An EEG Data-Driven Neural Mass Model for Investigating Abnormal Reward Circuitry of Heroin Addicts

Heroin abuse induces plastic and adaptive changes in the brain’s reward circuitry, leading to compulsive drugseeking behavior. While neuroimaging techniques have revealed some abnormal physiological parameters, measuring key neural interactions remains challenging. This study employs a datadriven Neural Mass Model (NMM) to characterize neural dynamics within the nucleus accumbens (NAc) and identify potential biomarkers of heroin addiction. The model simulates interactions among neurons in the ventral tegmental area (VTA), nucleus accumbens (NAc), and medial prefrontal cortex (mPFC), with parameters optimized using particle swarm optimization (PSO). NAc source signals were reconstructed from electroencephalography (EEG) recordings using a 12-layer realistic head model. Model validation confirmed robust performance in capturing NAc dynamics within low-frequency bands (δ, θ, α) through time-domain, frequency-domain, and nonlinear feature analyses. Systematic parameter optimization identified four candidate biomarkers distinguishing heroin addicts from healthy controls: bidirectional synaptic connectivity between VTA and NAc (C₃, C₄), maximum firing rate of NAc neurons (e_NAc), and average synaptic gain (H_NAc). These parameters exhibited significant correlations with clinical variables including abstinence duration and heroin dosage. Mediation, sensitivity-specificity, and constraint analyses confirmed that the identified biomarkers reflect genuine neurophysiological alterations rather than artifacts of model simplification. These computational findings converge with electrophysiological, optogenetic, and pharmacological evidence, supporting the model’s neurobiological validity. This study provides a mesoscopic computational framework for understanding addiction-related neuroplasticity and informs future development of targeted neuromodulation interventions.