Neural correlates of reward magnitude and delay during a probabilistic delay discounting task in alcohol use disorder

Brain structural magnetic resonance imaging predictors of brief intervention response in individuals with alcohol use disorder

AIMS

Magnetic resonance imaging (MRI) studies have identified brain structural predictors of treatment response in individuals with alcohol use disorder (AUD) but with varying findings and primarily in male veterans. The present study investigated cortical surface area and thickness (CT) as predictors of brief intervention response in community-based adults with AUD.

METHODS

Sixty-five non-treatment-seeking adults with AUD (44.6% male, aged 33.2 ± 1.3 years) underwent an MRI and received a brief intervention comprising personalized feedback and motivational interviewing, with follow-up ~6-8 weeks later to quantify changes in drinks/week (DPW), the primary outcome. Eighteen bilateral a priori regions of interest (ROIs) were used to predict DPW at follow-up, adjusting for baseline drinking. Significant predictors were examined with secondary outcomes, percent drinking and heavy drinking days, and in relation to out-of-scanner measures of impulsivity and comorbidities.

RESULTS

Participants exhibited significant decreases in alcohol consumption in response to the brief intervention. Eight bilateral CT ROIs in the frontal, temporal, and occipital lobes, most notably medial orbitofrontal, middle temporal, and lateral occipital gyri, predicted DPW; however, only three predicted the secondary outcomes. Significant associations were observed between CT in frontal and occipital regions and impulsivity (delay discounting, lack of premeditation), executive functioning, anxiety, and stress.

CONCLUSIONS

Thinner frontal, temporal, and occipital ROIs predicted poorer brief intervention response, with notable overlap with brain regions previously implicated in AUD. Clarifying whether these regions reflect premorbid or acquired differences and, if the latter, the potential for recovery of cortical gray matter following drinking reductions are future priorities.

Brain Stimulation of Dorsolateral Prefrontal Cortices Influences Impulsivity in Delay Discounting Choices

Intertemporal decision-making is pivotal for human interests and health. Recently, studies instructed participants to make intertemporal choices for both themselves and others, but the specific mechanisms are still debated. To address the issue, in the current study, the cost-unneeded conditions (i.e., "Self Immediately - Self Delay" and "Other Immediately - Other Delay" conditions) and the cost-needed conditions (i.e., "Self Immediately - Other Delay" and "Self Delay - Other Immediately" conditions) were set with the identity of OTHER being a stranger. We manipulated the magnitude of reward (Experiment 1) and disrupted the activation of the dorsolateral prefrontal cortex with repetitive transcranial magnetic stimulation (rTMS; Experiment 2). We found that both the behavioral and rTMS manipulations increased smaller but sooner choice probability via reducing self-control function. The reduced self-control function elicited by rTMS affected both self- and other-related intertemporal choices via increasing the choice preference for smaller but sooner reward options, which may help people deeply understand the relationship between self- and other-related intertemporal choices in processing mechanism, especially when the OTHER condition is set as a stranger.

Cortical thickness of the inferior parietal lobule as a potential predictor of relapse in men with alcohol dependence

Alcohol dependence is a disorder with a high recurrence rate that leads to a considerable public health burden. The risk of relapse appears to be related to a complex interplay of multiple factors. Herein, we aimed to explore the potential neural predictors of relapse in Chinese male patients with alcohol dependence. This study enrolled 58 male patients with alcohol dependence who had undergone acute detoxification. General demographic information and clinical features were collected. Magnetic resonance imaging data were used to measure cortical thickness across 34 regions of the brain. Patients were followed up at six months, and 51 patients completed the follow-up visit. These patients were divided into a relapser and an abstainer group. A binary logistic regression analysis was performed to investigate the potential risk factors of relapse. Compared to abstainers, relapsers showed higher inattention and non-planning impulsivity on the 11th version of the Barratt Impulsive Scale. The cortical thicknesses of the inferior-parietal lobules were significantly higher in abstainers compared with those in relapsers. Furthermore, binary logistic regression analysis showed that the thickness of the inferior parietal lobule predicted relapse, and lower non-planning impulse was a protective factor against relapse. Relapsers show poorer impulse control than abstainers, and structural magnetic resonance imaging revealed a decreased thickness of the inferior parietal lobule in relapsers. Our results indicate the thickness of the inferior parietal lobule as a potential relapse predictor in male patients with alcohol dependence.

Distributed attribute representation in the superior parietal lobe during probabilistic decision-making

Several studies have examined the neural substrates of probabilistic decision-making, but few have systematically investigated the neural representations of the two objective attributes of probabilistic rewards, that is, the reward amount and the probability. Specifically, whether there are common or distinct neural activity patterns to represent the objective attributes and their association with the neural representation of the subjective valuation remains largely underexplored. We conducted two studies (nStudy1  = 34, nStudy2  = 41) to uncover distributed neural representations of the objective attributes and subjective value as well as their association with individual probability discounting rates. The amount and probability were independently manipulated to better capture brain signals sensitive to these two attributes and were presented simultaneously in Study 1 and successively in Study 2. Both univariate and multivariate pattern analyses showed that the brain activities in the superior parietal lobule (SPL), including the postcentral gyrus, were modulated by the amount of rewards and probability in both studies. Further, representational similarity analysis revealed a similar neural representation between these two objective attributes and between the attribute and valuation. Moreover, the SPL tracked the subjective value integrated by the hyperbolic function. Probability-related brain activations in the inferior parietal lobule were associated with the variability in individual discounting rates. These findings provide novel insights into a similar neural representation of the two attributes during probabilistic decision-making and perhaps support the common neural coding of stimulus objective properties and subjective value in the field of probabilistic discounting.

Event-related Potentials Corresponding to Decision-making Under Uncertain Conditions

BACKGROUND

Decision-making is essential to human functioning, and resolving uncertainty is an essential part of decision-making. Impaired decision-making is present in many pathological conditions, and identifying markers of decision-making under uncertainty will provide a measure of clinical impact in future studies of therapeutic intervention for impaired decision-making.

OBJECTIVE

To describe EEG event-related potentials (ERPs) correlating with decision-making under uncertain conditions when compared with certain conditions.

METHOD

We used a novel card-matching task based on the Wisconsin Card Sorting Test to describe the neural correlates of uncertainty, as measured by EEG, in a group of 27 neurotypical individuals. We evaluated 500-ms intervals in the 2 seconds after card presentation to identify ERPs that are associated with maximal uncertainty compared with maximal certainty.

RESULTS

After correcting for multiple comparisons, we identified an ERP in the 500-1000-ms time frame (certain > uncertain, max amplitude 12.73 µV, latency 914 ms) in the left posterior inferior region of the scalp. We also found a P300-like ERP in the left frontal and parietal regions in the 0-500-ms time frame when the individuals received correct versus incorrect feedback (incorrect feedback > correct feedback, max amplitude 1.625 µV, latency 339 ms).

CONCLUSION

We identified an ERP in the 500-1000-ms time frame (certain > uncertain) that may reflect the resolution of uncertainty, as well as a P300-like ERP when feedback is presented (incorrect feedback > correct feedback). These findings can be used in future studies to improve decision-making and resolve uncertainty on the described markers.

Neural Differences in Relation to Risk Preferences during Reward Processing: An Event-Related Potential Study

Inter-individual variability in risk preferences can be reflected in reward processing differences, making people risk-seekers or risk-averse. However, the neural correlates of reward processing in individuals with risk preferences remain unknown. Consequently, this event-related potential (ERP) study examined and compared electrophysiological correlates associated with different stages of reward processing in risk-seeking and risk-averse groups. Individuals scoring in the bottom and top 20% on the Balloon Analogue Risk Task (BART) were deemed risk-averse and risk-seeking, respectively. Participants engaged in a gambling task while their electroencephalogram (EEG) was recorded. Risk-seekers tended to choose high-risk options significantly more frequently than low-risk options, whereas risk-averse individuals chose low-risk options significantly more frequently than high-risk ones. All participants selected the low-risk alternative more slowly than the high-risk option. During the anticipation stage, the low-risk option elicited a relatively attenuated stimulus-preceding negativity (SPN) response from risk-seekers compared to risk-averse participants. During the outcome stage, feedback-related negativity (FRN) increased in risk-seekers responding to greater losses but not in risk-averse participants. These results indicate that ERP components can detect differences in reward processing during risky situations. In addition, these results suggest that motivation and cognitive control, along with their associated neural processes, may play a central role in differences in reward-based behavior between the two groups.

Differential Neural Correlates in the Prefrontal Cortex during a Delay Discounting Task in Healthy Adults: An fNIRS Study

The phenomenon of future rewards being devalued as a function of delay is referred to as delay discounting (DD). It is considered a measure of impulsivity, and steep DD characterizes psychiatric problems such as addictive disorders and attention deficit/hyperactivity disorder. This preliminarily study examined prefrontal hemodynamic activity using functional near-infrared spectroscopy (fNIRS) in healthy young adults performing a DD task. Prefrontal activity during a DD task with hypothetical monetary rewards was measured in 20 participants. A discounting rate (k-value) in the DD task was determined on the basis of a hyperbolic function. To validate the k-value, a DD questionnaire and the Barratt impulsiveness scale (BIS) were administered after fNIRS. The DD task induced a significant increase in oxygenated hemoglobin (oxy-Hb) concentration bilaterally in the frontal pole and dorsolateral prefrontal cortex (PFC) compared with a control task. Significant positive correlations were detected between left PFC activity and discounting parameters. Right frontal pole activity, however, showed significantly negative correlation with motor impulsivity as a BIS subscore. These results suggest that left and right PFCs have differential contributions when performing the DD task. The present findings suggest the idea that fNIRS measurement of prefrontal hemodynamic activity can be useful for understanding the neural mechanisms underlying DD and is applicable for assessing PFC function among psychiatric patients with impulsivity-related problems.

Cortical thickness of the inferior parietal lobule as a potential predictor of relapse in men with alcohol dependence

Background

Alcohol dependence (AD) is a disorder with a high recurrence rate that leads to a considerable public health burden. The risk of relapse appears to be related to a complex interplay of multiple factors. Herein, we aimed to explore the potential neural predictors of relapse in Chinese male patients with AD.

Methods

This study enrolled 58 male patients with AD who had undergone acute detoxification. General demographic information and clinical features were collected. Magnetic resonance imaging (MRI) data were used to measure cortical thickness across 34 regions of the brain. Patients were followed up at 6 months, and 51 patients completed the follow-up visit. These patients were divided into a relapser and an abstainer group. A binary logistic regression analysis was performed to investigate the potential risk factors of relapse.

Results

Compared to abstainers, relapsers showed higher inattention and non-planning impulsivity on the 11th version of the Barratt Impulsive Scale. The cortical thicknesses of the inferior-parietal lobule were significantly greater in abstainers compared with those in relapsers. Furthermore, binary logistic regression analysis showed that the thickness of the inferior parietal lobule predicted relapse.

Conclusions

Relapsers show poorer impulse control than abstainers, and MRI imaging shows a decreased thickness of the inferior parietal lobule in relapsers. Our results indicate the thickness of the inferior parietal lobule as a potential relapse predictor in male patients with AD.

Turning data into better mental health: Past, present, and future

In this mini-review, we discuss the fundamentals of using technology in mental health diagnosis and tracking. We highlight those principles using two clinical concepts: (1) cravings and relapse in the context of addictive disorders and (2) anhedonia in the context of depression. This manuscript is useful for both clinicians wanting to understand the scope of technology use in psychiatry and for computer scientists and engineers wishing to assess psychiatric frameworks useful for diagnosis and treatment. The increase in smartphone ownership and internet connectivity, as well as the accelerated development of wearable devices, have made the observation and analysis of human behavior patterns possible. This has, in turn, paved the way to understand mental health conditions better. These technologies have immense potential in facilitating the diagnosis and tracking of mental health conditions; they also allow the implementation of existing behavioral treatments in new contexts (e.g., remotely, online, and in rural/underserved areas), and the possibility to develop new treatments based on new understanding of behavior patterns. The path to understand how to best use technology in mental health includes the need to match interdisciplinary frameworks from engineering/computer sciences and psychiatry. Thus, we start our review by introducing bio-behavioral sensing, the types of information available, and what behavioral patterns they may reflect and be related to in psychiatric diagnostic frameworks. This information is linked to the use of functional imaging, highlighting how imaging modalities can be considered "ground truth" for mental health/psychiatric dimensions, given the heterogeneity of clinical presentations, and the difficulty of determining what symptom corresponds to what disease. We then discuss how mental health/psychiatric dimensions overlap, yet differ from, psychiatric diagnoses. Using two clinical examples, we highlight the potential agreement areas in assessment/management of anhedonia and cravings. These two dimensions were chosen because of their link to two very prevalent diseases worldwide: depression and addiction. Anhedonia is a core symptom of depression, which is one of the leading causes of disability worldwide. Cravings, the urge to use a substance or perform an action (e.g., shopping, internet), is the leading step before relapse. Lastly, through the manuscript, we discuss potential mental health dimensions.

Targeting the Salience Network: A Mini-Review on a Novel Neuromodulation Approach for Treating Alcohol Use Disorder

Alcohol use disorder (AUD) continues to be challenging to treat despite the best available interventions, with two-thirds of individuals going on to relapse by 1 year after treatment. Recent advances in the brain-based conceptual framework of addiction have allowed the field to pivot into a neuromodulation approach to intervention for these devastative disorders. Small trials of repetitive transcranial magnetic stimulation (rTMS) have used protocols developed for other psychiatric conditions and applied them to those with addiction with modest efficacy. Recent evidence suggests that a TMS approach focused on modulating the salience network (SN), a circuit at the crossroads of large-scale networks associated with AUD, may be a fruitful therapeutic strategy. The anterior insula or dorsal anterior cingulate cortex may be particularly effective stimulation sites given emerging evidence of their roles in processes associated with relapse.

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.

The Resting-State Neural Network of Delay Discounting

Delay discounting is a common phenomenon in daily life, which refers to the subjective value of a future reward decreasing as a function of time. Previous studies have identified several cortical regions involved in delay discounting, but the neural network constructed by the cortical regions of delay discounting is less clear. In this study, we employed resting-state functional magnetic resonance imaging (RS-fMRI) to measure the spontaneous neural activity in a large sample of healthy young adults and used the Monetary Choice Questionnaire to directly measure participants’ level of delay discounting. To identify the neural network of delay discounting at rest, we used an individual difference approach to explore brain regions whose spontaneous activities were related to delay discounting across the whole brain. Then, these brain regions served as seeds to identify the neural network of delay discounting. We found that the fractional amplitude of low-frequency fluctuations (fALFF) of the left insula were positively correlated to delay discounting. More importantly, its connectivity to the anterior cingulate cortex was read out for participants’ behavioral performance in the task of delay discounting. In short, our study provides empirical evidence that insula-anterior cingulate cortex connectivity may serve as a part of the neural network for delay discounting.

Reduced sensitivity to delayed time and delayed reward of the post-operative insular glioma patients in delay discounting

Previous studies have shown that the insula is closely related to addiction, and the structure's role in delay discounting can be measured by a specific task, but the specific role of the insula has been less studied. In this study, we first conducted a lesion study in which we recruited healthy controls (n = 30) and patients with unilateral insula injury (n = 16) to complete a behavioral delay discounting task. Then we conducted a functional magnetic resonance imaging (fMRI) study, and a separate group healthy volunteers (n = 51) completed a delay discounting task during the fMRI scan. The lesion study showed a significant difference between the two groups in the delay discounting task, which revealed that insula injury was associated with impaired decision making. The fMRI study revealed choice-sensitive insula activation that was modulated by delayed time and delayed reward, indicating an important role of the insula in delay discounting. Overall, our results provide evidence for a role of the insular lobe in delay discounting and suggests that this structure may be considered an important factor in the future treatment and diagnosis of addiction disorders.

Psilocybin targets a common molecular mechanism for cognitive impairment and increased craving in alcoholism

Alcohol-dependent patients commonly show impairments in executive functions that facilitate craving and can lead to relapse. However, the molecular mechanisms leading to executive dysfunction in alcoholism are poorly understood, and new effective pharmacological treatments are desired. Here, using a bidirectional neuromodulation approach, we demonstrate a causal link between reduced prefrontal mGluR2 function and both impaired executive control and alcohol craving. A neuron-specific prefrontal mGluR2 knockdown in rats generated a phenotype of reduced cognitive flexibility and excessive alcohol seeking. Conversely, virally restoring prefrontal mGluR2 levels in alcohol-dependent rats rescued these pathological behaviors. In the search for a pharmacological intervention with high translational potential, psilocybin was capable of restoring mGluR2 expression and reducing relapse behavior. Last, we propose a FDG-PET biomarker strategy to identify mGluR2 treatment-responsive individuals. In conclusion, we identified a common molecular pathological mechanism for both executive dysfunction and alcohol craving and provided a personalized mGluR2 mechanism-based intervention strategy for medication development for alcoholism.

Delay discounting and under-valuing of recent information predict poorer adherence to social distancing measures during the COVID-19 pandemic

The COVID-19 pandemic has brought about unprecedented global changes in individual and collective behaviour. To reduce the spread of the virus, public health bodies have promoted social distancing measures while attempting to mitigate their mental health consequences. The current study aimed to identify cognitive predictors of social distancing adherence and mental health symptoms, using computational models derived from delay discounting (the preference for smaller, immediate rewards over larger, delayed rewards) and patch foraging (the ability to trade-off between exploiting a known resource and exploring an unknown one). In a representative sample of the UK population (N = 442), we find that steeper delay discounting predicted poorer adherence to social distancing measures and greater sensitivity to reward magnitude during delay discounting predicted higher levels of anxiety symptoms. Furthermore, under-valuing recently sampled information during foraging independently predicted greater violation of lockdown guidance. Our results suggest that those who show greater discounting of delayed rewards struggle to maintain social distancing. Further, those who adapt faster to new information are better equipped to change their behaviour in response to public health measures. These findings can inform interventions that seek to increase compliance with social distancing measures whilst minimising negative repercussions for mental health.

Addictions NeuroImaging Assessment (ANIA): Towards an integrative framework for alcohol use disorder

Alcohol misuse and addiction are major international public health issues. Addiction can be characterized as a disorder of aberrant neurocircuitry interacting with environmental, genetic and social factors. Neuroimaging in alcohol misuse can thus provide a critical window into underlying neural mechanisms, highlighting possible treatment targets and acting as clinical biomarkers for predicting risk and treatment outcomes. This neuroimaging review on alcohol misuse in humans follows the Addictions Neuroclinical Assessment (ANA) that proposes incorporating three functional neuroscience domains integral to the neurocircuitry of addiction: incentive salience and habits, negative emotional states, and executive function within the context of the addiction cycle. Here we review and integrate multiple imaging modalities focusing on underlying cognitive processes such as reward anticipation, negative emotionality, cue reactivity, impulsivity, compulsivity and executive function. We highlight limitations in the literature and propose a model forward in the use of neuroimaging as a tool to understanding underlying mechanisms and potential clinical applicability for phenotyping of heterogeneity and predicting risk and treatment outcomes.

Probabilistic Reversal Learning Deficits in Patients With Methamphetamine Use Disorder-A Longitudinal Pilot Study

Methamphetamine use disorder (MUD) is increasing worldwide and commonly associated with learning deficits. Little is known the about underlying trajectories, i.e., how the affected higher-order cognitive functions develop over time and with respect to abstinence and relapse. A probabilistic reversal learning (PRL) paradigm was implemented to uncover the microstructure of impulsive choice and maladaptive learning strategies in 23 patients with MUD in comparison with 24 controls. Baseline data revealed fewer optimal choices and a pattern of altered learning behavior from negative and positive feedback in patients suggesting impairments in flexibly-adapting behavior to changes of reward contingencies. Integrating longitudinal data from a follow-up assessment after 3 months of specific treatment revealed a group-by-time interaction indicating a normalization of these cognitive impairments in patients with MUD. In summary, our study demonstrates behavioral correlates of maladaptive decision-making processes in patients with MUD, which may recover after 3 months of MUD-specific therapy paving the way for further learning-based interventions. Limited by a small sample size, the results of this pilot study warrant replication in larger populations.