History of withdrawal modulates drug- and food-cue reactivity in cocaine dependent participants

Denomme, William James; Shane, Matthew S.

doi:10.1016/j.drugalcdep.2019.107815

Cited by 9 publications

(5 citation statements)

References 44 publications

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“…Participants in the present study were only required to abstain from cannabis for 24 hr before the lab appointment, which may not have been sufficient enough time for a deprivation state to develop. Indeed, research examining withdrawal trajectory and severity in disordered alcohol (Jasinska et al, 2014), cocaine (Denomme & Shane, 2020), and heroin (Lou et al, 2012) users consistently finds increased drug-related cue activity in regions of the brain associated with reward and craving, relative to nondrug cues. Future research may find the expected blunting and amplification of positive and negative reinforcement processing, respectively, in a sample of regular cannabis users who are asked to abstain from marijuana for a longer period of time (e.g., 1 week).…”

Section: Discussionmentioning

confidence: 99%

Impact of acute stress on neural indices of positive and negative reinforcement processing in cannabis users.

Preston¹,

Albanese²,

Schmidt³

et al. 2022

Psychology of Addictive Behaviors

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Objective: Chronic cannabis use is maintained in part through dysregulated stress and reward response systems. Specifically, stress-related negative affect is thought to act as a salient motivator for chronic substance use. Models of addiction posit that the transition from positive to negative reinforcement motives for substance use is a key mechanism of disordered use. However, research in substance-using samples has not assessed stress-related neural processing of both positive and negative reinforcement. Method: Therefore, the present study utilized laboratory stress induction to examine how stress affects the reward positivity, an event-related potential sensitive to both positive (RewP) and negative (relief-RewP) reinforcement, in 87 cannabis users (58.10% female, M age = 19.40) varying in cannabis use disorder (CUD) severity and, as part of larger study aims, history of traumatic brain injury (TBI). We predicted greater CUD severity would be associated with a blunted RewP and enhanced relief-RewP, particularly after stress induction, independent of TBI status. Results: Findings indicated that CUD severity was not associated with RewP/relief-RewP amplitude regardless of acute stress. Exploratory analyses revealed, however, that among those with history of TBI+, CUD severity was associated with greater stress-elicited blunting of the RewP and enhancement of the relief-RewP. Conclusion: Although initial findings contradict current allostatic models of addiction, exploratory findings suggest that history of TBI, and potentially other confounding variables related to increased risk of TBI experience, may influence the extent to which stressful experiences modulate the neurophysiology of both positive and negative reinforcement reward processing in CUD. Public Health Significance StatementThe present study suggests that in people with history of TBI, greater disordered cannabis use is associated with a stress-elicited decrease in reward sensitivity to positive reinforcement, in addition to a simultaneous increase in sensitivity to negative reinforcement. Present results may inform future interventions targeting reward dysregulation in specific populations of disordered cannabis users.

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Section: Discussionmentioning

confidence: 99%

Impact of acute stress on neural indices of positive and negative reinforcement processing in cannabis users.

Preston¹,

Albanese²,

Schmidt³

et al. 2022

Psychology of Addictive Behaviors

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“…Cha et al (2016), for instance, used a latent factor approach to delineate shared/unique neurobiological components of Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD; Cha et al, 2016), where comorbidity of symptom characteristics has long been acknowledged (Hamilton et al, 2014). Similar modelling of shared/unique variance has rarely been undertaken within the externalizing domain (but see more rudimentary approaches recently undertaken by Denomme et al, 2018;Denomme & Shane, 2020;Simard et al, 2021), but may be equally informative for distinguishing the extent to which specific neurobiological abnormalities relate to core underlying antisocial characteristics, to consequences of an antisocial/substance-abusing lifestyle, or to shared latent factors. Indeed, HiTOP currently conceptualizes externalizing as composed of two higher-order factors (disinhibited/ antagonistic externalizing), and the triarchic model of psychopathy has conceptualized psychopathy as the higher-order factor of three subdimensions (boldness, meanness, and disinhibition).…”

Section: Latent Factor Approachesmentioning

confidence: 99%

“…For instance, some work suggests that SUDs may be conceptualized as occurring with or without withdrawal symptoms (e.g. Denomme & Shane, 2020). Whereas, DSM5's crude approach classifies all SUD patients together so long as they show some combination of required symptoms, a hierarchical approach that can individually model the prevalence and relevance of each symptom can allow researchers to delve further into these more nuanced questions.…”

Section: Latent Factor Approachesmentioning

confidence: 99%

“…A small amount of work outside the machine learning literature has moved in this direction. For instance, Denomme et al, 2018 used fMRI to identify regions related to enhanced cue reactivity in cocaine-dependent individuals, and undertook subsequent regression analyses to dissect the extent to which activity within each of these regions related more closely to continuously derived metrics of psychopathic traits (i.e., PCL-R total score) or substance use severity (i.e., years of lifetime use; see also Denomme & Shane, 2020;Simard et al, 2021). And utilizing a quite distinct approach, Hyatt et al (2019) created neuroanatomical profiles (based on their five-factor traits) on 1101 participants from the Human Connectome Project, and reported that while neuroanatomical profiles of Agreeableness and Conscientiousness showed medium-to-large relationships with externalizing psychopathology, neuroanatomical profiles of Agreeableness related more closely to metrics of antisocial behavior, while neuroanatomical profiles of Conscientiousness related more closely to metrics of substance abuse.…”

Section: Collect Data On Both Addiction and Antisocialitymentioning

confidence: 99%

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Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer

Shane

Denomme

2021

Personality Neuroscience

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By some accounts, as many as 93% of individuals diagnosed with antisocial personality disorder (ASPD) or psychopathy also meet criteria for some form of substance use disorder (SUD). This high level of comorbidity, combined with an overlapping biopsychosocial profile, and potentially interacting features, has made it difficult to delineate the shared/unique characteristics of each disorder. Moreover, while rarely acknowledged, both SUD and antisociality exist as highly heterogeneous disorders in need of more targeted parcellation. While emerging data-driven nosology for psychiatric disorders (e.g., Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP)) offers the opportunity for a more systematic delineation of the externalizing spectrum, the interrogation of large, complex neuroimaging-based datasets may require data-driven approaches that are not yet widely employed in psychiatric neuroscience. With this in mind, the proposed article sets out to provide an introduction into machine learning methods for neuroimaging that can help parse comorbid, heterogeneous externalizing samples. The modest machine learning work conducted to date within the externalizing domain demonstrates the potential utility of the approach but remains highly nascent. Within the paper, we make suggestions for how future work can make use of machine learning methods, in combination with emerging psychiatric nosology systems, to further diagnostic and etiological understandings of the externalizing spectrum. Finally, we briefly consider some challenges that will need to be overcome to encourage further progress in the field.

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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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History of withdrawal modulates drug- and food-cue reactivity in cocaine dependent participants

Cited by 9 publications

References 44 publications

Impact of acute stress on neural indices of positive and negative reinforcement processing in cannabis users.

Impact of acute stress on neural indices of positive and negative reinforcement processing in cannabis users.

Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer

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

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