Altered effective connectivity of the reward network during an incentive-processing task in adults with alcohol use disorder

Impact of data processing varieties on DCM estimates of effective connectivity from task-fMRI

Effective connectivity (EC) refers to directional or causal influences between interacting neuronal populations or brain regions and can be estimated from functional magnetic resonance imaging (fMRI) data via dynamic causal modeling (DCM). In contrast to functional connectivity, the impact of data processing varieties on DCM estimates of task-evoked EC has hardly ever been addressed. We therefore investigated how task-evoked EC is affected by choices made for data processing. In particular, we considered the impact of global signal regression (GSR), block/event-related design of the general linear model (GLM) used for the first-level task-evoked fMRI analysis, type of activation contrast, and significance thresholding approach. Using DCM, we estimated individual and group-averaged task-evoked EC within a brain network related to spatial conflict processing for all the parameters considered and compared the differences in task-evoked EC between any two data processing conditions via between-group parametric empirical Bayes (PEB) analysis and Bayesian data comparison (BDC). We observed strongly varying patterns of the group-averaged EC depending on the data processing choices. In particular, task-evoked EC and parameter certainty were strongly impacted by GLM design and type of activation contrast as revealed by PEB and BDC, respectively, whereas they were little affected by GSR and the type of significance thresholding. The event-related GLM design appears to be more sensitive to task-evoked modulations of EC, but provides model parameters with lower certainty than the block-based design, while the latter is more sensitive to the type of activation contrast than is the event-related design. Our results demonstrate that applying different reasonable data processing choices can substantially alter task-evoked EC as estimated by DCM. Such choices should be made with care and, whenever possible, varied across parallel analyses to evaluate their impact and identify potential convergence for robust outcomes.

Functional Domains of Substance Use and their Implications to Trauma: A Systematic Review of Neuroimaging Studies

Substance use disorder (SUD) is a significant health problem, and trauma exposure is a known risk factor for the escalation of substance use. However, the shared neural mechanisms through which trauma is associated with substance use are still unknown. Therefore, we systematically review neuroimaging studies focusing on three domains that may contribute to the overlapping mechanisms of SUD and trauma-reward salience, negative emotionality, and inhibition. Using PRISMA guidelines, we identified 45 studies utilizing tasks measuring these domains in alcohol, tobacco, and cannabis use groups. Greater reward, lesser regulation of inhibitory processes, and mixed findings of negative emotionality processes in individuals who use substances versus controls were found. Specifically, greater orbitofrontal cortex, ventral tegmental area, striatum, amygdala, and hippocampal activation was found in response to reward-related tasks, and reduced activation was found in the inferior frontal gyrus and hippocampus in response to inhibition-related tasks. Importantly, no studies in trauma-exposed individuals met our review criteria. Future studies examining the role of trauma-related factors are needed, and more studies should explore inhibition- and negative-emotionality domains in individuals who use substances to uncover clinically significant alterations in these domains that place an individual at greater risk for developing a SUD.

People who binge drink show neuroendocrine tolerance to alcohol cues that is associated with immediate and future drinking- results from a randomized clinical experiment

Neuroendocrine tolerance to alcohol, i.e., a blunted cortisol response to alcohol, has been linked to Ventromedial Prefrontal Cortex (VmPFC) alcohol cue reactivity and relapse risk in severe Alcohol Use Disorders (AUDs), but its role in the development of AUDs is not clear. Recent work suggests that blunted cortisol responses to alcohol cues in individuals who engage in binge drinking (BD) may play a role in motivation to consume larger amounts of alcohol, but the link between this dysregulated endocrine response and BD's neural responses to alcohol cues remains unclear. To examine this, two groups of participants were recruited based on their recent drinking history. Thirty-three BD and 31 non-binging, social drinkers (SD) were exposed to alcohol cues and water cues in two separate 7 T functional magnetic resonance imaging (fMRI) scans. Each scan was followed by the Alcohol Taste Test (ATT) of implicit motivation for alcohol and a post-experiment, one-month prospective measurement of their "real world" drinking behavior. During each scan session, blood plasma was collected repeatedly to examine the separate effects of alcohol cues and alcohol consumption on cortisol levels. Relative to water cues and SD, BD demonstrated blunted cortisol cue reactivity that was negatively associated with VmPFC cue reactivity. In BD, both blunted cortisol and greater VmPFC cue reactivity were related to immediate and future alcohol consumption in the month following the scans. Thus, neuroendocrine tolerance in BD may be associated with increased incentive salience of cues and contribute mechanistically to increased alcohol consumption seen in the development of AUDs.

Blunted neural reward response to alcohol and greater alcohol motivation in binge drinkers in a randomized clinical experiment

BACKGROUND

Alcohol stimulates cerebral blood flow (CBF) in brain reward regions. However, neural processes that support sustained alcohol motivation after the first drink are not well understood.

METHODS

Using a novel placebo-controlled, randomized, crossover experiment, 27 individuals who binge drink (BD; 15 M, 12 F) and 25 social drinkers (SD; 15 M, 10 F) underwent a behavioral test of self-motivated alcohol consumption using an Alcohol Taste Test (ATT) involving alcoholic and nonalcoholic beer on separate days. The test was followed immediately by perfusion functional magnetic resonance imaging (fMRI). On both days, participants then engaged in a post-scan ATT with placebo beer to assess sustained alcohol self-motivation without active alcohol effects. Linear mixed effects models were used to examine the effects of drinking group on the placebo-controlled effect of initial alcohol motivation on brain perfusion (whole brain corrected p < 0.001, cluster corrected p < 0.025) and on the relationship between placebo-controlled brain perfusion and sustained alcohol motivation.

RESULTS

Initial alcohol self-motivation in the alcohol relative to placebo session led to markedly decreased activation in the medial orbitofrontal cortex (OFC) and the ventral striatum in BD relative to SD, indicative of neural reward tolerance. The BD group also showed an enhanced neural response in behavioral intention regions of the supplementary motor area (SMA) and inferior frontal gyrus (IFG) regions. Moreover, there was greater sustained alcohol motivation in BD than SD in the post-scan ATT in the alcohol relative to placebo session. Correspondingly, only in BD and only in the alcohol session, lower alcohol-induced OFC response correlated with concurrent sensitized SMA response, and each predicted the subsequent sustained higher alcohol motivation in the post-scan ATT.

CONCLUSIONS

Alcohol-related OFC tolerance may play an important role in sustained alcohol motivation. Furthermore, both specific alcohol-related neural reward tolerance and premotor sensitization responses may contribute to escalating alcohol motivation to drive excessive alcohol intake, even in individuals without alcohol use disorder.

The differential neural substrates for reward choice under gain-loss contexts and risk in alcohol use disorder: Evidence from a voxel-based meta-analysis

BACKGROUND

Making a risky decision is a complex process that involves the evaluation of both the values of the options and the associated risk level; this process is distinct from reward processing in gain versus loss contexts. Although disrupted reward processing in mesolimbic dopamine circuitry is suggested to underlie pathological incentive processing in patients with alcohol use disorder (AUD), the differential neural processes subserving these motivational tendencies for risk situations or gain/loss choices in decision-making have not been identified.

METHODS

To examine the common or distinct neural mechanisms in the evaluation of risk versus outcomes for AUD, we conducted two separate coordinate-based meta-analyses of functional neuroimaging studies by using Seed-Based d Mapping software to evaluate 13 studies investigating gain and loss processing and 10 studies investigating risky decision-making.

RESULTS

During gain and loss processing, relative to healthy controls, AUD patients showed reduced activation in the mesocortical-limbic circuit, including the orbital prefrontal cortex (OFC), dorsal striatum, insula, hippocampus, cerebellum, cuneus cortex and superior temporal gyrus, but hyperactivation in the inferior temporal gyrus and paracentral lobule (extending to the middle cingulate cortex (MCC) and precuneus). During decision-making under risk, AUD patients exhibited hypoactivity of the prefrontal and cingulate cortices, including the posterior cingulate cortex (extending to the MCC), middle frontal gyrus, medial prefrontal cortex, dorsolateral prefrontal cortex, OFC and anterior cingulate cortex.

CONCLUSIONS

Our results extend existing neurological evidence by showing that a reduced response in the mesocortical-limbic circuit is found in gain versus loss processing, with decreased responsivity in cortical regions in risk decision-making. Our results implicate dissociable neural circuit responses for gain-loss processing and risk decision-making, which contribute to a better understanding of the pathophysiological mechanism underlying nondrug incentive and risk processing in individuals with AUD.

Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures

Individuals with alcohol use disorder (AUD) may manifest an array of neural and behavioral abnormalities, including altered brain networks, impaired neurocognitive functioning, and heightened impulsivity. Using multidomain measures, the current study aimed to identify specific features that can differentiate individuals with AUD from healthy controls (CTL), utilizing a random forests (RF) classification model. Features included fMRI-based resting-state functional connectivity (rsFC) across the reward network, neuropsychological task performance, and behavioral impulsivity scores, collected from thirty abstinent adult males with prior history of AUD and thirty CTL individuals without a history of AUD. It was found that the RF model achieved a classification accuracy of 86.67% (AUC = 93%) and identified key features of FC and impulsivity that significantly contributed to classifying AUD from CTL individuals. Impulsivity scores were the topmost predictors, followed by twelve rsFC features involving seventeen key reward regions in the brain, such as the ventral tegmental area, nucleus accumbens, anterior insula, anterior cingulate cortex, and other cortical and subcortical structures. Individuals with AUD manifested significant differences in impulsivity and alterations in functional connectivity relative to controls. Specifically, AUD showed heightened impulsivity and hypoconnectivity in nine connections across 13 regions and hyperconnectivity in three connections involving six regions. Relative to controls, visuo-spatial short-term working memory was also found to be impaired in AUD. In conclusion, specific multidomain features of brain connectivity, impulsivity, and neuropsychological performance can be used in a machine learning framework to effectively classify AUD individuals from healthy controls.