TY - JOUR
T1 - A New Brain Network Construction Paradigm for Brain Disorder Via Diffusion-Based Graph Contrastive Learning
AU - Zong, Yongcheng
AU - Zuo, Qiankun
AU - Ng, Michael Kwok-Po
AU - Lei, Baiying
AU - Wang, Shuqiang
N1 - This work was supported in part by the National Key Research and Development Program of China under Grant 2023YFC2506902, in part by the National Natural Science Foundations of China under Grant 62172403, Grant 12326614, and Grant U22A2024, in part by the Distinguished Young Scholars Fund of Guangdong under Grant 2021B1515020019, in part by Hong Kong Research Grants Council (HKRGC) under Grant 17201020 and Grant 17300021, Grant HKRGC CRF C7004-21GF, Grant Joint NSFC-RGC N-HKU769/21, and Grant HLCA/P-211/24.
Publisher Copyright:
© 2024 IEEE
PY - 2024/12
Y1 - 2024/12
N2 - 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.
AB - 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.
KW - Alzheimers disease
KW - autism spectrum disorder
KW - brain network
KW - graph contrastive learning
KW - region-aware diffusion
UR - https://ieeexplore.ieee.org/document/10636067/
UR - http://www.scopus.com/inward/record.url?scp=85201308147&partnerID=8YFLogxK
U2 - 10.1109/TPAMI.2024.3442811
DO - 10.1109/TPAMI.2024.3442811
M3 - Journal article
SN - 1939-3539
VL - 46
SP - 10389
EP - 10403
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
JF - IEEE Transactions on Pattern Analysis and Machine Intelligence
IS - 12
ER -