Distinct resting-state brain activities in heroin-dependent individuals

Previous functional imaging studies on heroin addicts have focused on abnormal brain functions based on specific tasks, while few fMRI studies concentrated on the resting-state abnormalities of heroin-dependent individuals. In the current study, we applied the pattern classification technique, which employs the feature extraction method of non-negative matrix factorization (NMF) and a support vector machine (SVM) classifier. Its main purpose was to characterize the discrepancy in activation patterns between heroin-dependent individuals and healthy subjects during the resting state. The results displayed a high accuracy in the activation pattern differences of the two groups, which included the orbitofrontal cortex (OFC), cingulate gyrus, frontal and para-limbic regions such as the anterior cingulate cortex (ACC), hippocampal/parahippocampal region, amygdala, caudate, putamen, as well as the posterior insula and thalamus. These findings indicate that significant biomarkers exist among the network of circuits that are involved in drug abuse. The implications from our study may help explain the behavioral and neuropsychological deficits in heroin-dependent individuals and shed light on the mechanisms underlying heroin addiction.

► We investigate the resting-state abnormalities of heroin-dependent individuals.
► We apply the pattern classification technique to characterize the discrepancy patterns.
► The pattern classification method uses NMF to extract features and uses SVM as a classifier.
► Significant biomarkers exist among the network of four circuits involved in drug abuse.