Consolidating the Circuit Model for Addiction

Author:

Lüscher Christian12,Janak Patricia H.34

Affiliation:

1. Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, CH-1211 Geneva, Switzerland;

2. Clinic of Neurology, Department of Clinical Neurosciences, Geneva University Hospital, CH-1211 Geneva, Switzerland

3. Department of Psychological and Brain Sciences, Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, Maryland 21218, USA

4. The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, Maryland 21205, USA

Abstract

Addiction is a disease characterized by compulsive drug seeking and consumption observed in 20–30% of users. An addicted individual will favor drug reward over natural rewards, despite major negative consequences. Mechanistic research on rodents modeling core components of the disease has identified altered synaptic transmission as the functional substrate of pathological behavior. While the initial version of a circuit model for addiction focused on early drug adaptive behaviors observed in all individuals, it fell short of accounting for the stochastic nature of the transition to compulsion. The model builds on the initial pharmacological effect common to all addictive drugs—an increase in dopamine levels in the mesolimbic system. Here, we consolidate this early model by integrating circuits underlying compulsion and negative reinforcement. We discuss the genetic and epigenetic correlates of individual vulnerability. Many recent data converge on a gain-of-function explanation for circuit remodeling, revealing blueprints for novel addiction therapies.

Publisher

Annual Reviews

Subject

General Neuroscience

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3