DCPFC dataset

 Brain parcellation provides fundamental insights into the organizational principles of the human brain. Recently, there has been an increasing interest in fMRI connectivity-based parcellation to non-invasively delineate functional brain subdivisions. However, current clustering methods struggle to fully exploit the complex and nonlinear features of the brain, compromising the reproducibility and spatial continuity of the resulting parcels. Moreover, the derived parcels are typically labeled based on anatomical axes and lack detailed functional characterization, limiting a deeper understanding of brain function. To address these issues, we propose a Deep Clustering Parcellation with Functional Characterization (DCPFC) framework and apply it to the striatal subdivisions of individuals or populations in two open-access resting-state fMRI datasets. This framework introduces a deep clustering network to extract and reconstruct nonlinear features while simultaneously performing clustering through iterative optimization. An additional spatial constraint with manifold distance is proposed to ensure the spatial continuity of parcels. Importantly, the functional characteristics of these striatal parcels are further decoded by mapping the reconstructed cluster centroids to the cerebral cortex. Compared with other prevalent connectivity-based parcellation (CBP) methods, the proposed framework demonstrated superior reproducibility and spatial continuity, as well as comparable functional homogeneity. Furthermore, the striatum is divided into 6 parcels, delineated based on somatomotor, attention (dorsal and ventral), and frontoparietal networks. Significant correlations were observed between cognitive flexibility and delay discounting with the size of these parcels, highlighting the efficacy of our framework in capturing the cognitive-related functional topography of the brain. The functionally explainable striatal subdivisions generated by this framework further enhance our understanding of the brain and provide a solid foundation for advancing clinical research.