Largely overlapping neuronal substrates of reactivity to drug, gambling, food and sexual cues: A comprehensive meta-analysis
Introduction
Craving is a critical and ambiguous psychiatric term that in its core refers to a subjective state of urge or desire to gain pleasure (positive reinforcement) or to terminate distress (negative reinforcement) from drug use or other activities (Wise, 1988). This phenomenon is often associated with neurophysiological response patterns to an acute presentation of stimuli also referred to as “cue reactivity” (Drummond, 2000, Sinha, 2013).
Despite the numerous issues involved in the conceptualization of craving (Wise, 1988, Sinha, 2013), the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) now includes craving as a diagnostic criteria for alcohol and substance use disorders (AUD and SUDs) (Cherpitel et al., 2010). However, craving remains to be an ill-defined term, especially if it comes to the underlying neurobiolological mechanisms. As said neural cue reactivity captures at least one dimension of craving and as such a quantitative and comprehensive evaluation of the neurobiological substrates of cue reactivity across all drug and natural rewards may provide a better understanding of the neurobiological underpinnings of craving. In addition comprehensive meta-analyses of cue reactivity across all drug and natural rewards will also provide a reliable framework to define cue-mediated craving by objective measures and dimensions. Studies concerning with cue reactivity paradigms commonly assume that the experiments reflect cue-mediated craving and assume that drug, sexual desire and food may share common neural pathways (Childress et al., 1999, Garavan et al., 2000).
In the past two decades, a growing number of studies have utilized functional neuroimaging techniques such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and single-photon emission computed tomography (SPECT) to characterize the cue-induced neural response patterns in healthy and addicted individuals. While numerous meta-analyses have restrictively investigated the activation patterns and influencing factors for specific stimulus types (Kuhn and Gallinat, 2011a, Engelmann et al., 2012, Heckman et al., 2013, Schacht et al., 2013, Wray et al., 2013, Hanlon et al., 2014, Chase et al., 2011) and occasionally across different cues (Kuhn and Gallinat, 2011b, Tang et al., 2012), no attempts have been made yet to provide a robust and comprehensive characterization of the specific features of drug- and natural cues in order to identify the universal substrates of cue reactivity in general. Such a global analysis is critical to evaluate whether cue reactivity constitutes an appropriate and one-dimensional indicator of AUD and SUDs (and gambling). Furthermore it enables us to ultimately construct quantitative approaches towards the ambiguous term craving based on neurobiological measures.
Therefore, the aim of the present study is to conduct validated and comprehensive meta-analyses following MOOSE (Meta-analysis Of Observational Studies in Epidemiology) guidelines (Stroup et al., 2000) to assess the common and distinct neural activation patterns across all previously published functional neuroimaging studies of drug (alcohol, cocaine, nicotine, heroin and cannabis), natural (sexual, food), and gambling cue reactivity using the activation likelihood estimation (ALE) approach (Turkeltaub et al., 2002). This approach provides weighted averages of stereotactic coordinates of clusters with respect to each particular cue. In addition, we performed a two-step clustering technique to identify brain regions that either directly overlap or converge for different stimulus types and to determine group differences in activation patterns between drug-induced and natural cue reactivity.
To evaluate the methodological quality of the primary research underlying the meta-analyses and minimize the biases (Laird et al., 2005), an 83-item checklist (Poldrack et al., 2008) was used. Furthermore, sensitivity analyses were performed to estimate the level of robustness of the meta-analyses with respect to demographic and experimental parameters.
Section snippets
Search strategy
The online portal of the National Library of Medicine (http://www.ncbi.nlm.nih.gov/pubmed/) including PubMed, PubMed Central and MEDLINE was used as the platform for literature research. A systematic screening of the original research articles published until March 2015 was performed based on the keywords: alcohol (OR) Ethanol (OR) cannabis (OR) THC (OR) joint (OR) cocaine (OR) crack (OR) amphetamine (OR) methamphetamine (OR) nicotine (OR) smoking (OR) smoke (OR) tobacco (OR) cigarettes (OR)
Individual study and overall estimates
Searches of the electronic databases identified 5102 unique abstracts, titles, or both identified as original publications. The gray literature search (i.e. searching the reference sections of identified papers, reviews and meta-analyses) identified 48 additional articles. 768 research articles proved potentially relevant for full-text review and 176 articles (providing data from 5573 individuals) were selected for the meta-analysis based on the inclusion criteria. A flow diagram of the study
Discussions
The present study provides to our knowledge the first meta-analytic evidence on the largely overlapping but also distinct neural basis of cue reactivity to drug, natural and gambling stimuli. In particular, independent of the cue type or paradigm, exposure to rewarding conditioned stimuli alters the activity of a brain network that significantly overlaps with neurocircuitries associated with reward processing (liking (hedonic) and wanting (incentive salience)) (Berridge and Robinson, 2003,
Author disclosure
Funding for this study was provided by the Bundesministerium fu¨r Bildung und Forschung (e:Med program; FKZ: 01ZX1311A, Spanagel et al., 2013 and FKZ: 01ZX1503), by the European Commission Horizon2020 (668863-SyBil-AA), by the Deutsche Forschungsgemeinschaft (DFG): Reinhart-Koselleck Award SP 383/5-1, and by the ERANET COCADDICT grant. None of the funding agencies had any further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in
Role of the funding source
Funding by Bundesministerium fu¨r Bildung und Forschung (e:Med program; FKZ: 01ZX1311A and FKZ: 01ZX1503), by the European Commission Horizon2020 (668863-SyBil-AA), by the Deutsche Forschungsgemeinschaft (DFG): Reinhart-Koselleck Award SP 383/5-1, and by the ERANET COCADDICT grant were used for the salaray of HRN and ACL as well as for obtaining the necessary computational resources.
Contributors
HRN and RS designed the study. HRN, RS and ACL wrote the paper. HRN and ACL conducted the analysis.
Conflict of interest
The authors declare no conflict of interest, financial or otherwise.
Acknowledgments
This work was supported by the Bundesministerium fu¨r Bildung und Forschung (e:Med program; FKZ: 01ZX1311A and FKZ: 01ZX1503), by the European Commission Horizon2020 (668863-SyBil-AA), by the Deutsche Forschungsgemeinschaft (DFG): Reinhart-Koselleck Award SP 383/5-1, and by the ERANET COCADDICT grant.
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