Learning Lifespan Brain Anatomical Correspondence Via Cortical Developmental Continuity Transfer
Abstract
Identifying anatomical correspondences in the human brain throughout the lifespan is an essential prerequisite for studying brain development and aging. But given the tremendous individual variability in cortical folding patterns, the heterogeneity of different neurodevelopmental stages, and the scarce of neuroimaging data, it is difficult to infer reliable lifespan anatomical correspondence at finer scales. To solve this problem, in this work, we take the advantage of the developmental continuity of the cerebral cortex and propose a novel transfer learning strategy: the model is trained from scratch using the age group with the largest sample size, and then is transferred and adapted to the other groups following the cortical developmental trajectory. A novel loss function is designed to ensure that during the transfer process the common patterns will be extracted and preserved, while the group-specific new patterns will be captured. The proposed framework was evaluated using multiple datasets covering four lifespan age groups with 1,000+ brains (from 34 gestational weeks to young adult). Our experimental results show that: 1) the proposed transfer strategy can dramatically improve the model performance on populations (e.g., early neurodevelopment) with very limited number of training samples; and 2) with the transfer learning we are able to robustly infer the complicated many-to-many anatomical correspondences among different brains at different neurodevelopmental stages. (Code will be released soon: https://github.com/qidianzl/CDC-transfer).
Recommended Citation
L. Zhang and Z. Wu and X. Yu and Y. Lyu and Z. Wu and H. Dai and L. Zhao and L. Wang and G. Li and X. Wang and T. Liu and D. Zhu, "Learning Lifespan Brain Anatomical Correspondence Via Cortical Developmental Continuity Transfer," Medical Image Analysis, vol. 99, article no. 103328, Elsevier, Jan 2025.
The definitive version is available at https://doi.org/10.1016/j.media.2024.103328
Department(s)
Computer Science
Keywords and Phrases
Common and group-specific patterns; Developmental continuity; Lifespan correspondence; Transfer learning
International Standard Serial Number (ISSN)
1361-8423; 1361-8415
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Elsevier, All rights reserved.
Publication Date
01 Jan 2025
PubMed ID
39243599
Comments
National Institutes of Health, Grant R01MH116225