Frequency-filtered attention for cross-species fMRI time series prediction under small-sample learning

Functional magnetic resonance imaging (fMRI) time series exhibit long-term temporal dependencies and stable functional connectivity (FC) structures. However, most existing prediction models mainly focus on the temporal domain, making it difficult to jointly capture spectral characteristics and neurobiological priors. We propose a general fMRI sequence prediction model, the Frequency-Filtered Attention Transformer (FFAformer). It models low-frequency variations in the frequency domain to capture long-range dependencies and incorporates FC consistency constraints to preserve brain network structure. In addition, FFAformer introduces a trainable symmetric positive definite full-rank matrix into the attention mechanism to alleviate representation degradation under small-sample learning. The predicted fMRI time series preserve low-dimensional brain activity patterns and FC consistent with real data. Experiments on small-sample cross-species fMRI datasets (mice, macaques, and humans) demonstrate lower prediction errors, higher FC consistency, and robust cross-species generalization, supporting reliable fMRI sequence prediction.