MorphSys: A branch-aware contrastive learning framework for neuron morphology graphs

Objectives. Neuron morphology plays a vital role in defining cellular identity and function, offering valuable insights for cell type classification and neurological disorder diagnosis. However, two main challenges hinder progress: the difficulty of learning meaningful representations from complex, tree-like structures, and the high cost of expert annotation for large-scale datasets. Approach. To address these challenges, we propose MorphSys, a self-supervised contrastive learning framework that complements a Branch-Aware module and a GNN-based module. We present a branch-level representation of neuron morphology by introducing an Inter-Branch Attention, which captures inter-branch relationships that are overlooked by conventional tree-graph models relying on node-level message passing. We further demonstrate the effectiveness and interpretability of branch-level knowledge in learning meaningful representations of neuron morphology. Meanwhile, our GNN-based module shows a robust ability for various GNN architectures in learning local features of neuron tree graph, where we draw from results that GatedGraphConv with SumPool yields the superior performance. Main results. Comprehensive experiments on multiple benchmark datasets indicate that MorphSys consistently outperforms existing methods in neuron cell type classification. On the BIL dataset, MorphSys achieves the KNN-Acc of 83.99%, surpassing the previous state-of-the-art by 2.99%. Ablation study is conducted to verify the efficacy of several components of MorphSys, while an in-depth discussion is performed to identify powerful approaches for branch feature extraction. Significance. These results highlight that MorphSys serves an effective tool for the representation learning of neuron morphology and morphology-driven neuronal analysis.