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IMPORTANCE: In the last 25 years, functional magnetic resonance imaging drug cue reactivity (FDCR) studies have characterized some core aspects in the neurobiology of drug addiction. However, no FDCR-derived biomarkers have been approved for treatment development or clinical adoption. Traversing this translational gap requires a systematic assessment of the FDCR literature evidence, its heterogeneity, and an evaluation of possible clinical uses of FDCR-derived biomarkers. OBJECTIVE: To summarize the state of the field of FDCR, assess their potential for biomarker development, and outline a clear process for biomarker qualification to guide future research and validation efforts. EVIDENCE REVIEW: The PubMed and Medline databases were searched for every original FDCR investigation published from database inception until December 2022. Collected data covered study design, participant characteristics, FDCR task design, and whether each study provided evidence that might potentially help develop susceptibility, diagnostic, response, prognostic, predictive, or severity biomarkers for 1 or more addictive disorders. FINDINGS: There were 415 FDCR studies published between 1998 and 2022. Most focused on nicotine (122 [29.6%]), alcohol (120 [29.2%]), or cocaine (46 [11.1%]), and most used visual cues (354 [85.3%]). Together, these studies recruited 19 311 participants, including 13 812 individuals with past or current substance use disorders. Most studies could potentially support biomarker development, including diagnostic (143 [32.7%]), treatment response (141 [32.3%]), severity (84 [19.2%]), prognostic (30 [6.9%]), predictive (25 [5.7%]), monitoring (12 [2.7%]), and susceptibility (2 [0.5%]) biomarkers. A total of 155 interventional studies used FDCR, mostly to investigate pharmacological (67 [43.2%]) or cognitive/behavioral (51 [32.9%]) interventions; 141 studies used FDCR as a response measure, of which 125 (88.7%) reported significant interventional FDCR alterations; and 25 studies used FDCR as an intervention outcome predictor, with 24 (96%) finding significant associations between FDCR markers and treatment outcomes. CONCLUSIONS AND RELEVANCE: Based on this systematic review and the proposed biomarker development framework, there is a pathway for the development and regulatory qualification of FDCR-based biomarkers of addiction and recovery. Further validation could support the use of FDCR-derived measures, potentially accelerating treatment development and improving diagnostic, prognostic, and predictive clinical judgments.

Cue reactivity is one of the most frequently used paradigms in functional magnetic resonance imaging (fMRI) studies of substance use disorders (SUDs). Although there have been promising results elucidating the neurocognitive mechanisms of SUDs and SUD treatments, the interpretability and reproducibility of these studies is limited by incomplete reporting of participants' characteristics, task design, craving assessment, scanning preparation and analysis decisions in fMRI drug cue reactivity (FDCR) experiments. This hampers clinical translation, not least because systematic review and meta-analysis of published work are difficult. This consensus paper and Delphi study aims to outline the important methodological aspects of FDCR research, present structured recommendations for more comprehensive methods reporting and review the FDCR literature to assess the reporting of items that are deemed important. Forty-five FDCR scientists from around the world participated in this study. First, an initial checklist of items deemed important in FDCR studies was developed by several members of the Enhanced NeuroImaging Genetics through Meta-Analyses (ENIGMA) Addiction working group on the basis of a systematic review. Using a modified Delphi consensus method, all experts were asked to comment on, revise or add items to the initial checklist, and then to rate the importance of each item in subsequent rounds. The reporting status of the items in the final checklist was investigated in 108 recently published FDCR studies identified through a systematic review. By the final round, 38 items reached the consensus threshold and were classified under seven major categories: 'Participants' Characteristics', 'General fMRI Information', 'General Task Information', 'Cue Information', 'Craving Assessment Inside Scanner', 'Craving Assessment Outside Scanner' and 'Pre- and Post-Scanning Considerations'. The review of the 108 FDCR papers revealed significant gaps in the reporting of the items considered important by the experts. For instance, whereas items in the 'General fMRI Information' category were reported in 90.5% of the reviewed papers, items in the 'Pre- and Post-Scanning Considerations' category were reported by only 44.7% of reviewed FDCR studies. Considering the notable and sometimes unexpected gaps in the reporting of items deemed to be important by experts in any FDCR study, the protocols could benefit from the adoption of reporting standards. This checklist, a living document to be updated as the field and its methods advance, can help improve experimental design, reporting and the widespread understanding of the FDCR protocols. This checklist can also provide a sample for developing consensus statements for protocols in other areas of task-based fMRI.

While imaging studies have demonstrated volumetric differences in subcortical structures associated with dependence on various abused substances, findings to date have not been wholly consistent. Moreover, most studies have not compared brain morphology across those dependent on different substances of abuse to identify substance-specific and substance-general dependence effects. By pooling large multinational datasets from 33 imaging sites, this study examined subcortical surface morphology in 1628 nondependent controls and 2277 individuals with dependence on alcohol, nicotine, cocaine, methamphetamine, and/or cannabis. Subcortical structures were defined by FreeSurfer segmentation and converted to a mesh surface to extract two vertex-level metrics-the radial distance (RD) of the structure surface from a medial curve and the log of the Jacobian determinant (JD)-that, respectively, describe local thickness and surface area dilation/contraction. Mega-analyses were performed on measures of RD and JD to test for the main effect of substance dependence, controlling for age, sex, intracranial volume, and imaging site. Widespread differences between dependent users and nondependent controls were found across subcortical structures, driven primarily by users dependent on alcohol. Alcohol dependence was associated with localized lower RD and JD across most structures, with the strongest effects in the hippocampus, thalamus, putamen, and amygdala. Meanwhile, nicotine use was associated with greater RD and JD relative to nonsmokers in multiple regions, with the strongest effects in the bilateral hippocampus and right nucleus accumbens. By demonstrating subcortical morphological differences unique to alcohol and nicotine use, rather than dependence across all substances, results suggest substance-specific relationships with subcortical brain structures.

Many studies have reported that heavy substance use is associated with impaired response inhibition. Studies typically focused on associations with a single substance, while polysubstance use is common. Further, most studies compared heavy users with light/non-users, though substance use occurs along a continuum. The current mega-analysis accounted for these issues by aggregating individual data from 43 studies (3610 adult participants) that used the Go/No-Go (GNG) or Stop-signal task (SST) to assess inhibition among mostly "recreational" substance users (i.e., the rate of substance use disorders was low). Main and interaction effects of substance use, demographics, and task-characteristics were entered in a linear mixed model. Contrary to many studies and reviews in the field, we found that only lifetime cannabis use was associated with impaired response inhibition in the SST. An interaction effect was also observed: the relationship between tobacco use and response inhibition (in the SST) differed between cannabis users and non-users, with a negative association between tobacco use and inhibition in the cannabis non-users. In addition, participants' age, education level, and some task characteristics influenced inhibition outcomes. Overall, we found limited support for impaired inhibition among substance users when controlling for demographics and task-characteristics.

Although there is general consensus that altered brain structure and function underpins addictive disorders, clinicians working in addiction treatment rarely incorporate neuroscience-informed approaches into their practice. We recently launched the Neuroscience Interest Group within the International Society of Addiction Medicine (ISAM-NIG) to promote initiatives to bridge this gap. This article summarizes the ISAM-NIG key priorities and strategies to achieve implementation of addiction neuroscience knowledge and tools for the assessment and treatment of substance use disorders. We cover two assessment areas: cognitive assessment and neuroimaging, and two interventional areas: cognitive training/remediation and neuromodulation, where we identify key challenges and proposed solutions. We reason that incorporating cognitive assessment into clinical settings requires the identification of constructs that predict meaningful clinical outcomes. Other requirements are the development of measures that are easily-administered, reliable, and ecologically-valid. Translation of neuroimaging techniques requires the development of diagnostic and prognostic biomarkers and testing the cost-effectiveness of these biomarkers in individualized prediction algorithms for relapse prevention and treatment selection. Integration of cognitive assessments with neuroimaging can provide multilevel targets including neural, cognitive, and behavioral outcomes for neuroscience-informed interventions. Application of neuroscience-informed interventions including cognitive training/remediation and neuromodulation requires clear pathways to design treatments based on multilevel targets, additional evidence from randomized trials and subsequent clinical implementation, including evaluation of cost-effectiveness. We propose to address these challenges by promoting international collaboration between researchers and clinicians, developing harmonized protocols and data management systems, and prioritizing multi-site research that focuses on improving clinical outcomes.

Summary: Episodic future thinking (EFT) strengthens self‐regulation abilities by increasing the perceived value of long‐term reinforcements and reducing impulsive choice in delay discounting tasks. As such, EFT interventions have the potential to improve dietary and eating‐related decision‐making in individuals with obesity or binge eating symptoms, conditions associated with elevated delay discounting. Here, we meta‐analyzed evidence from 12 studies that assessed whether EFT interventions improve delay discounting and real‐world food choice compared to control interventions. Included studies involved 951 adults with overweight or obesity (body mass index [BMI] ≥25). There were no studies involving participants with binge eating disorder. EFT intervention pooled effects were significant, improving delay discounting with a medium effect, g = 0.55, p < 0.0001, and subsequent food choice outcomes with a small effect, g = 0.31, p < 0.01. Notably, our review is the first to analyze mechanisms of effect in this population, demonstrating that improvements were greater when temporal horizons of EFT episodes were aligned with delay discounting tasks and more distant horizons predicted far‐transfer to subsequent dietary and eating‐related choices. Our findings thus show that EFT is an effective intervention for individuals with higher weight at risk of adverse health consequences. [ABSTRACT FROM AUTHOR]

Drug use is a choice with immediate positive outcomes, but long-term negative consequences. Thus, the repeated use of drugs in the face of negative consequences suggests dysfunction in the cognitive mechanisms underpinning decision-making. This cognitive dysfunction can be mapped into three stages: the formation of preferences involving valuation of decision options; choice implementation including motivation, self-regulation and inhibitory processes; and feedback processing implicating reinforcement learning. This article reviews behavioral studies that have examined alterations in these three stages of decision-making in people with substance use disorders. Relative to healthy individuals, those with alcohol, cannabis, stimulant and opioid use disorders value risky options more highly during the formation of preferences; have a greater appetite for superficially attractive rewards during choice implementation; and are both more efficient in learning from rewards and less efficient in learning from losses during feedback processing. These observed decision-making deficits are most likely due to both premorbid factors and drug-induced effects. Because decision-making deficits have been prospectively associated with a greater risk of drug relapse, we advocate for greater research on modulating the component stages that give rise to dysfunctional decision-making in disorders of addiction.