基于生物力学约束的脑发育建模，提升个体生长轨迹的生物学合理性。
NEUBORN: The Neurodevelopmental Evolution framework Using BiOmechanical RemodelliNg
- 采用分层网络实现纵向形变图像配准，融合生物力学约束。
- 在新生儿数据上训练，生成更平滑、负雅可比更少的形变场。
- 适合研究脑发育异常及早期畸形预测的科研人员。
理解个体皮层发育对识别与神经发育障碍相关的偏差至关重要。然而，现有规范建模框架因依赖群体平均参考空间，难以捕捉精细解剖细节。本文提出一种新框架，通过分层网络架构实现基于生物力学约束的纵向微分图像配准，利用发育人脑连接组计划（DHCPI）的新生儿MRI数据进行训练。该方法提升了形变场的生物学合理性，生成更符合群体趋势的生长轨迹，同时获得更平滑的形变场和更少的负雅可比值，优于当前最先进基线。所得个体特异性形变提供了可解释且基于生物学的发育映射，为脑成熟度预测和皮层发育畸形的早期识别开辟新途径。
原文摘要 · Abstract (English)
Understanding individual cortical development is essential for identifying deviations linked to neurodevelopmental disorders. However, current normative modelling frameworks struggle to capture fine-scale anatomical details due to their reliance on modelling data within a population-average reference space. Here, we present a novel framework for learning individual growth trajectories from biomechanically constrained, longitudinal, diffeomorphic image registration, implemented via a hierarchical network architecture. Trained on neonatal MRI data from the Developing Human Connectome Project, the method improves the biological plausibility of warps, generating growth trajectories that better follow population-level trends while generating smoother warps, with fewer negative Jacobians, relative to state-of-the-art baselines. The resulting subject-specific deformations provide interpretable, biologically grounded mappings of development. This framework opens new possibilities for predictive modeling of brain maturation and early identification of malformations of cortical development.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。