RAVEL: Factorizing multi-region neural communication into reusable propagation routes and dynamic recruitment

Inter-regional neural interactions can change rapidly with behavior and internal state, but such changes are mechanistically ambiguous: they may reflect changing transmission properties, or changing recruitment of otherwise stable communication mechanisms. Existing multi-region models capture delayed and time-varying interactions, but do not explicitly separate these alternatives. Motivated by parallel biological communication routes and their state-dependent engagement, we introduce RAVEL, a conditional spectral model for directed communication in multi-region local field potential (LFP) recordings. RAVEL predicts target spectral activity from a source region and factorizes the source-to-target transfer into reusable delayed propagation components, with explicit transport delays and frequency-dependent filtering, and time-varying recruitment that determines when each component is expressed. In synthetic experiments, RAVEL recovers stable propagation physics and changing recruitment when that mechanism generates the data, while a matched dynamic-delay model is favored when transmission delays genuinely change. On Allen Brain Observatory LFP recordings, propagation dictionaries are reproducible across mice, transfer across behavioral contexts with little loss in held-out predictive performance, and exhibit behavior-dependent recruitment. Locally learned transfers can also be composed to predict a withheld long-range interaction without fitting the endpoint directly. RAVEL further performs competitively on complete-target cross-region prediction against established models. Together, these results support separating relatively stable propagation structure from its dynamic recruitment as an interpretable description of multi-region neural communication.