Hu Dan, Yin Weiyan, Wu Zhengwang, Chen Liangjun, Wang Li, Lin Weili, Li Gang
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
Med Image Comput Comput Assist Interv. 2021 Sep-Oct;12903:231-240. doi: 10.1007/978-3-030-87199-4_22. Epub 2021 Sep 21.
The difficulty of acquiring resting-state fMRI of early developing children under the same condition leads to a dedicated protocol, i.e., scanning younger infants during sleep and older children during being awake, respectively. However, the obviously different brain activities of sleep and awake states arouse a new challenge of awake-to-sleep connectome prediction/translation, which remains unexplored despite its importance in the longitudinally-consistent delineation of brain functional development. Due to the data scarcity and huge differences between natural images and geometric data (e.g., brain connectome), existing methods tailored for image translation generally fail in predicting functional connectome from awake to sleep. To fill this critical gap, we unprecedentedly propose a novel reference-relation guided autoencoder with deep CCA restriction (RAE-dCCA) for awake-to-sleep connectome prediction. Specifically, 1) A reference-autoencoder (RAE) is proposed to realize a guided generation from the source domain to the target domain. The limited paired data are thus greatly augmented by including the combinations of all the age-restricted neighboring subjects as the references, while the target-specific pattern is fully learned; 2) A relation network is then designed and embedded into RAE, which utilizes the similarity in the source domain to determine the belief-strength of the reference during prediction; 3) To ensure that the learned relation in the source domain can effectively guide the generation in the target domain, a deep CCA restriction is further employed to maintain the neighboring relation during translation; 4) New validation metrics dedicated for connectome prediction are also proposed. Experimental results showed that our proposed RAE-dCCA produces better prediction accuracy and well maintains the modular structure of brain functional connectome in comparison with state-of-the-art methods.
在相同条件下获取早期发育儿童静息态功能磁共振成像（fMRI）存在困难，这导致了一种专门的方案，即分别在睡眠期间扫描年幼婴儿，在清醒期间扫描年长儿童。然而，睡眠和清醒状态下明显不同的大脑活动引发了从清醒到睡眠的连接组预测/转换这一新挑战，尽管其在大脑功能发育的纵向一致描绘中很重要，但仍未得到探索。由于数据稀缺以及自然图像和几何数据（如脑连接组）之间存在巨大差异，现有的针对图像转换的方法通常无法从清醒状态预测到睡眠状态的功能连接组。为了填补这一关键空白，我们前所未有地提出了一种具有深度典型相关分析（CCA）限制的新型参考关系引导自动编码器（RAE-dCCA）用于从清醒到睡眠的连接组预测。具体而言，1）提出了一种参考自动编码器（RAE），以实现从源域到目标域的引导生成。通过将所有年龄受限的相邻受试者的组合作为参考，有限的配对数据因此得到了极大扩充，同时充分学习了目标特定模式；2）然后设计并将一个关系网络嵌入到RAE中，该网络利用源域中的相似性来确定预测期间参考的置信强度；3）为确保在源域中学习到的关系能够有效地指导目标域中的生成，进一步采用深度CCA限制以在转换过程中保持相邻关系；4）还提出了专门用于连接组预测的新验证指标。实验结果表明，与现有方法相比，我们提出的RAE-dCCA具有更高的预测准确率，并且能很好地保持脑功能连接组的模块化结构。
