Cross-Frequency Coupling as a Neural Substrate for Prediction Error Evaluation: A Laminar Neural Mass Modeling Approach
bioRxiv (Cold Spring Harbor Laboratory) March 19, 2025 Giulio Ruffini, Edmundo Lopez-Sola, Raul P. Aristides et al. 8 citations preprint
Cross-frequency coupling (CFC), where brain rhythms at different speeds interact, may be the mechanism the brain uses to compare sensory input with internal predictions. Using a laminar neural mass model, the authors show that two forms of CFC—signal-envelope coupling and envelope-envelope coupling—can implement hierarchical prediction-error computation and precision-weighting. In Alzheimer's disease, disruptions in fast-spiking interneurons lead to aberrant prediction errors: inflated early on, then attenuated. Serotonergic psychedelics reduce the influence of predictions, increasing prediction-error signals. These findings suggest that CFC across multiple timescales is a key computational mechanism supporting predictive coding, with disruptions central to certain disorders.