BACE: Behavior-Adaptive Connectivity Estimation from Multi-Region Neural Recordings

Understanding how distributed brain regions coordinate during behavior requires models that are both predictive and interpretable. We introduce Behavior-Adaptive Connectivity Estimation (BACE), an end-to-end framework for learning behavior-conditioned directed effective connectivity from multi-region intracranial local field potentials (LFPs). BACE first encodes within-region dynamics with region-specific temporal encoders, then applies a learned adjacency matrix selected for each behavioral context, and finally forecasts future neural activity through a graph-conditioned autoregressive decoder. This design yields explicit region-level connectivity matrices whose edges are tied to predictive dynamics rather than post-hoc explanation. On controlled synthetic time series with known directed graphs, BACE recovers the ground-truth edge structure from forecasting alone. We then evaluate BACE on human deep-brain LFP recordings from three participants performing structured motor tasks. Across participants, BACE achieves strong neural forecasting while producing compact, behavior-specific directed graphs that can be inspected across task segments. Reliability analyses further support the stability of the inferred connectivity patterns. Together, these results position BACE as a practical framework for estimating behavior-adaptive effective connectivity from high-dimensional intracranial recordings, enabling interpretable hypotheses about how deep-brain networks reorganize during behavior.