IEEE Trans Med Imaging. 2022 Oct;41(10):2764-2776. doi: 10.1109/TMI.2022.3171778. Epub 2022 Sep 30.
Infancy is a critical period for the human brain development, and brain age is one of the indices for the brain development status associated with neuroimaging data. The difference between the predicted age based on neuroimaging and the chronological age can provide an important early indicator of deviation from the normal developmental trajectory. In this study, we utilize the Graph Convolutional Network (GCN) to predict the infant brain age based on resting-state fMRI data. The brain connectivity obtained from rs-fMRI can be represented as a graph with brain regions as nodes and functional connections as edges. However, since the brain connectivity is a fully connected graph with features on edges, current GCN cannot be directly used for it is a node-based method for sparse graphs. Hence, we propose an edge-based Graph Path Convolution (GPC) method, which aggregates the information from different paths and can be naturally applied on dense graphs. We refer the whole model as Brain Connectivity Graph Convolutional Networks (BC-GCN). Further, two upgraded network structures are proposed by including the residual and attention modules, referred as BC-GCN-Res and BC-GCN-SE to emphasize the information of the original data and enhance influential channels. Moreover, we design a two-stage coarse-to-fine framework, which determines the age group first and then predicts the age using group-specific BC-GCN-SE models. To avoid accumulated errors from the first stage, a cross-group training strategy is adopted for the second stage regression models. We conduct experiments on infant fMRI scans from 6 to 811 days of age. The coarse-to-fine framework shows significant improvements when being applied to several models (reducing error over 10 days). Comparing with state-of-the-art methods, our proposed model BC-GCN-SE with coarse-to-fine framework reduces the mean absolute error of the prediction from >70 days to 49.9 days. The code is now available at https://github.com/SCUT-Xinlab/BC-GCN.
婴儿期是人类大脑发育的关键时期，大脑年龄是与神经影像学数据相关的大脑发育状况的指标之一。基于神经影像学预测的年龄与实际年龄之间的差异可以为偏离正常发育轨迹提供重要的早期指标。在这项研究中，我们利用图卷积网络（GCN）基于静息态 fMRI 数据预测婴儿大脑年龄。从 rs-fMRI 获得的脑连接可以表示为一个图，其中脑区作为节点，功能连接作为边。然而，由于脑连接是一个具有边特征的全连接图，因此当前的 GCN 不能直接使用，因为它是一种基于节点的稀疏图方法。因此，我们提出了一种基于边的图路径卷积（GPC）方法，该方法可以聚合来自不同路径的信息，并可自然应用于密集图。我们将整个模型称为脑连接图卷积网络（BC-GCN）。此外，通过包含残差和注意力模块，提出了两种升级的网络结构，分别称为 BC-GCN-Res 和 BC-GCN-SE，以强调原始数据的信息并增强有影响力的通道。此外，我们设计了一个两阶段的粗到精框架，首先确定年龄组，然后使用特定于组的 BC-GCN-SE 模型预测年龄。为了避免第一阶段的累积误差，采用了跨组训练策略来训练第二阶段的回归模型。我们在 6 至 811 天龄的婴儿 fMRI 扫描上进行了实验。在应用于多个模型时，粗到精框架显示出显著的改进（减少了超过 10 天的误差）。与最先进的方法相比，我们提出的带有粗到精框架的 BC-GCN-SE 模型将预测的平均绝对误差从>70 天减少到 49.9 天。代码现在可在 https://github.com/SCUT-Xinlab/BC-GCN 上获得。
