Spatial Statistics and Explainable Deep Learning for3D Cell-Protein Interaction Profiling

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Abstract Digital histology of human postmortem brain tissue is fundamental to understanding neurodegeneration, yet resolving thespatial interactome of neuroinflammation remains a major challenge due to the limitations of manual scoring and scalargeometric approximations. We present a hybrid computational framework that bridges the gap between mathematical rigorand morphological sensitivity by combining stochastic spatial point processes with explainable 3D deep learning. Usinghigh-resolution spinning-disk confocal immunofluorescence images from a human Parkinson’s disease cohort (Braak Stage3/4), we mapped the spatial relationships between IBA1-positive microglia and phosphorylated α-synuclein (pSyn) aggregatesacross the nigrostriatal pathway. While second-order spatial point process statistics demonstrated that global microglial spatialclustering and macro-distribution remain highly conserved between PD and healthy controls, a voxel-level Overlap Indexrevealed a significant, region-specific elevation of microglial-pSyn co-occupancy exclusively in the substantia nigra (d = 0.74,p = 0.044). To capture the underlying pixel-level transformations, we trained multi-channel 3D ResNet-18 architectures underpatient-contained nested cross-validation, achieving robust classification performance in the substantia nigra (mean AUROC= 0.74, excluding outlier fold) while confirming model integrity through a biological null control in the putamen. Furthermore,human-centred interpretability via 3D Gradient-Weighted Class Activation Mapping and Area of Relevance profiling successfullylocalized the discriminative disease signal to micro-morphological texture gradients along peripheral microglial ramificationsat pathology-rich tissue depths. By mathematically ruling out global density shifts and visually decoding the network’sdecisions, this dual-tiered framework successfully resolves the qualitative morphological transformation of microglia at theneurodegenerative locus, offering a reproducible, explainable paradigm for digital computational neuropathology.

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