Deep-layer autoencoder (DAE) provides a powerful way for medical image analysis, while it remains a daunting challenge due to the limited samples but high dimension. In this paper, a DAE with sparse and graph Laplacian regularization, termed as GSDAE, is presented to identify significant differences of dynamic functional connectivity (dFC) between child and young adult groups. The proposed model incorporates prior knowledge into sparse learning, i.e., the intrinsic structural information defined by manifold in the data. In this way, the reconstruction ability of unsupervised DAE can be improved, which facilitates the extraction of most discriminative features of dFC changing with age. Results on the fMRI data from the Philadelphia Neurodevelopmental Cohort project reveal essential differences lying in the reoccurrence patterns of dFC and in the connectivity of resting state networks with increasing age, e.g., there exist different trajectories of connectivity patterns in brain functions: those associated with complex cognitive functions generally decreased, while those associated with basic visual or motor control functions usually enhanced. In addition, the brain circuitry moves from segregation to integration during brain development.

深层自动编码器 (DAE) 为医学图像分析提供了一种强大的方法，但由于样本有限但维度高，它仍然是一项艰巨的挑战。在本文中，提出了一种具有稀疏和图拉普拉斯正则化的 DAE，称为 GSDAE，用于识别儿童和青年组之间动态功能连接 (dFC) 的显着差异。所提出的模型将先验知识纳入稀疏学习，即由数据中的流形定义的内在结构信息。通过这种方式，可以提高无监督 DAE 的重建能力，这有助于提取随年龄变化的 dFC 的大多数判别特征。费城神经发育队列项目的 fMRI 数据结果揭示了 dFC 的复发模式和静息状态网络随着年龄增长的连接性的本质差异，例如，大脑功能中存在不同的连接模式轨迹：那些与复杂认知功能普遍下降，而与基本视觉或运动控制功能相关的认知功能通常增强。此外，大脑回路在大脑发育过程中从分离变为整合。而那些与基本视觉或运动控制功能相关的功能通常会增强。此外，大脑回路在大脑发育过程中从分离变为整合。而那些与基本视觉或运动控制功能相关的功能通常会增强。此外，大脑回路在大脑发育过程中从分离变为整合。