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).

中文翻译：

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通过皮质发育连续性转移学习寿命脑解剖对应


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。但鉴于皮质折叠模式的巨大个体变异性、不同神经发育阶段的异质性以及神经影像学数据的稀缺，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，提出了一种新的迁移学习策略：该模型使用样本量最大的年龄组从头开始训练，然后按照皮层发育轨迹转移并适应其他组。设计了一种新的损失函数，以确保在传输过程中提取和保留常见模式，同时捕获特定于组的新模式。使用多个数据集对拟议的框架进行了评估，该数据集涵盖四个寿命年龄组，拥有 1,000+ 个大脑（从 34 孕周到青年）。我们的实验结果表明：1） 所提出的转移策略可以显着提高模型在训练样本数量非常有限的人群（例如，早期神经发育）上的性能;2） 通过迁移学习，我们能够稳健地推断不同神经发育阶段不同大脑之间复杂的多对多解剖对应关系。（代码将很快发布：https://github.com/qidianzl/CDC-transfer）。