2026-10-14T19:45:08Z https://www.scidb.cn/oai

10.57760/sciencedb.29686 2026-03-11T09:29:57Z

2026-03-11 Data Sharing of the Meta-Analysis Maps from the DIRECT and ENIGMA Consortia doi:10.57760/sciencedb.29686 en Background and RationaleMajor depressive disorder (MDD) is one of the leading contributors to global disability and disease burden. Large-scale collaborative initiatives such as the Depression Imaging REsearch ConsorTium (DIRECT) and the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) MDD working group have substantially advanced our understanding of the neurobiological basis of depression. Integrating results across these consortia provides an important opportunity to identify robust and reproducible neuroimaging patterns associated with MDD.What’s Shared: The DIRECT–ENIGMA Meta MapsBased on the collaborative infrastructure of DIRECT Phase II and the ENIGMA consortium, we conducted a large-scale joint meta-analysis of neuroimaging data. To promote transparency, reproducibility, and open science, we are sharing the statistical meta-maps derived from this analysis.The shared maps include summary-level statistical T maps from each site. Specifically, the released maps cover cortical thickness and cortical surface area models for all participants, adults, and adolescents, including case–control comparisons, age-by-diagnosis and sex-by-diagnosis interaction analyses, age-of-onset analyses, illness-stage analyses, remission-status analyses, symptom-severity associations, and antidepressant-use analyses. For each site-level analysis, the shared outputs also include the corresponding ModelSummaryInfo.txt file, documenting the summary information.By releasing these maps, we aim to facilitate further research on the neurobiological mechanisms of depression and to support the development and validation of new neuroimaging analytic approaches.Key FeaturesLarge-scale dataset: Meta-analysis maps derived from the integrated analyses of the DIRECT Phase II and ENIGMA datasets, representing one of the largest neuroimaging samples of MDD to date.Support for secondary analyses: The shared maps enable threshold-free spatial analyses, cross-study comparisons, and benchmarking of neuroimaging analysis methods.Data Sharing Process and RequirementsAs these data consist of summary-level statistical maps rather than individual-level participant data, they do not contain protected health information.Therefore, the dataset is shared under an Unrestricted Sharing model:The meta-maps are openly accessible to the research community.Researchers may perform secondary analyses using these maps.Users should follow standard scientific citation practices when using the dataset.Code AvailabilityCustom scripts used for data processing, statistical analysis and figure generation in this study are publicly available at GitHub: https://github.com/Chaogan-Yan/PaperScripts/tree/master/Yan_2025.Author Contributions StatementCGY, ZHW, EP and LS conceived and designed the study and interpreted the data. All authors contributed to data collection, data processing and/or data provision from the participating cohorts, critically revised the manuscript, and approved the final version for publication. Depression; DIRECT; ENIGMA; Brain Imaging; Meta-analysis Chao-Gan Yan Zi-Han Wang Laura Han Nina Alexander Dag Alnæs Zeynep Başgöze Stéphanie E.E.C. Bauduin Jochen Bauer Francesco Benedetti Klaus Berger Bianca Besteher Tiana Borgers Robin Bülow Li-Ping Cao Guan-Mao Chen Jian-Shan Chen Si-Cheng Chen Tao Chen Tao-Lin Chen Xiao Chen Yan-Rong Chen Yu-Qi Cheng Zhao-Song Chu Romain Colle Colm G. Connolly Emmanuelle Corruble Baptiste Couvy-Duchesne Xi-Long Cui Kathryn R. Cullen Udo Dannlowski Christopher Davey Danai Dima Annemiek Dols Tracy Erwin-Grabner Jennifer W. Evans Kira Flinkenflügel Cynthia H.Y. Fu Paola Fuentes-Claramonte Qing-Lin Gao Qi-Yong Gong Ali Saffet Gonul Ian H. Gotlib Roberto Goya-Maldonado Hans J. Grabe Nynke Groenewold Dominik Grotegerd Wen-Bin Guo Tim Hahn Geoffrey B. Hall Paul J. Hamilton Ben Harrison Sean Hatton Can-Can He Marco Hermesdorf Ian B. Hickie Tiffany C. Ho Zheng-Jia-Yi Hu Qian Huang Neda Jahanshad Hamidreza Jamalabadi Alec Jamieson Xin-Lei Ji Feng-Nan Jia Toshiharu Kamishikiryo Tilo Kircher Sheri-Michelle Koopowitz Anna Kraus Axel Krug Li Kuang Jim Lagopoulos Thomas Lancaster Nils Inge Landrø Elisabeth J. Leehr Meng Li Bao-Juan Li Feng Li Hui-Xian Li Tao Li Xue-Ying Li Yi-Fan Liao Ze-Di Lin David E.J. Linden Hai-Long Liu Xiao-Yun Liu Zhe-Ning Liu Yan-Song Liu Yi-Cheng Long Jian-Ping Lu Bin Lu Frank P. MacMaster Luigi A. Maglanoc Andrew M. McIntosh Sarah Medland David M.A. Mehler Susanne Meinert Benson Mwangi Amar Ojha Go Okada Mardien L. Oudega Brenda W.J.H. Penninx Sara Poletti Edith Pomarol-Clotet Maria J. Portella Jiang Qiu Liesbeth Reneman Henning S. Rise Elena Rodriguez-Cano Matthew D. Sacchet Raymond Salvador Hendrik Schliepe Anouk Schrantee Xiao-Xiao Shan Hotaka Shinzato Tian-Mei Si Kang Sim Jair C Soares Dan J. Stein Benjamin Straube Lachlan T. Strike Peng-Feng Sun Florian Thomas-Odenthal Sophia I. Thomopoulos Paul M. Thompson Yara Toenders Aslihan Uyar Nic J.A. van der Wee Steven J.A. Van der Werff Ilya M. Veer Dick J. Veltman Henry Völzke Margot Wagenmakers Henrik Walter Martin Walter Chuan-Yue Wang Han-Lin Wang Hua-Ning Wang Xiang Wang Xin-Yu Wang Ying Wang Yu-Wei Wang Lars Tjelta Westlye Heather C. Whalley Sarah Whittle Nils R. Winter Katharina Wittfeld Mon-Ju Wu Xiao-Ping Wu Xin-Ran Wu Yan-Kun Wu Chen-Nan Wu Chun-Ming Xie Guang-Rong Xie Peng Xie Xiu-Feng Xu Zhen-Peng Xue Tony T. Yang Hong Yang Jian Yang Hua Yu Yong-Qiang Yu Min-Lan Yuan Yong-Gui Yuan Yu-Feng Zang Carlos A. Zarate Ai-Xia Zhang Ke-Rang Zhang Wei Zhang Jing-Ping Zhao Wen-Yi Zhong Jia-Jia Zhu Giovana B. Zunta Soares Xi-Nian Zuo Elena Pozzi Lianne Schmaal RESTRICTED dataset Science Data Bank