  An essential premise for neuroscience brain network analysis is the
successful segmentation of the cerebral cortex into functionally homogeneous
regions. Resting-state functional magnetic resonance imaging (rs-fMRI),
capturing the spontaneous activities of the brain, provides the potential for
cortical parcellation. Previous parcellation methods can be roughly categorized
into three groups, mainly employing either local gradient, global similarity,
or a combination of both. The traditional clustering algorithms, such as
"K-means" and "Spectral clustering" may affect the reproducibility or the
biological interpretation of parcellations; The region growing-based methods
influence the expression of functional homogeneity in the brain at a large
scale; The parcellation method based on probabilistic graph models inevitably
introduce model assumption biases. In this work, we develop an assumption-free
model called as BDEC, which leverages the robust data fitting capability of
deep learning. To the best of our knowledge, this is the first study that uses
deep learning algorithm for rs-fMRI-based parcellation. By comparing with nine
commonly used brain parcellation methods, the BDEC model demonstrates
significantly superior performance in various functional homogeneity
indicators. Furthermore, it exhibits favorable results in terms of validity,
network analysis, task homogeneity, and generalization capability. These
results suggest that the BDEC parcellation captures the functional
characteristics of the brain and holds promise for future voxel-wise brain
network analysis in the dimensionality reduction of fMRI data.
