Book Chapter

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

Y Im, L Nabulsi, MJY Kang, SI Thomopoulos, AMD Zuluaga, AM Dale, A Karuk, A Di Giorgio, B Mwangi, B Gutman, B Overs, CL Jaramillo, C McDonald, DJ Stein, DM Cannon, D Glahn, D Hidalgo-Mazzei, D Pecheva, D Grotegerd, E Pomarol-Clotet Show all

Lecture Notes in Computer Science | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Springer Nature Switzerland | Published : 2026

DOI: 10.1007/978-3-032-06774-6_18

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).

Keywords

4607 Graphics, Augmented Reality and Games
Bioengineering
Neurosciences
46 Information and Computing Sciences
Bipolar Disorder
Brain Disorders
Mental Health
Mental Illness
Data Science
Networking and Information Technology R&d (nitrd)
Neurological