Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/65363
Title: Reward networks in the brain as captured by connectivity measures
Author: Camara Mancha, Estela
Rodríguez Fornells, Antoni
Ye, Zheng
Münte, Thomas F.
Keywords: Aprenentatge per reforç
Conducta compulsiva
Imatges per ressonància magnètica
Reinforcement learning
Compulsive behavior
Magnetic resonance imaging
Issue Date: 2009
Publisher: Frontiers Media
Abstract: An assortment of human behaviors is thought to be driven by rewards including reinforcement learning, novelty processing, learning, decision making, economic choice, incentive motivation, and addiction. In each case the ventral tegmental area/ventral striatum (nucleus accumbens) (VTA<br>VS) system has been implicated as a key structure by functional imaging studies, mostly on the basis of standard, univariate analyses. Here we propose that standard functional magnetic resonance imaging analysis needs to be complemented by methods that take into account the differential connectivity of the VTA<br>VS system in the different behavioral contexts in order to describe reward based processes more appropriately. We fi rst consider the wider network for reward processing as it emerged from animal experimentation. Subsequently, an example for a method to assess functional connectivity is given. Finally, we illustrate the usefulness of such analyses by examples regarding reward valuation, reward expectation and the role of reward in addiction.
Note: Reproducció del document publicat a: http://dx.doi.org/ 10.3389/neuro.01.034.2009
It is part of: Frontiers in Neuroscience, 2009, vol. 3, num. 3, p. 350-362
URI: http://hdl.handle.net/2445/65363
Related resource: http://dx.doi.org/10.3389/neuro.01.034.2009
ISSN: 1662-4548
Appears in Collections:Articles publicats en revistes (Cognició, Desenvolupament i Psicologia de l'Educació)

Files in This Item:
File Description SizeFormat 
578091.pdf1.03 MBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons