Lecture notes in computer scienceNeurosciencePeer-reviewed journal article
A Scalable Toolkit for Modeling 3D Surface-based Brain Geometry
Yanghee ImiD, Leila NabulsiiD, Melody J.Y. KangiD, Sophia I. ThomopoulosiD, Ana M. Díaz Zuluaga, Anders Martin DaleiD, Andriana KarukiD, Annabella Di GiorgioiD, Benson MwangiiD, Boris A. GutmaniD, Bronwyn J. OversiD, Carlos López‐JaramilloiD, Colm McDonaldiD, Dan Joseph SteiniD, Dara M. CannoniD, David C. GlahniD, Diego Hidalgo‐MazzeiiD, Diliana PechevaiD, Dominik GrotegerdiD, Edith Pomarol‐ClotetiD, Eduard VietaiD, Émilie OliéiD, Enric VilajosanaiD, Fabio SambataroiD, Fleur Margaret HowellsiD, , for the ENIGMA Bipolar Disorder Working Group
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).
- organism: human
- assay: spatial transcriptomics
- research focus: dimensionality reduction
- approach: statistical model
- Citations
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- FWCI
- 0.00
- Field percentile
- 26th percentile
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