The human brain undergoes rapid development during the third trimester of pregnancy. In this work, we model the neonatal development of the infant brain in this age range. As a basis, we use MR images of preterm- and term-birth neonates from the developing human connectome project (dHCP). We propose a neural network, specifically an implicit neural representation (INR), to predict 2D- and 3D images of varying time points. In order to model a subject-specific development process, it is necessary to disentangle the age from the subjects' identity in the latent space of the INR. We propose two methods, Subject Specific Latent Vectors (SSL) and Stochastic Global Latent Augmentation (SGLA), enabling this disentanglement. We perform an analysis of the results and compare our proposed model to an age-conditioned denoising diffusion model as a baseline. We also show that our method can be applied in a memory-efficient way, which is especially important for 3D data.

翻译：人类大脑在妊娠晚期经历快速发育。本研究针对该年龄段婴儿大脑的新生期发育过程进行建模。我们以发育中人脑连接组计划（dHCP）中早产与足月新生儿的磁共振图像为基础，提出一种神经网络方法——具体采用隐式神经表征（INR）——来预测不同时间点的二维及三维图像。为建立个体特异性发育模型，需在INR的潜在空间中实现年龄特征与个体身份的分离。我们提出两种方法：主体特异性潜在向量（SSL）与随机全局潜在增强（SGLA），以实现这种特征解耦。我们对结果进行了分析，并将所提模型与以年龄为条件的去噪扩散模型作为基线进行比较。同时证明该方法能以内存高效的方式实现，这对三维数据处理尤为重要。