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A Scalable Toolkit for Modeling 3D Surface-based Brain Geometry

Im, Y.; Nabulsi, L.; Kang, M. J. Y.; Thomopoulos, S. I.; Zuluaga, A. M. D.; Dale, A. M.; Karuk, A.; Giorgio, A. D.; Mwangi, B.; Gutman, B.; Overs, B.; Jaramillo, C. L.; McDonald, C.; Stein, D.; Cannon, D. M.; Glahn, D.; Hidalgo-Mazzei, D.; Pecheva, D.; Grotegerd, D.; Pomarol-Clotet, E.; Vieta, E.; Olie, E.; Cherto, E. V.; Sambataro, F.; Howells, F.; Scheffler, F.; Busatto, G.; Anmella, G.; Zunta-Soares, G. B.; Roberts, G.; Temmingh, H.; Gotlib, I.; Agartz, I.; Soares, J. C.; Karantonis, J. A.; Prisciandaro, J.; Fullerton, J. M.; Radua, J.; Savitz, J.; Houenou, J.; Sim, K.; Harada, K.; Berger,

2025-08-09 neuroscience
10.1101/2025.08.06.668521 bioRxiv
Show abstract

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).

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.