0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessAbstract 3D surface-based computational mapping is more sensitive to localized brain alterations in neurological, developmental and psychiatric conditions than traditional gross volumetric analysis, providing fine-scale 3D maps of a wide range of surface-based features. Here we introduce a scalable toolkit for large-scale computational surface analysis, with efficient algorithms for multisite data integration, statistical harmonization, accelerated multivariate statistics, and visualization. We showcase the utility of the toolkit by mapping subcortical shape variations and factors that affect them across 21 international samples from the ENIGMA Bipolar Disorder Working Group (N=3,373).
Yanghee Im, Leila Nabulsi, Melody J.Y. Kang, Sophia I. Thomopoulos, Ana M. Diaz Zuluaga, Anders M. Dale, Andriana Karuk, Annabella Di Giorgio, Benson Mwangi, Boris A. Gutman, Bronwyn J. Overs, Carlos López‐Jaramillo, Colm McDonald, Dan Joseph Stein, Dara M. Cannon, David C. Glahn, Diego Hidalgo‐Mazzei, Diliana Pecheva, Dominik Grotegerd, Edith Pomarol-Clotet, Eduard Vieta, Émilie Olié, Enric Vilajosana Chertó, Fabio Sambataro, Fleur M. Howells, Freda Scheffler, Geraldo F. Busatto, Gerard Anmella, Giovana Zunta‐Soares, Gloria Roberts, Henk Temmingh, Ian H. Gotlib, Ingrid Agartz, Jair C. Soares, James A. Karantonis, James J. Prisciandaro, Janice M. Fullerton, Joaquim Raduà, Jonathan Savitz, Josselin Houenou, Kang Sim, Kenichiro Harada, Klaus Berger, Koji Matsuo, Lakshmi N. Yatham, Lars T. Westlye, Lisa T. Eyler, Lisa S. Furlong, Anna Luisa Klahn, Marco Hermesdorf, Marcus V. Zanetti, Matthew J. Kempton, Matthew D. Sacchet, Mikael Landén, Mon‐Ju Wu, Pedro G. P. Rosa, Philip B. Mitchell, Pravesh Parekh, Raymond Salvador, Rayus Kuplicki, Salvador Sarró, Susan L. Rossell, Tamsyn E. Van Rheenen, Theodore D. Satterthwaite, Tilo Kircher, Tomáš Hájek, Udo Dannlowski, Xavier Caseras, Yuji Zhao, Ole A. Andreassen, Paul M. Thompson, Christopher R. K. Ching (2025). A Scalable Toolkit for Modeling 3D Surface-based Brain Geometry. , DOI: https://doi.org/10.1101/2025.08.06.668521.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Preprint
Year
2025
Authors
72
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1101/2025.08.06.668521
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access
