Rhythm Flow: Bridging the Gap Between Functional Fluctuations and Neural Spikes Through Oscillatory Synchronization

Understanding the complex relationship between spatially coarse millisecond-scale neural oscillations and fine-resolution yet slow hemodynamic fluctuations remains a fundamental challenge in multimodal neuroimaging. Although recent generative models attempt to synthesize functional magnetic resonance imaging (fMRI) from concurrent electroencephalography (EEG), current `black-box' architectures fail to model intrinsic neurovascular coupling mechanisms, yielding anatomically plausible but functionally incoherent fMRI data. To bridge this gap, we introduce Rhythm Flow, a biologically-principled generative framework in which one oscillatory-synchronization mechanism governs both the EEG encoder and the residual dynamics it drives. Our approach decomposes the generated $3D+t$ latent space into three components: a time-invariant anatomy term, a low-rank EEG residual produced by a rhythm-gated spiking encoder (named as VibeSync), and a stochastic correction flow. The correction flow uses the same oscillator-and-spiking computation (VibeSync) to capture population-level dynamics that the EEG residual leaves unexplained. To test whether the predictions depend on the specific EEG input, we swap the input EEG, which changes only the EEG residual, and check without retraining whether temporally matched EEG explains the observed fMRI better than temporally shifted or cross-subject controls. Across two simultaneously recorded EEG–fMRI cohorts, temporally matched EEG outperforms both controls, and without the correction flow, the EEG residual is positive for nine of ten held-out subjects under five-fold subject cross-validation (one-sided Wilcoxon $p=0.024$), indicating that the EEG specificity generalizes to unseen subjects. On 1,152 subjects of the Healthy Brain Network, Rhythm Flow further outperforms all compared models in both representational similarity (RSA-ROI) and voxel fidelity. Beyond empirical gains, Rhythm Flow turns EEG reliance into a quantity measured directly on a trained model, making the claim that a generative EEG-to-fMRI model has learned physiological coupling falsifiable.