  Brain network analysis plays an increasingly important role in studying brain
function and the exploring of disease mechanisms. However, existing brain
network construction tools have some limitations, including dependency on
empirical users, weak consistency in repeated experiments and time-consuming
processes. In this work, a diffusion-based brain network pipeline, DGCL is
designed for end-to-end construction of brain networks. Initially, the brain
region-aware module (BRAM) precisely determines the spatial locations of brain
regions by the diffusion process, avoiding subjective parameter selection.
Subsequently, DGCL employs graph contrastive learning to optimize brain
connections by eliminating individual differences in redundant connections
unrelated to diseases, thereby enhancing the consistency of brain networks
within the same group. Finally, the node-graph contrastive loss and
classification loss jointly constrain the learning process of the model to
obtain the reconstructed brain network, which is then used to analyze important
brain connections. Validation on two datasets, ADNI and ABIDE, demonstrates
that DGCL surpasses traditional methods and other deep learning models in
predicting disease development stages. Significantly, the proposed model
improves the efficiency and generalization of brain network construction. In
summary, the proposed DGCL can be served as a universal brain network
construction scheme, which can effectively identify important brain connections
through generative paradigms and has the potential to provide disease
interpretability support for neuroscience research.
