Electrophysiology and Functional Magnetic Resonance Imaging of Cue Craving: Potential Biomarkers for Therapeutic Neuromodulation in Addiction

Comprehensive Summary

Substance use disorder (SUD) is a major public health problem, with pathological drug craving serving as a central driver of continued use and relapse. To better understand the neural mechanisms underlying craving, this review focuses on the role of the nucleus accumbens (NAc), a key hub within the brain’s reward circuitry, and examines how local field potentials (LFPs) recorded from this region relate to craving states. Tanabe et al. synthesize evidence on potential neural biomarkers of craving by reviewing electrophysiological and functional magnetic resonance imaging (fMRI) studies in both animal models and humans. These studies, such as LFP recordings, single-unit activity, and cue-reactivity fMRI, are interpreted using the triple network model, which integrates reward, salience, and executive control networks. Across studies, craving states were consistently associated with increased low-frequency neural oscillations, particularly delta (1–4 Hz) and theta (4–8 Hz) band activity within the NAc. The reviewed data suggest that elevations in NAc delta/theta power correlate with self-reported craving and may precede loss-of-control behaviors. Overall, Tanabe et al. conclude that further research is needed to validate these oscillatory signals as reliable biomarkers, in order to improve and personalize treatments for SUD.

Outcomes and Implications

Craving is a strong predictor of relapse in individuals with SUD, making the identification of objective neurobiomarkers a critical clinical goal. This review highlights emerging approaches to quantify craving through neural signals, which could move assessment beyond subjective self-report. Tanabe et al. emphasize the need for additional validation studies to determine the feasibility of translating these biomarkers into clinical practice. By clarifying which brain networks and nodes are most closely linked to craving, this work supports the development of neuromodulation strategies and closed-loop systems capable of detecting and responding to high-craving neural states in real time.