fMRI observation on impact of protracted abstinence on large brain networks in heroin addicts on craving tasks / 中国医学影像技术
Chinese Journal of Medical Imaging Technology
; (12): 1169-1174, 2019.
Article
en Zh
| WPRIM
| ID: wpr-861267
Biblioteca responsable:
WPRO
ABSTRACT
Objective: To investigate the impact of protracted abstinence on heroin addicts' psychological craving and brain network function under drug cue task. Methods: Totally 37 heroin addicts (protracted abstinence group) and 32 matched normal volunteers (control group) were recruited. Resting state and craving task fMRI data were acquired, and the psychological craving quantitative scoring was evaluated. Resting fMRI data were analyzed to obtain task negative correlation networks (TNN) and task positive correlation networks (TPN). After modeling task-state fMRI data and comparing with the control group, the abnormal activated brain areas in TNN and TPN in the protracted abstinence group were obtained, and the correlation with psychologic behavior was analyzed. Results: The craving scores of the protracted abstinence group were significantly higher than those of control group before and after the presentation of task cues (all P<0.01). Under the craving task, compared with control group, the right para-hippocampal gyrus was significantly activated in the protracted abstinence group in TNN, and the significantly enhanced networks in TPN included visual spatial network (bilateral anterior central gyrus and inferior frontal gyrus) and sensorimotor network (left posterior central gyrus). The activation intensity of bilateral inferior frontal gyrus in protracted abstinence group was negatively correlated with the duration of heroin use (right: r=-0.37, P=0.02; left: r=-0.41, P=0.01). Conclusion: The subjective craving of heroin addicts who are forced to quit is still increased, and the abnormal function of multiple large brain networks has not completely recovered, which may be the neuropathological basis of relapse.
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Índice:
WPRIM
Tipo de estudio:
Prognostic_studies
Idioma:
Zh
Revista:
Chinese Journal of Medical Imaging Technology
Año:
2019
Tipo del documento:
Article