{"ID":2857222,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.20831","arxiv_id":"2510.20831","title":"BACE: Behavior-Adaptive Connectivity Estimation for Interpretable Graphs of Neural Dynamics","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 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.","short_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 intrac...","url_abs":"https://arxiv.org/abs/2510.20831","url_pdf":"https://arxiv.org/pdf/2510.20831v1","authors":"[\"Mehrnaz Asadi\",\"Sina Javadzadeh\",\"Rahil Soroushmojdehi\",\"S. Alireza Seyyed Mousavi\",\"Terence D. Sanger\"]","published":"2025-10-11T22:48:36Z","proceeding":"q-bio.NC","tasks":"[\"q-bio.NC\",\"cs.LG\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}