Naltrexone ameliorates functional network abnormalities in alcohol-dependent individuals

Laurel S. Morris, Kwangyeol Baek, Roger Tait, Rebecca Elliott, Karen D. Ersche, Remy Flechais, John McGonigle, Anna Murphy, Liam J. Nestor, Csaba Orban, Filippo Passetti, Louise Paterson, Ilan Rabiner, Laurence Reed, Dana Smith, John Suckling, Eleanor Taylor, Edward T. Bullmore, Anne Lingford-Hughes, Bill DeakinDavid J. Nutt, Barbara Sahakian, Trevor W. Robbins, Valerie Voon

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


Naltrexone, an opioid receptor antagonist, is commonly used as a relapse prevention medication in alcohol and opiate addiction, but its efficacy and the mechanisms underpinning its clinical usefulness are not well characterized. In the current study, we examined the effects of 50-mg naltrexone compared with placebo on neural network changes associated with substance dependence in 21 alcohol and 36 poly-drug-dependent individuals compared with 36 healthy volunteers. Graph theoretic and network-based statistical analysis of resting-state functional magnetic resonance imaging (MRI) data revealed that alcohol-dependent subjects had reduced functional connectivity of a dispersed network compared with both poly-drug-dependent and healthy subjects. Higher local efficiency was observed in both patient groups, indicating clustered and segregated network topology and information processing. Naltrexone normalized heightened local efficiency of the neural network in alcohol-dependent individuals, to the same levels as healthy volunteers. Naltrexone failed to have an effect on the local efficiency in abstinent poly-substance-dependent individuals. Across groups, local efficiency was associated with substance, but no alcohol exposure implicating local efficiency as a potential premorbid risk factor in alcohol use disorders that can be ameliorated by naltrexone. These findings suggest one possible mechanism for the clinical effects of naltrexone, namely, the amelioration of disrupted network topology.
Original languageEnglish
Pages (from-to)425-436
Number of pages12
JournalAddiction Biology
Issue number1
Early online date28 Feb 2017
Publication statusPublished - Jan 2018
Externally publishedYes


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