Figure 1.

Schematic representation of the proposed convolutional neural network (CNN) architecture, where each colored square represents a specific network layer. Brain connectome matrix, ${\overline{S}}_l$ of the i^th patient is entered at the first stage, which is composed of two types of layers: convolution layer and batch normalization (BN) layer. The response of this stage is passed through a rectified linear unit (ReLU) layer. Then, the maximums of local patches are extracted by a max pool layer. Four blocks of convolution, BN, and ReLU layers are applied to learn high-level fine features from low-level features of the connectome matrix ${\overline{S}}_l$ (i.e., response of the first stage). For each residual unit, its input is added to the output before the ReLU layer. The basic idea is that, rather than expecting blocks to approximate the regression relationship, we explicitly let these layers approximate a residual function, which is easier to be optimized. Finally, fully connected and regression layers are induced to get the predicted score y_i. An average pooling layer is also applied to help prevent overfitting.