Abdelaal, K.; Walder-Christensen, K.; Blount, C.; Williford, K.; Adams-Grimaldi, m.; Mague, S.; Carlson, D.; Dzirasa, K.

Opioid addiction is characterized by escalating drug use, driven in part by negative reinforcement from withdrawal, but the neural processes linking withdrawal to increased drug-taking remain poorly understood. Here, we use multisite local field potential recordings and interpretable machine learning to identify large-scale brain networks engaged by repeated opioid exposure and withdrawal. After discovering that repeated fentanyl exposure induces a progressively ramping network of widespread high beta and low gamma oscillations, we then identified a distinct brain network that selectively encodes the emergence and severity of opioid withdrawal. This network, termed EN-Withdrawal, is characterized by regional gamma oscillations and widely synchronized delta/theta oscillations. Its activity patterns predict the emergence of spontaneous and naloxone-precipitated withdrawal across multiple independent cohorts, generalizing across mice, sex, opioids, and dosing regimens, while persisting over multiple days of withdrawal. Using a novel, data-driven severity index, we find that network activity scales with individual behavioral severity without simply reflecting ongoing somatic behaviors or general aversion, suggesting that EN-Withdrawal underlies a withdrawal-induced internal state. Strikingly, network activity predicts the escalation of fentanyl self-administration on a mouse-by-mouse basis in experienced, but not drug-naive, animals. These findings reveal a neurophysiological substrate of the negative reinforcement cycle of addiction that shapes individual vulnerability.