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: ).

中文翻译：

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通过皮层发育连续性转移学习生命周期大脑解剖学对应


识别人脑在整个生命周期中的解剖对应关系是研究大脑发育和衰老的重要先决条件。但考虑到皮质折叠模式存在巨大的个体差异、不同神经发育阶段的异质性以及神经影像数据的稀缺，很难在更精细的尺度上推断出可靠的寿命解剖学对应关系。为了解决这个问题，在这项工作中，我们利用大脑皮层的发育连续性，提出了一种新颖的迁移学习策略：使用样本量最大的年龄组从头开始训练模型，然后进行迁移和学习适应遵循皮质发育轨迹的其他群体。设计了一种新颖的损失函数，以确保在传输过程中提取并保留共同模式，同时捕获特定于组的新模式。使用多个数据集对所提出的框架进行了评估，涵盖四个寿命年龄组，拥有 1,000 多个大脑（从妊娠 34 周到年轻人）。我们的实验结果表明：1）所提出的迁移策略可以在训练样本数量非常有限的人群（例如早期神经发育）上显着提高模型性能； 2）通过迁移学习，我们能够稳健地推断不同神经发育阶段的不同大脑之间复杂的多对多解剖学对应关系。 （代码即将发布：）。