BDEC:Brain Deep Embedded Clustering model

TL;DR

This paper introduces BDEC, a novel deep learning-based model for brain parcellation using rs-fMRI data, outperforming traditional methods in functional homogeneity and generalization, with promising applications in brain network analysis.

Contribution

First application of deep learning for rs-fMRI-based brain parcellation, providing an assumption-free, data-driven approach that surpasses existing methods in performance.

Findings

BDEC outperforms nine common parcellation methods in functional homogeneity.

BDEC shows superior validity, network analysis, and task homogeneity results.

BDEC demonstrates strong generalization capability across datasets.

Abstract

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.