SABER: Subject-Adaptive and Boundary-Aware Deep Multi-modal Clustering Network for Infant Group-Level Cortical Parcellation

Constructing a group-level multi-modal cortical parcellation map for infants during the first two postnatal years is essential for studying early brain development and identifying disease risks from structural and functional perspectives. However, infant data are noisy and high-dimensional, making machine learning prone to fragmented and unstable parcellations, which motivates the use of deep learning. Yet, deep learning remains underexplored for this task because jointly modeling associative and complementary structural-functional information is challenging, while existing unsupervised learning objectives insufficiently address group-level parcellation practical demands, including improving cross-subject adaptability and anatomical consistency while preserving spatial continuity. To this end, we propose SABER, the first Subject-Adaptive and Boundary-aware DEep multi-modal clusteRing network for infant group-level cortical parcellation. SABER employs a two-stage fusion strategy combining multi-layer graph mutual learning with vertex-level modality weighting to capture structural-functional associations and complementarity. It further introduces two tailored objectives: the Dynamic Spatial-Weighted Generalized Homogeneity Loss improves cross-subject adaptability by optimizing parcellation homogeneity over a large set of individual-level features, while incorporating dynamic spatial weighting mechanism to prevent fragmentation; the Local Gradient-Weighted Active Boundary Loss incorporates local gradient-based boundary evidence to enhance sensitivity to potential biological boundaries. Experiments on the BCP dataset show that SABER outperforms existing SOTA methods and published parcellations across evaluation metrics and downstream tasks. Spatial null model analysis further indicates that the resulting parcellations capture biologically meaningful organization rather than random spatial patterns. Ablation studies demonstrate the plug-and-play ability of both losses and show that improved cross-subject adaptability benefits downstream task performance. https://anonymous.4open.science/r/SABER2027.