clare E., Dirkx, Michiel F., Helmich, Rick, Assogna, Francesca, Piras, Fabrizio, Bright, Joanna K., Spalletta, Gianfranco, Poston, Kathleen, Lochner, Christine, McMillan, Corey T., Weintraub, Daniel, Druzgal, Jason, Newman, Benjamin, Van Den Heuvel, Odile A., Jahanshad, Neda, Thompson, Paul M., van der Werf, Ysbrand D., Gutman, Boris, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Abdulkadir, Ahmed, editor, Bathula, Deepti R., editor, Dvornek, Nicha C., editor, Habes, Mohamad, editor, Kia, Seyed Mostafa, editor, Kumar, Vinod, editor, and Wolfers, Thomas, editor
Published: 2022
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37. 409. Cortico-Thalamic Structural Co-Variation Networks are Related to Familial Risk for Schizophrenia in the Context of Lower Nuclei Volume Estimates in Patients: An ENIGMA Study

Author: Lella, Annalisa, primary, Antonucci, Linda A., additional, Weinberger, Daniel R., additional, Glahn, David C., additional, Sim, Kang, additional, Gruber, Oliver, additional, Chung, Young-Chul, additional, Sugranyes, Gisela, additional, Clote, Edith Pomarol, additional, Marcelis, Machteld, additional, Kircher, Tilo, additional, Van Rheenen, Tamsyn E., additional, Sponheim, Scott R., additional, Dannlowski, Udo, additional, Iasevoli, Felice, additional, Pearlson, Godfrey D., additional, Green, Melissa J., additional, Spalletta, Gianfranco, additional, Lee, Tae Young, additional, Turner, Jessica A., additional, van Erp, Theo G.M., additional, Thompson, Paul M., additional, Bertolino, Alessandro, additional, and Pergola, Giulio, additional
Published: 2024
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38. New insights into the genetic etiology of Alzheimer’s disease and related dementias

Author: Bellenguez, Céline, Küçükali, Fahri, Jansen, Iris E., Kleineidam, Luca, Moreno-Grau, Sonia, Amin, Najaf, Naj, Adam C., Campos-Martin, Rafael, Grenier-Boley, Benjamin, Andrade, Victor, Holmans, Peter A., Boland, Anne, Damotte, Vincent, van der Lee, Sven J., Costa, Marcos R., Kuulasmaa, Teemu, Yang, Qiong, de Rojas, Itziar, Bis, Joshua C., Yaqub, Amber, Prokic, Ivana, Chapuis, Julien, Ahmad, Shahzad, Giedraitis, Vilmantas, Aarsland, Dag, Garcia-Gonzalez, Pablo, Abdelnour, Carla, Alarcón-Martín, Emilio, Alcolea, Daniel, Alegret, Montserrat, Alvarez, Ignacio, Álvarez, Victoria, Armstrong, Nicola J., Tsolaki, Anthoula, Antúnez, Carmen, Appollonio, Ildebrando, Arcaro, Marina, Archetti, Silvana, Pastor, Alfonso Arias, Arosio, Beatrice, Athanasiu, Lavinia, Bailly, Henri, Banaj, Nerisa, Baquero, Miquel, Barral, Sandra, Beiser, Alexa, Pastor, Ana Belén, Below, Jennifer E., Benchek, Penelope, Benussi, Luisa, Berr, Claudine, Besse, Céline, Bessi, Valentina, Binetti, Giuliano, Bizarro, Alessandra, Blesa, Rafael, Boada, Mercè, Boerwinkle, Eric, Borroni, Barbara, Boschi, Silvia, Bossù, Paola, Bråthen, Geir, Bressler, Jan, Bresner, Catherine, Brodaty, Henry, Brookes, Keeley J., Brusco, Luis Ignacio, Buiza-Rueda, Dolores, Bûrger, Katharina, Burholt, Vanessa, Bush, William S., Calero, Miguel, Cantwell, Laura B., Chene, Geneviève, Chung, Jaeyoon, Cuccaro, Michael L., Carracedo, Ángel, Cecchetti, Roberta, Cervera-Carles, Laura, Charbonnier, Camille, Chen, Hung-Hsin, Chillotti, Caterina, Ciccone, Simona, Claassen, Jurgen A. H. R., Clark, Christopher, Conti, Elisa, Corma-Gómez, Anaïs, Costantini, Emanuele, Custodero, Carlo, Daian, Delphine, Dalmasso, Maria Carolina, Daniele, Antonio, Dardiotis, Efthimios, Dartigues, Jean-François, de Deyn, Peter Paul, de Paiva Lopes, Katia, de Witte, Lot D., Debette, Stéphanie, Deckert, Jürgen, del Ser, Teodoro, Denning, Nicola, DeStefano, Anita, Dichgans, Martin, Diehl-Schmid, Janine, Diez-Fairen, Mónica, Rossi, Paolo Dionigi, Djurovic, Srdjan, Duron, Emmanuelle, Düzel, Emrah, Dufouil, Carole, Eiriksdottir, Gudny, Engelborghs, Sebastiaan, Escott-Price, Valentina, Espinosa, Ana, Ewers, Michael, Faber, Kelley M., Fabrizio, Tagliavini, Nielsen, Sune Fallgaard, Fardo, David W., Farotti, Lucia, Fenoglio, Chiara, Fernández-Fuertes, Marta, Ferrari, Raffaele, Ferreira, Catarina B., Ferri, Evelyn, Fin, Bertrand, Fischer, Peter, Fladby, Tormod, Fließbach, Klaus, Fongang, Bernard, Fornage, Myriam, Fortea, Juan, Foroud, Tatiana M., Fostinelli, Silvia, Fox, Nick C., Franco-Macías, Emlio, Bullido, María J., Frank-García, Ana, Froelich, Lutz, Fulton-Howard, Brian, Galimberti, Daniela, García-Alberca, Jose Maria, García-González, Pablo, Garcia-Madrona, Sebastian, Garcia-Ribas, Guillermo, Ghidoni, Roberta, Giegling, Ina, Giorgio, Giaccone, Goate, Alison M., Goldhardt, Oliver, Gomez-Fonseca, Duber, González-Pérez, Antonio, Graff, Caroline, Grande, Giulia, Green, Emma, Grimmer, Timo, Grünblatt, Edna, Grunin, Michelle, Gudnason, Vilmundur, Guetta-Baranes, Tamar, Haapasalo, Annakaisa, Hadjigeorgiou, Georgios, Haines, Jonathan L., Hamilton-Nelson, Kara L., Hampel, Harald, Hanon, Olivier, Hardy, John, Hartmann, Annette M., Hausner, Lucrezia, Harwood, Janet, Heilmann-Heimbach, Stefanie, Helisalmi, Seppo, Heneka, Michael T., Hernández, Isabel, Herrmann, Martin J., Hoffmann, Per, Holmes, Clive, Holstege, Henne, Vilas, Raquel Huerto, Hulsman, Marc, Humphrey, Jack, Biessels, Geert Jan, Jian, Xueqiu, Johansson, Charlotte, Jun, Gyungah R., Kastumata, Yuriko, Kauwe, John, Kehoe, Patrick G., Kilander, Lena, Ståhlbom, Anne Kinhult, Kivipelto, Miia, Koivisto, Anne, Kornhuber, Johannes, Kosmidis, Mary H., Kukull, Walter A., Kuksa, Pavel P., Kunkle, Brian W., Kuzma, Amanda B., Lage, Carmen, Laukka, Erika J., Launer, Lenore, Lauria, Alessandra, Lee, Chien-Yueh, Lehtisalo, Jenni, Lerch, Ondrej, Lleó, Alberto, Longstreth, Jr, William, Lopez, Oscar, de Munain, Adolfo Lopez, Love, Seth, Löwemark, Malin, Luckcuck, Lauren, Lunetta, Kathryn L., Ma, Yiyi, Macías, Juan, MacLeod, Catherine A., Maier, Wolfgang, Mangialasche, Francesca, Spallazzi, Marco, Marquié, Marta, Marshall, Rachel, Martin, Eden R., Montes, Angel Martín, Rodríguez, Carmen Martínez, Masullo, Carlo, Mayeux, Richard, Mead, Simon, Mecocci, Patrizia, Medina, Miguel, Meggy, Alun, Mehrabian, Shima, Mendoza, Silvia, Menéndez-González, Manuel, Mir, Pablo, Moebus, Susanne, Mol, Merel, Molina-Porcel, Laura, Montrreal, Laura, Morelli, Laura, Moreno, Fermin, Morgan, Kevin, Mosley, Thomas, Nöthen, Markus M., Muchnik, Carolina, Mukherjee, Shubhabrata, Nacmias, Benedetta, Ngandu, Tiia, Nicolas, Gael, Nordestgaard, Børge G., Olaso, Robert, Orellana, Adelina, Orsini, Michela, Ortega, Gemma, Padovani, Alessandro, Paolo, Caffarra, Papenberg, Goran, Parnetti, Lucilla, Pasquier, Florence, Pastor, Pau, Peloso, Gina, Pérez-Cordón, Alba, Pérez-Tur, Jordi, Pericard, Pierre, Peters, Oliver, Pijnenburg, Yolande A. L., Pineda, Juan A., Piñol-Ripoll, Gerard, Pisanu, Claudia, Polak, Thomas, Popp, Julius, Posthuma, Danielle, Priller, Josef, Puerta, Raquel, Quenez, Olivier, Quintela, Inés, Thomassen, Jesper Qvist, Rábano, Alberto, Rainero, Innocenzo, Rajabli, Farid, Ramakers, Inez, Real, Luis M., Reinders, Marcel J. T., Reitz, Christiane, Reyes-Dumeyer, Dolly, Ridge, Perry, Riedel-Heller, Steffi, Riederer, Peter, Roberto, Natalia, Rodriguez-Rodriguez, Eloy, Rongve, Arvid, Allende, Irene Rosas, Rosende-Roca, Maitée, Royo, Jose Luis, Rubino, Elisa, Rujescu, Dan, Sáez, María Eugenia, Sakka, Paraskevi, Saltvedt, Ingvild, Sanabria, Ángela, Sánchez-Arjona, María Bernal, Sanchez-Garcia, Florentino, Juan, Pascual Sánchez, Sánchez-Valle, Raquel, Sando, Sigrid B., Sarnowski, Chloé, Satizabal, Claudia L., Scamosci, Michela, Scarmeas, Nikolaos, Scarpini, Elio, Scheltens, Philip, Scherbaum, Norbert, Scherer, Martin, Schmid, Matthias, Schneider, Anja, Schott, Jonathan M., Selbæk, Geir, Seripa, Davide, Serrano, Manuel, Sha, Jin, Shadrin, Alexey A., Skrobot, Olivia, Slifer, Susan, Snijders, Gijsje J. L., Soininen, Hilkka, Solfrizzi, Vincenzo, Solomon, Alina, Song, Yeunjoo, Sorbi, Sandro, Sotolongo-Grau, Oscar, Spalletta, Gianfranco, Spottke, Annika, Squassina, Alessio, Stordal, Eystein, Tartan, Juan Pablo, Tárraga, Lluís, Tesí, Niccolo, Thalamuthu, Anbupalam, Thomas, Tegos, Tosto, Giuseppe, Traykov, Latchezar, Tremolizzo, Lucio, Tybjærg-Hansen, Anne, Uitterlinden, Andre, Ullgren, Abbe, Ulstein, Ingun, Valero, Sergi, Valladares, Otto, Broeckhoven, Christine Van, Vance, Jeffery, Vardarajan, Badri N., van der Lugt, Aad, Dongen, Jasper Van, van Rooij, Jeroen, van Swieten, John, Vandenberghe, Rik, Verhey, Frans, Vidal, Jean-Sébastien, Vogelgsang, Jonathan, Vyhnalek, Martin, Wagner, Michael, Wallon, David, Wang, Li-San, Wang, Ruiqi, Weinhold, Leonie, Wiltfang, Jens, Windle, Gill, Woods, Bob, Yannakoulia, Mary, Zare, Habil, Zhao, Yi, Zhang, Xiaoling, Zhu, Congcong, Zulaica, Miren, Farrer, Lindsay A., Psaty, Bruce M., Ghanbari, Mohsen, Raj, Towfique, Sachdev, Perminder, Mather, Karen, Jessen, Frank, Ikram, M. Arfan, de Mendonça, Alexandre, Hort, Jakub, Tsolaki, Magda, Pericak-Vance, Margaret A., Amouyel, Philippe, Williams, Julie, Frikke-Schmidt, Ruth, Clarimon, Jordi, Deleuze, Jean-François, Rossi, Giacomina, Seshadri, Sudha, Andreassen, Ole A., Ingelsson, Martin, Hiltunen, Mikko, Sleegers, Kristel, Schellenberg, Gerard D., van Duijn, Cornelia M., Sims, Rebecca, van der Flier, Wiesje M., Ruiz, Agustín, Ramirez, Alfredo, and Lambert, Jean-Charles
Published: 2022
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39. Deep Learning for Quality Control of Subcortical Brain 3D Shape Models

Author: Petrov, Dmitry, Kuznetsov, Boris A. Gutman Egor, van Erp, Theo G. M., Turner, Jessica A., Schmaal, Lianne, Veltman, Dick, Wang, Lei, Alpert, Kathryn, Isaev, Dmitry, Zavaliangos-Petropulu, Artemis, Ching, Christopher R. K., Calhoun, Vince, Glahn, David, Satterthwaite, Theodore D., Andreassen, Ole Andreas, Borgwardt, Stefan, Howells, Fleur, Groenewold, Nynke, Voineskos, Aristotle, Radua, Joaquim, Potkin, Steven G., Crespo-Facorro, Benedicto, Tordesillas-Gutierrez, Diana, Shen, Li, Lebedeva, Irina, Spalletta, Gianfranco, Donohoe, Gary, Kochunov, Peter, Rosa, Pedro G. P., James, Anthony, Dannlowski, Udo, Baune, Bernhard T., Aleman, Andre, Gotlib, Ian H., Walter, Henrik, Walter, Martin, Soares, Jair C., Ehrlich, Stefan, Gur, Ruben C., Doan, N. Trung, Agartz, Ingrid, Westlye, Lars T., Harrisberger, Fabienne, Riecher-Rossler, Anita, Uhlmann, Anne, Stein, Dan J., Dickie, Erin W., Pomarol-Clotet, Edith, Fuentes-Claramonte, Paola, Canales-Rodriguez, Erick Jorge, Salvador, Raymond, Huang, Alexander J., Roiz-Santianez, Roberto, Cong, Shan, Tomyshev, Alexander, Piras, Fabrizio, Vecchio, Daniela, Banaj, Nerisa, Ciullo, Valentina, Hong, Elliot, Busatto, Geraldo, Zanetti, Marcus V., Serpa, Mauricio H., Cervenka, Simon, Kelly, Sinead, Grotegerd, Dominik, Sacchet, Matthew D., Veer, Ilya M., Li, Meng, Wu, Mon-Ju, Irungu, Benson, Walton, Esther, and Thompson, Paul M.
Subjects: Quantitative Biology - Neurons and Cognition
Abstract: We present several deep learning models for assessing the morphometric fidelity of deep grey matter region models extracted from brain MRI. We test three different convolutional neural net architectures (VGGNet, ResNet and Inception) over 2D maps of geometric features. Further, we present a novel geometry feature augmentation technique based on a parametric spherical mapping. Finally, we present an approach for model decision visualization, allowing human raters to see the areas of subcortical shapes most likely to be deemed of failing quality by the machine. Our training data is comprised of 5200 subjects from the ENIGMA Schizophrenia MRI cohorts, and our test dataset contains 1500 subjects from the ENIGMA Major Depressive Disorder cohorts. Our final models reduce human rater time by 46-70%. ResNet outperforms VGGNet and Inception for all of our predictive tasks., Comment: Accepted to Shape in Medical Imaging (ShapeMI) workshop at MICCAI 2018. arXiv admin note: substantial text overlap with arXiv:1707.06353
Published: 2018

40. Neuropsychiatric, neuropsychological, and neuroimaging features in isolated REM sleep behavior disorder: The importance of MCI

Author: Vacca, Mariacarolina, Assogna, Francesca, Pellicano, Clelia, Chiaravalloti, Agostino, Placidi, Fabio, Izzi, Francesca, Camedda, Riccardo, Schillaci, Orazio, Spalletta, Gianfranco, Lombardo, Caterina, Mercuri, Nicola Biagio, and Liguori, Claudio
Published: 2022
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41. Elevated plasma ceramide levels in post-menopausal women: a cross-sectional study

Author: Vozella, Valentina, Basit, Abdul, Piras, Fabrizio, Realini, Natalia, Armirotti, Andrea, Bossù, Paola, Assogna, Francesca, Sensi, Stefano L, Spalletta, Gianfranco, and Piomelli, Daniele
Subjects: Aging, Estrogen, Cancer, Adult, Aged, Ceramides, Cross-Sectional Studies, Female, Humans, Male, Middle Aged, Postmenopause, Young Adult, estrogen, menopause, sex-dependent, aging, ceramides, liquid chromatography/mass spectrometry, Biochemistry and Cell Biology, Physiology, Oncology and Carcinogenesis, Developmental Biology
Abstract: Circulating ceramide levels are abnormally elevated in age-dependent pathologies such as cardiovascular diseases, obesity and Alzheimer's disease. Nevertheless, the potential impact of age on plasma ceramide levels has not yet been systematically examined. In the present study, we quantified a focused panel of plasma ceramides and dihydroceramides in a cohort of 164 subjects (84 women) 19 to 80 years of age. After adjusting for potential confounders, multivariable linear regression analysis revealed a positive association between age and ceramide (d18:1/24:0) (β (SE) = 5.67 (2.38); p = .0198) and ceramide (d18:1/24:1) (β (SE) = 2.88 (.61); p < .0001) in women, and between age and ceramide (d18:1/24:1) in men (β (SE) = 1.86 (.77); p = .0179). In women of all ages, but not men, plasma ceramide (d18:1/24:1) was negatively correlated with plasma estradiol (r = -0.294; p = .007). Finally, in vitro experiments in human cancer cells expressing estrogen receptors showed that incubation with estradiol (10 nM, 24 h) significantly decreased ceramide accumulation. Together, the results suggest that aging is associated with an increase in circulating ceramide levels, which in post-menopausal women is at least partially associated with lower estradiol levels.
Published: 2019

42. An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group

Author: Boedhoe, Premika SW, Heymans, Martijn W, Schmaal, Lianne, Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie H, Anticevic, Alan, Arnold, Paul D, Batistuzzo, Marcelo C, Benedetti, Francesco, Beucke, Jan C, Bollettini, Irene, Bose, Anushree, Brem, Silvia, Calvo, Anna, Calvo, Rosa, Cheng, Yuqi, Cho, Kang Ik K, Ciullo, Valentina, Dallaspezia, Sara, Denys, Damiaan, Feusner, Jamie D, Fitzgerald, Kate D, Fouche, Jean-Paul, Fridgeirsson, Egill A, Gruner, Patricia, Hanna, Gregory L, Hibar, Derrek P, Hoexter, Marcelo Q, Hu, Hao, Huyser, Chaim, Jahanshad, Neda, James, Anthony, Kathmann, Norbert, Kaufmann, Christian, Koch, Kathrin, Kwon, Jun Soo, Lazaro, Luisa, Lochner, Christine, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Mataix-Cols, David, Menchón, José M, Minuzzi, Luciano, Morer, Astrid, Nakamae, Takashi, Nakao, Tomohiro, Narayanaswamy, Janardhanan C, Nishida, Seiji, Nurmi, Erika L, O'Neill, Joseph, Piacentini, John, Piras, Fabrizio, Piras, Federica, Reddy, YC Janardhan, Reess, Tim J, Sakai, Yuki, Sato, Joao R, Simpson, H Blair, Soreni, Noam, Soriano-Mas, Carles, Spalletta, Gianfranco, Stevens, Michael C, Szeszko, Philip R, Tolin, David F, van Wingen, Guido A, Venkatasubramanian, Ganesan, Walitza, Susanne, Wang, Zhen, Yun, Je-Yeon, Working-Group, ENIGMA-OCD, Thompson, Paul M, Stein, Dan J, van den Heuvel, Odile A, and Twisk, Jos WR
Subjects: Biomedical and Clinical Sciences, Information and Computing Sciences, Neurosciences, Applied Computing, Machine Learning, neuroimaging, MRI, IPD meta-analysis, mega-analysis, linear mixed-effect models, ENIGMA-OCD Working-Group, Cognitive Sciences, Applied computing, Machine learning
Abstract: Objective: Brain imaging communities focusing on different diseases have increasingly started to collaborate and to pool data to perform well-powered meta- and mega-analyses. Some methodologists claim that a one-stage individual-participant data (IPD) mega-analysis can be superior to a two-stage aggregated data meta-analysis, since more detailed computations can be performed in a mega-analysis. Before definitive conclusions regarding the performance of either method can be drawn, it is necessary to critically evaluate the methodology of, and results obtained by, meta- and mega-analyses. Methods: Here, we compare the inverse variance weighted random-effect meta-analysis model with a multiple linear regression mega-analysis model, as well as with a linear mixed-effects random-intercept mega-analysis model, using data from 38 cohorts including 3,665 participants of the ENIGMA-OCD consortium. We assessed the effect sizes and standard errors, and the fit of the models, to evaluate the performance of the different methods. Results: The mega-analytical models showed lower standard errors and narrower confidence intervals than the meta-analysis. Similar standard errors and confidence intervals were found for the linear regression and linear mixed-effects random-intercept models. Moreover, the linear mixed-effects random-intercept models showed better fit indices compared to linear regression mega-analytical models. Conclusions: Our findings indicate that results obtained by meta- and mega-analysis differ, in favor of the latter. In multi-center studies with a moderate amount of variation between cohorts, a linear mixed-effects random-intercept mega-analytical framework appears to be the better approach to investigate structural neuroimaging data.
Published: 2019

43. Childhood Trauma in Bipolar Disorders

Author: Janiri, Delfina, Spalletta, Gianfranco, Sani, Gabriele, Spalletta, Gianfranco, editor, Janiri, Delfina, editor, Piras, Federica, editor, and Sani, Gabriele, editor
Published: 2020
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44. Childhood Trauma in Obsessive-Compulsive Disorder

Author: Piras, Federica, Spalletta, Gianfranco, Spalletta, Gianfranco, editor, Janiri, Delfina, editor, Piras, Federica, editor, and Sani, Gabriele, editor
Published: 2020
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45. Neurobiological Basis of Childhood Trauma and the Risk for Neurological Deficits Later in Life

Author: Assogna, Francesca, Piras, Federica, Spalletta, Gianfranco, Spalletta, Gianfranco, editor, Janiri, Delfina, editor, Piras, Federica, editor, and Sani, Gabriele, editor
Published: 2020
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46. Introduction on Childhood Trauma in Mental Disorders: A Comprehensive Approach

Author: Janiri, Delfina, Sani, Gabriele, Piras, Federica, Spalletta, Gianfranco, Spalletta, Gianfranco, editor, Janiri, Delfina, editor, Piras, Federica, editor, and Sani, Gabriele, editor
Published: 2020
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47. The thalamus and its subnuclei—a gateway to obsessive-compulsive disorder

Author: Weeland, Cees J., Kasprzak, Selina, de Joode, Niels T., Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie H., Anticevic, Alan, Arnold, Paul D., Balachander, Srinivas, Banaj, Nerisa, Bargallo, Nuria, Batistuzzo, Marcelo C., Benedetti, Francesco, Beucke, Jan C., Bollettini, Irene, Brecke, Vilde, Brem, Silvia, Cappi, Carolina, Cheng, Yuqi, Cho, Kang Ik K., Costa, Daniel L. C., Dallaspezia, Sara, Denys, Damiaan, Eng, Goi Khia, Ferreira, Sónia, Feusner, Jamie D., Fontaine, Martine, Fouche, Jean-Paul, Grazioplene, Rachael G., Gruner, Patricia, He, Mengxin, Hirano, Yoshiyuki, Hoexter, Marcelo Q., Huyser, Chaim, Hu, Hao, Jaspers-Fayer, Fern, Kathmann, Norbert, Kaufmann, Christian, Kim, Minah, Koch, Kathrin, Bin Kwak, Yoo, Kwon, Jun Soo, Lazaro, Luisa, Li, Chiang-shan R., Lochner, Christine, Marsh, Rachel, Martínez-Zalacaín, Ignacio, Mataix-Cols, David, Menchón, Jose M., Minnuzi, Luciano, Moreira, Pedro Silva, Morgado, Pedro, Nakagawa, Akiko, Nakamae, Takashi, Narayanaswamy, Janardhanan C., Nurmi, Erika L., Ortiz, Ana E., Pariente, Jose C., Piacentini, John, Picó-Pérez, Maria, Piras, Fabrizio, Piras, Federica, Pittenger, Christopher, Reddy, Y. C. Janardhan, Rodriguez-Manrique, Daniela, Sakai, Yuki, Shimizu, Eiji, Shivakumar, Venkataram, Simpson, Helen Blair, Soreni, Noam, Soriano-Mas, Carles, Sousa, Nuno, Spalletta, Gianfranco, Stern, Emily R., Stevens, Michael C., Stewart, S. Evelyn, Szeszko, Philip R., Takahashi, Jumpei, Tanamatis, Tais, Tang, Jinsong, Thorsen, Anders Lillevik, Tolin, David, van der Werf, Ysbrand D., van Marle, Hein, van Wingen, Guido A., Vecchio, Daniela, Venkatasubramanian, G., Walitza, Susanne, Wang, Jicai, Wang, Zhen, Watanabe, Anri, Wolters, Lidewij H., Xu, Xiufeng, Yun, Je-Yeon, Zhao, Qing, White, Tonya, Thompson, Paul M., Stein, Dan J., van den Heuvel, Odile A., and Vriend, Chris
Published: 2022
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48. Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging

Author: Petrov, Dmitry, Gutman, Boris A., Shih-Hua, Yu, van Erp, Theo G. M., Turner, Jessica A., Schmaal, Lianne, Veltman, Dick, Wang, Lei, Alpert, Kathryn, Isaev, Dmitry, Zavaliangos-Petropulu, Artemis, Ching, Christopher R. K., Calhoun, Vince, Glahn, David, Satterthwaite, Theodore D., Andreasen, Ole Andreas, Borgwardt, Stefan, Howells, Fleur, Groenewold, Nynke, Voineskos, Aristotle, Radua, Joaquim, Potkin, Steven G., Crespo-Facorro, Benedicto, Tordesillas-Gutierrez, Diana, Shen, Li, Lebedeva, Irina, Spalletta, Gianfranco, Donohoe, Gary, Kochunov, Peter, Rosa, Pedro G. P., James, Anthony, Dannlowski, Udo, Baune, Bernhard T., Aleman, Andre, Gotlib, Ian H., Walter, Henrik, Walter, Martin, Soares, Jair C., Ehrlich, Stefan, Gur, Ruben C., Doan, N. Trung, Agartz, Ingrid, Westlye, Lars T., Harrisberger, Fabienne, Riecher-Rossler, Anita, Uhlmann, Anne, Stein, Dan J., Dickie, Erin W., Pomarol-Clotet, Edith, Fuentes-Claramonte, Paola, Canales-Rodriguez, Erick Jorge, Salvador, Raymond, Huang, Alexander J., Roiz-Santianez, Roberto, Cong, Shan, Tomyshev, Alexander, Piras, Fabrizio, Vecchio, Daniela, Banaj, Nerisa, Ciullo, Valentina, Hong, Elliot, Busatto, Geraldo, Zanetti, Marcus V., Serpa, Mauricio H., Cervenka, Simon, Kelly, Sinead, Grotegerd, Dominik, Sacchet, Matthew D., Veer, Ilya M., Li, Meng, Wu, Mon-Ju, Irungu, Benson, Walton, Esther, and Thompson, Paul M.
Subjects: Quantitative Biology - Quantitative Methods
Abstract: As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70\%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability., Comment: Arxiv version of the MICCAI 2017 Machine Learning in Medical Imaging workshop paper
Published: 2017

49. Organization and hierarchy of the human functional brain network lead to a chain-like core

Author: Mastrandrea, Rossana, Gabrielli, Andrea, Piras, Fabrizio, Spalletta, Gianfranco, Caldarelli, Guido, and Gili, Tommaso
Subjects: Quantitative Biology - Neurons and Cognition, Physics - Biological Physics, Physics - Physics and Society, Statistics - Applications
Abstract: The brain is a paradigmatic example of a complex system as its functionality emerges as a global property of local mesoscopic and microscopic interactions. Complex network theory allows to elicit the functional architecture of the brain in terms of links (correlations) between nodes (grey matter regions) and to extract information out of the noise. Here we present the analysis of functional magnetic resonance imaging data from forty healthy humans during the resting condition for the investigation of the basal scaffold of the functional brain network organization. We show how brain regions tend to coordinate by forming a highly hierarchical chain-like structure of homogeneously clustered anatomical areas. A maximum spanning tree approach revealed the centrality of the occipital cortex and the peculiar aggregation of cerebellar regions to form a closed core. We also report the hierarchy of network segregation and the level of clusters integration as a function of the connectivity strength between brain regions.
Published: 2017

50. Publisher Correction: Variations in Dysbindin-1 are associated with cognitive response to antipsychotic drug treatment.

Author: Scheggia, Diego, Mastrogiacomo, Rosa, Mereu, Maddalena, Sannino, Sara, Straub, Richard E, Armando, Marco, Managò, Francesca, Guadagna, Simone, Piras, Fabrizio, Zhang, Fengyu, Kleinman, Joel E, Hyde, Thomas M, Kaalund, Sanne S, Pontillo, Maria, Orso, Genny, Caltagirone, Carlo, Borrelli, Emiliana, De Luca, Maria A, Vicari, Stefano, Weinberger, Daniel R, Spalletta, Gianfranco, and Papaleo, Francesco
Abstract: In the original version of this Article, references in the Methods section incorrectly referred to references in the Supplementary References section. The relevant references (now numbered 20, 27, 42, 47, 69-80) have been removed from the Supplementary References section of the Supplementary Information file and added to the References section of the main manuscript, in both the PDF and HTML versions of the Article.
Published: 2018

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