Decoding Beyond Hemodynamic Delay: Delay-Coupled Graph Dynamics for fMRI-to-Image Reconstruction

Reconstructing visual stimuli from functional magnetic resonance imaging (fMRI) requires extracting stimulus-related information from spatially distributed blood-oxygen-level-dependent (BOLD) responses shaped by hemodynamic delay and temporal dispersion. We propose DCG-CHI, a framework that combines delay-coupled graph representation learning with hemodynamic consistency training. Its Delay-Coupled Graph State-Space Model (DCG-SSM) embeds graph diffusion within delayed state transitions, explicitly coupling propagation scales to temporal lags and combining propagation modes through input-dependent weights. Counterfactual Hemodynamic Invariance (CHI) synthesizes two BOLD views of a shared latent trajectory through a parameterized pair of hemodynamic response kernels. Cross-view agreement, stop-gradient anchoring, and synthetic BOLD re-synthesis encourage the shared encoder to recover consistent representations. Independent semantic and structural readouts condition a pretrained Stable Diffusion 2.1 backbone through cross-attention and a trainable ControlNet branch, respectively. CHI is used only during training. Experiments on NSD and THINGS-fMRI demonstrate competitive structural fidelity and semantic consistency, with SSIM scores of 0.468 and 0.366 and CLIP identification scores of 0.963 and 0.755, respectively. Ablations and controlled comparisons support the contributions of both components, while additional tests show improved reconstruction under hemodynamic response shifts.