BDEC: Brain Deep Embedded Clustering Model for Resting State fMRI Group-Level Parcellation of the Human Cerebral Cortex

Objective: To develop a robust group-level brain parcellation method using deep learning based on resting-state functional magnetic resonance imaging (rs-fMRI), aiming to release the model assumptions made by previous approaches. Methods: We proposed Brain Deep Embedded Clustering (BDEC), a deep clustering model that employs a loss function designed to maximize inter-class separation and enhance intra-class similarity, thereby promoting the formation of functionally coherent brain regions. Results: Compared to ten widely used brain parcellation methods, the BDEC model demonstrates significantly improved performance in various functional homogeneity metrics. It also showed favorable results in parcellation validity, downstream tasks, task inhomogeneity, and generalization capability. Conclusion: The BDEC model effectively captures intrinsic functional properties of the brain, supporting reliable and generalizable parcellation outcomes. Significance: BDEC provides a useful parcellation for brain network analysis and dimensionality reduction of rs-fMRI data, while also contributing to a deeper understanding of the brain's functional organization.