CortexFlow: Topology-Aware Spectral Decoding with Confidence-Adaptive Diffusion for fMRI-to-Image Reconstruction

Most fMRI-to-image reconstruction methods, despite advances in generative modeling, begin by representing spatially organized cortical activity as a flat vector. This overlooks the way visual cortex actually works. Cortical activity is organized across functionally specialized regions and spread over a folded cortical surface, where neighboring locations respond continuously. Beyond that, the relative importance of low-level structure and high-level semantics varies across scenes. Natural landscapes may depend more on spatial layout and texture, whereas scenes depicting events or activities may require a stronger emphasis on object identities and their interactions. A fixed diffusion schedule and guidance strength ignore this variability. We propose CortexFlow, which builds a within-hemisphere graph over the fsaverage cortical surface, extracts its Laplacian eigenmodes, and integrates branch-specific conditional flows in these spectral coordinates to incorporate cortical topology into both the VGG structural branch and the CLIP semantic branch. Two confidence gates estimate the feature-level agreement of the respective predictions. Structural confidence adapts the diffusion start, while semantic confidence controls the strength of semantic guidance. CortexFlow achieves state-of-the-art reconstruction performance on NSD across a broad range of low- and high-level metrics, with particularly strong structure-sensitive performance in SSIM. Cortical projections of topology-induced updates further provide a model-level interpretation of the decoded representations.