Title: BACE: Behavior-Adaptive Connectivity Estimation for Interpretable Graphs of Neural Dynamics
Primary area: applications to neuroscience & cognitive science
Keywords: Effective Connectivity -  Neural Forecasting  - Graph Neural Networks

Abstract: Understanding how distributed brain regions coordinate to produce behavior requires
models that are both predictive and interpretable. We introduce Behavior-
Adaptive Connectivity Estimation (BACE), an end-to-end framework that learns
phase-specific, directed inter-regional connectivity directly from multi-region intracranial
local field potentials (LFP). BACE aggregates many micro-contacts
within each anatomical region via per-region temporal encoders, applies a learnable
adjacency specific to each behavioral phase, and is trained on a forecasting
objective. On synthetic multivariate time series with known graphs, BACE accurately
recovers ground-truth directed interactions while achieving forecasting
performance comparable to state-of-the-art baselines. Applied to human subcortical
LFP recorded simultaneously from eight regions during a cued reaching task,
BACE yields an explicit 8×8 connectivity matrix for each within-trial behavioral
phase. The resulting behavioral phase-specific graphs reveal behavior-aligned reconfiguration
of inter-regional influence and provide compact, interpretable adjacency
matrices for comparing network organization across behavioral phases. By
linking predictive success to explicit connectivity estimates, BACE offers a practical
tool for generating data-driven hypotheses about the dynamic coordination
of subcortical regions during behavior.