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The habenula, an epithalamic nucleus involved in reward and aversive processing, may contribute to negative reinforcement mechanisms maintaining nicotine use. We used a performance feedback task that differentially activates the striatum and habenula and administered nicotine and varenicline (versus placebos) to overnight-abstinent smokers and nonsmokers to delineate feedback-related functional brain alterations both as a function of smoking trait (smokers versus nonsmokers) and drug administration state (drug versus placebo). Smokers showed less striatal responsivity to positive feedback, an alteration not mitigated by drug administration, but rather correlated with trait-level addiction severity. Conversely, nicotine administration reduced habenula activity following both positive and negative feedback among abstinent smokers, but not nonsmokers, and increased habenula activity among smokers correlated with elevated state-level tobacco cravings. These outcomes highlight a dissociation between neurobiological processes linked with the dependence severity trait and the nicotine withdrawal state. Interventions simultaneously targeting both aspects may improve currently poor cessation outcomes.

Two often-studied forms of uncertain decision-making (DM) are risky-DM (outcome probabilities known) and ambiguous-DM (outcome probabilities unknown). While DM in general is associated with activation of several brain regions, previous neuroimaging efforts suggest a dissociation between activity linked with risky and ambiguous choices. However, the common and distinct neurobiological correlates associated with risky-and ambiguous-DM, as well as their specificity when compared to perceptual-DM (as a 'control condition'), remains to be clarified. We conducted multiple meta-analyses on neuroimaging results from 151 studies to characterize common and domain-specific brain activity during risky-, ambiguous-, and perceptual-DM. When considering all DM tasks, convergent activity was observed in brain regions considered to be consituents of the canonical salience, valuation, and executive control networks. When considering subgroups of studies, risky-DM (vs. perceptual-DM) was linked with convergent activity in the striatum and anterior cingulate cortex (ACC), regions associated with reward-related processes (determined by objective functional decoding). When considering ambiguous-DM (vs. perceptual-DM), activity convergence was observed in the lateral prefrontal cortex and insula, regions implicated in affectively-neutral mental processes (e.g., cognitive control and behavioral responding; determined by functional decoding). An exploratory meta-analysis comparing brain activity between substance users and non-users during risky-DM identified reduced convergent activity among users in the striatum, cingulate, and thalamus. Taken together, these findings suggest a dissociation of brain regions linked with risky-and ambiguous-DM reflecting possible differential functionality and highlight brain alterations potentially contributing to poor decision-making in the context of substance use disorders.

Reward learning is a ubiquitous cognitive mechanism guiding adaptive choices and behaviors, and when impaired, can lead to considerable mental health consequences. Reward-related functional neuroimaging studies have begun to implicate networks of brain regions essential for processing various peripheral influences (e.g., risk, subjective preference, delay, social context) involved in the multifaceted reward processing construct. To provide a more complete neurocognitive perspective on reward processing that synthesizes findings across the literature while also appreciating these peripheral influences, we used emerging meta-analytic techniques to elucidate brain regions, and in turn networks, consistently engaged in distinct aspects of reward processing. Using a data-driven, meta-analytic, k-means clustering approach, we dissociated seven meta-analytic groupings (MAGs) of neuroimaging results (i.e., brain activity maps) from 749 experimental contrasts across 176 reward processing studies involving 13,358 healthy participants. We then performed an exploratory functional decoding approach to gain insight into the putative functions associated with each MAG. We identified a seven-MAG clustering solution that represented dissociable patterns of convergent brain activity across reward processing tasks. Additionally, our functional decoding analyses revealed that each of these MAGs mapped onto discrete behavior profiles that suggested specialized roles in predicting value (MAG-1 & MAG-2) and processing a variety of emotional (MAG-3), external (MAG-4 & MAG-5), and internal (MAG-6 & MAG-7) influences across reward processing paradigms. These findings support and extend aspects of well-accepted reward learning theories and highlight large-scale brain network activity associated with distinct aspects of reward processing.Keywords Valuation . K-means clustering . Functional decoding . Executive control network . Default mode network Reward learning is a fundamental cognitive process vital for adaptive functioning that consequently, when disrupted, has a significant impact on mental health. Widely accepted and commonly adopted neurocognitive models of reward learning propose that value predictions are formed and updated based on dopaminergic reward prediction error (RPE) signals

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Drug and natural cue-reactivityHill-Bowen et al. ABSTRACTBackground: The cue-reactivity paradigm is a widely adopted neuroimaging probe assessing brain activity linked to attention, memory, emotion, and reward processing associated with the presentation of appetitive stimuli. Lacking, is the apperception of more precise brain regions, neurocircuits, and mental operations comprising cue-reactivity's multi-elemental nature. To resolve such complexities, we employed emergent meta-analytic techniques to enhance insight into drug and natural cue-reactivity in the brain.Methods: Operating from this perspective, we first conducted multiple coordinate-based metaanalyses to define common and distinct brain regions showing convergent activation across studies involving drug-related and natural-reward cue-reactivity paradigms. In addition, we examined the activation profiles of each convergent brain region linked to cue-reactivity as seeds in taskdependent and task-independent functional connectivity analyses. Using methods to cluster regions of interest, we categorized cue-reactivity into cliques, or sub-networks, based on the functional similarities between regions. Cliques were further classified with psychological constructs.Results: We identified a total of 164 peer-reviewed articles: 108 drug-related, and 56 naturalreward. When considering cue-reactivity collectively, across both drug and natural studies, activity convergence was observed in the dorsal striatum, limbic, insula, parietal, occipital, and temporal regions. Common convergent neural activity between drug and natural cue-reactivity was observed in the caudate, amygdala, thalamus, cingulate, and temporal regions. Drug distinct convergence was observed in the putamen, cingulate, and temporal regions, while natural distinct convergence was observed in the caudate, parietal, occipital, and frontal regions. We seeded identified cuereactivity regions in meta-analytic connectivity modeling and resting-state functional connectivity analyses. Consensus hierarchical clustering of both connectivity analyses identified six distinct cliques that were further functionally characterized using the BrainMap and Neurosynth databases.Conclusions: We examined the multifaceted nature of cue-reactivity and decomposed this construct into six elements of visual, executive function, sensorimotor, salience, emotion, and selfreferential processing. Further, we demonstrated that these elements are supported by perceptual, sensorimotor, tripartite, and affective networks, which are essential to understanding the neural mechanisms involved in the development and or maintenance of addictive disorders.

Reward learning is a ubiquitous cognitive mechanism guiding adaptive choices and behaviors, and when impaired, can lead to considerable mental health consequences. Reward-related functional neuroimaging studies have begun to implicate networks of brain regions essential for processing various peripheral influences (e.g., risk, subjective preference, delay, social context) involved in the multifaceted reward processing construct. To provide a more complete neurocognitive perspective on reward processing that synthesizes findings across the literature while also appreciating these peripheral influences, we utilized emerging meta-analytic techniques to elucidate brain regions, and in turn networks, consistently engaged in distinct aspects of reward processing. Using a data-driven, meta-analytic, k-means clustering approach, we dissociated seven meta-analytic groupings (MAGs) of neuroimaging results (i.e., brain activity maps) from 749 experimental contrasts across 176 reward processing studies involving 13,358 healthy participants.We then performed an exploratory functional decoding approach to gain insight into the putative functions associated with each MAG. We identified a seven-MAG clustering solution which represented dissociable patterns of convergent brain activity across reward processing tasks.Additionally, our functional decoding analyses revealed that each of these MAGs mapped onto discrete behavior profiles that suggested specialized roles in predicting value and processing a variety of emotional (MAG-3), external (MAG-4 & MAG-5), and internal (MAG-6 & MAG-7) influences across reward processing paradigms. These findings support and extend aspects of well-accepted reward learning theories and highlight large-scale brain network activity associated with distinct aspects of reward processing.

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