Neurobiological correlates of cue-reactivity in alcohol-use disorders: A voxel-wise meta-analysis of fMRI studies

Zeng, Jianguang; Yu, Shuxian; Cao, Hengyi; Su, Yueyue; Dong, Zaiquan; Yang, Xun

doi:10.1016/j.neubiorev.2021.06.031

Cited by 56 publications

(81 citation statements)

References 140 publications

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“…However, insula and temporal gyri suffer from gray matter reduction, among many other structures, linked to AUD (Yang et al, 2016;Rolland et al, 2020). Continuing with fMRI data, taskbased studies found important effects of AUD in the insula cortex and the temporal gyrus including hyperconnectivity as a reaction to alcohol stimuli (Strosche et al, 2021), abnormal neural activity (Karch et al, 2015), abnormal brain function in verbal working memory (Park et al, 2011), and indications of neurobiological correlates to cue-reactivity (Zeng et al, 2021). Comparative observations from task-based studies mentioned in this paragraph, previous AUD classification reports (Zhu et al, 2018;Fede et al, 2019;Kamarajan et al, 2020), and those in the current work suggests that insula and temporal gyrus fMRI abnormalities linked to AUD are more prominent during exteroceptive brain function of task execution.…”

Section: Discussionmentioning

confidence: 96%

Identifying Alcohol Use Disorder With Resting State Functional Magnetic Resonance Imaging Data: A Comparison Among Machine Learning Classifiers

2022

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Alcohol use disorder (AUD) is a burden to society creating social and health problems. Detection of AUD and its effects on the brain are difficult to assess. This problem is enhanced by the comorbid use of other substances such as nicotine that has been present in previous studies. Recent machine learning algorithms have raised the attention of researchers as a useful tool in studying and detecting AUD. This work uses AUD and controls samples free of any other substance use to assess the performance of a set of commonly used machine learning classifiers detecting AUD from resting state functional network connectivity (rsFNC) derived from independent component analysis. The cohort used included 51 alcohol dependent subjects and 51 control subjects. Despite alcohol, none of the 102 subjects reported use of nicotine, cannabis or any other dependence or habit formation substance. Classification features consisted of whole brain rsFNC estimates undergoing a feature selection process using a random forest approach. Features were then fed to 10 different machine learning classifiers to be evaluated based on their classification performance. A neural network classifier showed the highest performance with an area under the curve (AUC) of 0.79. Other good performers with similar AUC scores were logistic regression, nearest neighbor, and support vector machine classifiers. The worst results were obtained with Gaussian process and quadratic discriminant analysis. The feature selection outcome pointed to functional connections between visual, sensorimotor, executive control, reward, and salience networks as the most relevant for classification. We conclude that AUD can be identified using machine learning classifiers in the absence of nicotine comorbidity.

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Section: Discussionmentioning

confidence: 96%

Identifying Alcohol Use Disorder With Resting State Functional Magnetic Resonance Imaging Data: A Comparison Among Machine Learning Classifiers

2022

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“…As a whole, studies focusing on self-reported craving indicate that craving is a meaningful clinical construct. With some exceptions-such as studies of attentional bias and response inhibition (reviewed below), as well as those using electrophysiological (e.g., Herrmann et al, 2001;Martins et al, 2019;Petit et al, 2013) and brain imaging methods (see Schacht et al, 2013 andZeng et al, 2021 for meta-analyses)-however, more work is needed to understand why craving is clinically significant. In other words, what happens when one is craving that may lead to alcohol use or relapse?…”

Section: Value Of Conceptualizing Craving As a Clinical Constructmentioning

confidence: 99%

How laboratory studies of cigarette craving can inform the experimental alcohol craving literature

Creswell

Sayette

2022

Alcoholism Clin & Exp Res

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Interest in alcohol and other drug craving has flourished over the past two decades, and evidence has accumulated showing that craving can be meaningfully linked to both drug use and relapse. Considerable human experimental alcohol craving research since 2000 has focused on craving as a clinical phenomenon. Self-reported craving to drink typically has served as a catch-all for the craving construct in these studies, whereas few studies have considered craving as a process (or hypothetical construct) that interacts with other phenomena to affect use. In contrast to alcohol, we believe that recently there has been more mechanistic work targeting cigarette craving-related processes. Here, we briefly present a narrative review of studies of acute alcohol craving in humans that have been conducted during the past two decades. We then specify important ways in which alcohol and tobacco differ (e.g., the role of withdrawal), and we note the unique challenges in inducing robust alcohol craving states in the laboratory. Finally, we offer recommendations for how the alcohol field might advance its conceptual understanding of craving by adopting ideas and methods drawn from the smoking research literature. Specifically, we suggest that researchers extend their studies to not only examine the link between alcohol craving and relapse but also to focus on why and, in some instances, how alcohol cravings matter clinically, and the circumstances under which craving especially matters. We propose research to investigate the shifts in alcohol-related cognitive and affective processing that occur during alcohol craving states. Furthermore, we highlight the value of research examining the level of insight that individuals with varying levels of alcohol involvement possess about their own craving-related processing shifts. We believe that laboratory studies can provide rich opportunities to examine conceptual questions about alcohol craving that are central to addiction.

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“…The underlying neural mechanisms of drug cue-reactivity 15 are typically mapped while participants passively view drug and non-drug stimuli during functional MRI (fMRI). These mechanisms involve the recruitment of the NAcc and vmPFC/OFC in response to drug vs. neutral cues in individuals with alcohol, 16,17 nicotine, 18,19 cannabis, 20 or cocaine [21][22][23] use disorders. Increased drug cue related activity in these cortico-striatal regions is associated with higher self-reported craving in alcohol, 24 nicotine, 18 and cannabis 20 addiction.…”

Section: Introductionmentioning

confidence: 99%

Cortico-striatal engagement during cue-reactivity, reappraisal, and savoring of drug and non-drug stimuli predicts craving in heroin addiction

Huang

Ceceli

Kronberg

et al. 2022

Preprint

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Importance: Heroin addiction is rampant and persistent, with devastating consequences to the public health, necessitating further study into the neurobiological mechanisms of drug cue-reactivity and craving-reducing interventions (e.g., reappraisal and savoring). Objective: To document cortico-striatal reactivity during passive viewing, reappraisal, and savoring, as predictors of heroin craving in individuals with heroin use disorder (iHUD) vs. controls. Design: A cross-sectional study (11/2020-09/2021), with a novel fMRI task in iHUD vs. controls. Setting: iHUD and controls were recruited from treatment facilities and surrounding neighborhoods, respectively. Participants: iHUD (N=32) [40.3(8.8) years; 7 (21.9%) women] and age-/sex-matched controls (N=21) [40.6(10.8) years; 8 (38%) women]. Main Outcomes and Measures: Between-group blood-oxygen-level-dependent signal differences during cue-reactivity/reappraisal/savoring and their direct contrast (reappraisal vs. savoring), and correlations with drug craving in iHUD. Results: Drug cue-reactivity (look drug>neutral) revealed higher nucleus accumbens and ventromedial prefrontal cortical activity in iHUD vs. controls (Z>3.1, p<.05), the latter positively correlated with post-task drug cravings (r2=.47, p<.001). In contrast, controls showed higher dorsolateral prefrontal cortex (dlPFC) and inferior frontal gyrus (IFG) reactivity to food cues (>drug; Z>3.1, p<.05; with the opposite pattern for the iHUD). Both drug-reappraisal and food-savoring (vs. respective passive viewing) showed increased activity in the IFG and supplementary motor area in all participants; the higher the dlPFC/IFG drug reappraisal in iHUD, the lower the post-MRI drug cue-induced craving (Z>3.1, p<.05). A direct comparison (drug-reappraisal vs. food-savoring) revealed higher drug-reappraisal in the ventral caudate and PFC regions in the iHUD (Z>3.1, p<.05), as predicted in the striatum by pre-task drug cravings (Z>2.57, p<.05); in controls, these areas showed instead higher food-savoring (Z>3.1, p<.05). Conclusions and Relevance: We demonstrate upregulated cortico-striatal activity during drug-cue exposure (while passively looking or reappraising) and impaired reactivity during processing (looking or savoring) of non-drug rewards in heroin addiction. These results bolster the impaired response inhibition and salience attribution model of drug addiction, previously supported mostly by results in stimulant addiction. Normalizing cortico-striatal function by reducing drug cue-reactivity (e.g., reappraising drug cues) and enhancing natural reward valuation (e.g., savoring food stimuli) may inform therapeutic mechanisms for reducing drug craving/seeking in heroin addiction.

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Neurobiological correlates of cue-reactivity in alcohol-use disorders: A voxel-wise meta-analysis of fMRI studies

Cited by 56 publications

References 140 publications

Identifying Alcohol Use Disorder With Resting State Functional Magnetic Resonance Imaging Data: A Comparison Among Machine Learning Classifiers

Identifying Alcohol Use Disorder With Resting State Functional Magnetic Resonance Imaging Data: A Comparison Among Machine Learning Classifiers

How laboratory studies of cigarette craving can inform the experimental alcohol craving literature

Cortico-striatal engagement during cue-reactivity, reappraisal, and savoring of drug and non-drug stimuli predicts craving in heroin addiction

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