Contrastive voxel clustering for multiscale modeling of brain network

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2024-08, Vol.297, p.120755, Article 120755
• Main Authors: Ding, Zhiyuan, Huang, Yulang, Zeng, Xiangzhu, Jiang, Shiyin, Feng, Shuyang, Wang, Zhenduo, Wang, Ling, Wang, Zeng, Xu, Yingying, Liu, Yan
• Format: Article
• Language: English
• Subjects: Blood levels; Brain; Brain - diagnostic imaging; Brain - physiology; Brain mapping; Brain Mapping - methods; Cluster Analysis; Connectome - methods; Contrastive clustering; Functional connectivity; Functional magnetic resonance imaging; Functional MRI (fMRI); Graph neural network (GNN); Humans; Image processing; Image Processing, Computer-Assisted - methods; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Models, Neurological; Nerve Net - diagnostic imaging; Nerve Net - physiology; Neural networks; Neural Networks, Computer; Neuroimaging; Reactive oxygen species; Structure-function relationships
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2024.120755

Resting-state functional magnetic resonance imaging (fMRI) provides an efficient way to analyze the functional connectivity between brain regions. A comprehensive understanding of brain functionality requires a unified description of multi-scale layers of neural structure. However, existing brain network modeling methods often simplify this property by averaging Blood oxygen level dependent (BOLD) signals at the brain region level for fMRI-based analysis with the assumption that BOLD signals are homogeneous within each brain region, which ignores the heterogeneity of voxels within each Region of Interest (ROI). This study introduces a novel multi-stage self-supervised learning framework for multiscale brain network analysis, which effectively delineates brain functionality from voxel to ROIs and up to sample level. A Contrastive Voxel Clustering (CVC) module is proposed to simultaneously learn the voxel-level features and clustering assignments, which ensures the retention of informative clustering features at the finest voxel-level and concurrently preserves functional connectivity characteristics. Additionally, based on the extracted features and clustering assignments at the voxel level by CVC, a Brain ROI-based Graph Neural Network (BR-GNN) is built to extract functional connectivity features at the brain ROI-level and used for sample-level prediction, which integrates the functional clustering maps with the pre-established structural ROI maps and creates a more comprehensive and effective analytical tool. Experiments are performed on two datasets, which illustrate the effectiveness and generalization ability of the proposed method by analyzing voxel-level clustering results and brain ROIs-level functional characteristics. The proposed method provides a multiscale modeling framework for brain functional connectivity analysis, which will be further used for other brain disease identification. Code is available at https://github.com/yanliugroup/fmri-cvc. •The proposed method unravels the complexity of brain networks from voxels to regions and across samples.•The voxel-level BOLD signals are used to extract voxel-level clustering features and retain functional connectivity characteristics.•The multiscale modeling offers new insights into neurological diseases like Alzheimer’s.