Abstract
Neuronal morphology encodes critical information about circuit function, development, and disease, yet current methods analyze topology or graph structure in isolation. We introduce GraPHFormer, a multimodal architecture that unifies these complementary views through CLIP-style contrastive learning. Our vision branch processes a novel three-channel persistence image encoding unweighted, persistence-weighted, and radius-weighted topological densities via DINOv2-ViT-S. In parallel, a TreeLSTM encoder captures geometric and radial attributes from skeleton graphs. Both project to a shared embedding space trained with symmetric InfoNCE loss, augmented by persistence-space transformations that preserve topological semantics. Evaluated on six benchmarks (BIL-6, ACT-4, JML-4, N7, M1-Cell, M1-REG) spanning self-supervised and supervised settings, GraPHFormer achieves state-of-the-art performance on five benchmarks, significantly outperforming topology-only, graph-only, and morphometrics baselines. We demonstrate practical utility by discriminating glial morphologies across cortical regions and species, and detecting signatures of developmental and degenerative processes. Code: this https URL
Abstract (translated)
神经元形态蕴含了关于神经回路功能、发育和疾病的关键信息，然而当前方法仅孤立地分析拓扑结构或图结构。我们提出了GraPHFormer，一种通过CLIP式对比学习统一这些互补视角的多模态架构。我们的视觉分支通过DINOv2-ViT-S处理一种新型的三通道持续图像，该图像编码了无权重、持续权重和半径权重的拓扑密度。同时，TreeLSTM编码器从骨架图中捕获几何和径向属性。两者均投影到一个共享的嵌入空间，该空间使用对称InfoNCE损失进行训练，并辅以保持拓扑语义的持续空间变换。在涵盖自监督和监督设置的六个基准测试（BIL-6、ACT-4、JML-4、N7、M1-Cell、M1-REG）上评估，GraPHFormer在五个基准测试中实现了最先进的性能，显著超越了仅拓扑、仅图和形态测量基线。我们通过跨皮层区域和物种区分胶质细胞形态，以及检测发育和退化过程的特征，展示了其实用价值。代码：此https URL
URL
https://arxiv.org/abs/2603.20970
