Predicting Functional Brain Connectivity with a Context-Aware Spatiomolecular Transformer
Alexander Ratzan, Sidharth Goel, Junhao Wen, Christos Davatzikos, Erdem Varol
Spatial location and molecular interactions have long been linked to the connectivity patterns of neural circuits. Yet, at the macroscale of human brain networks, the interplay between spatial position, gene expression, and connectivity remains poorly understood. Recent efforts to map the human transcriptome and connectome have yielded spatially resolved brain atlases, however predicting connectivity from high-dimensional transcriptomic data while accounting for inherent spatial confounds presents a significant challenge. This paper introduces the Spatiomolecular Transformer (SMT), a new architecture designed to predict whole-brain functional connectivity solely from gene expression and regional spatial coordinates. SMT explicitly models biological context by tokenizing genes based on their transcription start site (TSS) order to capture local genomic organization, and incorporating regional 3D spatial location via a dedicated context [CLS] token within its multi-head self-attention mechanism. We rigorously benchmark SMT against established linear and non-linear baselines. Crucially, we employ custom-generated spatiomolecular null maps, which preserve both spatial and transcriptomic autocorrelation, to ensure that learned relationships are not mere artefacts of spatial proximity. Across multiple connectomics datasets (UKBiobank and MPI-LEMON) and parcellation resolutions, SMT demonstrates competitive predictive performance, particularly for long-range connections, and its predictions robustly exceed the expected contributions from spatial autocorrelation. Beyond prediction, SMT provides enhanced interpretability of gene-set contributions to functional brain architecture. Our findings suggest that the complex, multimodal patterns of functional brain networks have significant spatiomolecular foundations beyond chance. Code to reproduce our results can be found in the anonymous repository at [https://anonymous.4open.science/r/SMT-C56C/].