GDAIP: A Graph-Based Domain Adaptive Framework for Individual Brain Parcellation
This work addresses domain adaptation for brain parcellation in neuroscience, but it is incremental as it combines existing techniques like GAT and MME for a specific application.
The paper tackled the problem of individual brain parcellation from fMRI data under cross-dataset domain shifts by proposing GDAIP, a graph-based domain adaptation framework, which achieved topologically plausible boundaries and strong cross-session consistency in experiments.
Recent deep learning approaches have shown promise in learning such individual brain parcellations from functional magnetic resonance imaging (fMRI). However, most existing methods assume consistent data distributions across domains and struggle with domain shifts inherent to real-world cross-dataset scenarios. To address this challenge, we proposed Graph Domain Adaptation for Individual Parcellation (GDAIP), a novel framework that integrates Graph Attention Networks (GAT) with Minimax Entropy (MME)-based domain adaptation. We construct cross-dataset brain graphs at both the group and individual levels. By leveraging semi-supervised training and adversarial optimization of the prediction entropy on unlabeled vertices from target brain graph, the reference atlas is adapted from the group-level brain graph to the individual brain graph, enabling individual parcellation under cross-dataset settings. We evaluated our method using parcellation visualization, Dice coefficient, and functional homogeneity. Experimental results demonstrate that GDAIP produces individual parcellations with topologically plausible boundaries, strong cross-session consistency, and ability of reflecting functional organization.