Computationally Informed Interventions for Targeting Compulsive Behaviors

Targeting VMPFC-amygdala circuit with TMS in substance use disorder: A mechanistic framework

The ventromedial prefrontal cortex (VMPFC), located along the medial aspect of the frontal area, plays a critical role in regulating arousal/emotions. Its intricate connections with subcortical structures, including the striatum and amygdala, highlight the VMPFC's importance in the neurocircuitry of addiction. Due to these features, the VMPFC is considered a promising target for transcranial magnetic stimulation (TMS) in substance use disorders (SUD). By the end of 2023, all 21 studies targeting VMPFC for SUD used anatomical landmarks (e.g., Fp1/Fp2 in the EEG system) to define coil location with a fixed orientation. Nevertheless, one-size-fits-all TMS over VMPFC has yielded variable outcomes. Here, we suggested a pipeline based on a tailored TMS targeting framework aimed at optimally modulating the VMPFC-amygdala circuit on an individual basis. We collected MRI data from 60 participants with methamphetamine use disorders (MUDs). We examined the variability in TMS target location based on task-based functional connectivity between VMPFC and amygdala using psychophysiological interaction (PPI) analysis. Electric fields (EF) were calculated for fixed vs. optimized location (Fp1/Fp2 vs. individualized maximal PPI), orientation (AF7/AF8 vs. optimized algorithm) and intensity (constant vs. adjusted) to maximize target engagement. In our pipeline, the left medial amygdala, identified as the brain region with the highest (0.31 ± 0.29) fMRI drug cue reactivity, was selected as the subcortical seed region. The voxel with the most positive amygdala-VMPFC PPI connectivity in each participant was considered the individualized TMS target (MNI-coordinates: [12.6, 64.23, -0.8] ± [13.64, 3.50, 11.01]). This individualized VMPFC-amygdala connectivity significantly correlated with VAS craving after cue exposure (R = 0.27, p = 0.03). Coil orientation was optimized to increase EF strength over the targeted circuit (0.99 ± 0.21 V/m vs. the fixed approach: Fp1: 0.56 ± 0.22 and Fp2: 0.78 ± 0.25 V/m) and TMS intensity was harmonized across the population. This study highlights the potential of an individualized VMPFC targeting framework to enhance treatment outcomes for addiction, specifically modulating the personalized VMPFC-amygdala circuit.

Decoding and modifying dynamic attentional bias in gaming disorder

With the spread of smartphones and computer games, concerns have escalated regarding the rising prevalence of gaming disorder. Patients often display attentional biases, unconsciously turning their attention towards gaming-related stimuli. However, attempts to discover and ameliorate these attentional deficits have yielded inconsistent outcomes, potentially due to the dynamic nature of attentional bias. This study investigated neural mechanisms underlying attentional bias state by combining neuroimaging (functional magnetic resonance imaging -fMRI) with an approach-avoidance task tailored to an individual's gaming preference. We conducted a multivariate pattern analysis of endogenous brain activity in 21 participants with probable gaming disorder. Our analyses revealed that activity patterns in the insula tracked temporal attentional bias states specific to gaming stimuli. A broad network of frontal and parietal regions instead appeared to predict a general temporal attentional bias state. Finally, we conducted a proof-of-concept study for 'just-in-time' attentional bias training through fMRI-decoded neurofeedback of insula activity patterns, named decoded attentional bias training (DecABT). Our preliminary results suggest that DecABT may help to decrease the attractiveness of gaming stimuli via a insula- and precuneus-based neural mechanism. This work provides new evidence for the insula as an endogenous regulator of attentional bias states in gaming disorder and a starting point to develop novel, individualized therapeutic approaches to treat addiction.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Impact of provoked stress on model-free and model-based reinforcement learning in individuals with alcohol use disorder

Background

From both clinical and theoretical perspectives, understanding the functionality of evaluative reinforcement learning mechanisms (Model-Free, MF, and Model-Based, MB) under provoked stress, particularly in Alcohol Use Disorder (AUD), is crucial yet underexplored. This study aims to evaluate whether individuals with AUD who do not seek treatment show a greater tendency towards retrospective behaviors (MF) rather than prospective and deliberative simulations (MB) compared to controls. Additionally, it examines the impact of induced social stress on these decision-making processes.

Methods

A cohort comprising 117 participants, including 55 individuals with AUD and 62 controls, was examined. Acute social stress was induced through the socially evaluated cold pressor task (SECPT), followed by engagement in a Two-Step Markov task to assess MB and MF learning tendencies. We measured hypothalamic-pituitary-adrenal axis stress response using salivary cortisol levels.

Results

Both groups showed similar baseline cortisol levels and responses to the SECPT. Our findings indicate that participants with AUD exhibit a reduced reliance on MB strategies compared to those without AUD. Furthermore, stress decreases reliance on MB strategies in healthy participants, but this effect is not observed in those with AUD.

Conclusion

An atypical pattern of stress modulation impacting the balance between MB and MF reinforcement learning was identified in individuals with AUD who are not seeking treatment. Potential explanations for these findings and their clinical implications are explored.

Functional specialization of medial and lateral orbitofrontal cortex in inferential decision-making

Inferring prospective outcomes and updating behavior are prerequisites for making flexible decisions in the changing world. These abilities are highly associated with the functions of the orbitofrontal cortex (OFC) in humans and animals. The functional specialization of OFC subregions in decision-making has been established in animals. However, the understanding of how human OFC contributes to decision-making remains limited. Therefore, we studied this issue by examining the information representation and functional interactions of human OFC subregions during inference-based decision-making. We found that the medial OFC (mOFC) and lateral OFC (lOFC) collectively represented the inferred outcomes which, however, were context-general coding in the mOFC and context-specific in the lOFC. Furthermore, the mOFC-motor and lOFC-frontoparietal functional connectivity may indicate the motor execution of mOFC and the cognitive control of lOFC during behavioral updating. In conclusion, our findings support the dissociable functional roles of OFC subregions in decision-making.

Neural correlates of model-based behavior in internet gaming disorder and alcohol use disorder

Background

An imbalance between model-based and model-free decision-making systems is a common feature in addictive disorders. However, little is known about whether similar decision-making deficits appear in internet gaming disorder (IGD). This study compared neurocognitive features associated with model-based and model-free systems in IGD and alcohol use disorder (AUD).

Method

Participants diagnosed with IGD (n = 22) and AUD (n = 22), and healthy controls (n = 30) performed the two-stage task inside the functional magnetic resonance imaging (fMRI) scanner. We used computational modeling and hierarchical Bayesian analysis to provide a mechanistic account of their choice behavior. Then, we performed a model-based fMRI analysis and functional connectivity analysis to identify neural correlates of the decision-making processes in each group.

Results

The computational modeling results showed similar levels of model-based behavior in the IGD and AUD groups. However, we observed distinct neural correlates of the model-based reward prediction error (RPE) between the two groups. The IGD group exhibited insula-specific activation associated with model-based RPE, while the AUD group showed prefrontal activation, particularly in the orbitofrontal cortex and superior frontal gyrus. Furthermore, individuals with IGD demonstrated hyper-connectivity between the insula and brain regions in the salience network in the context of model-based RPE.

Discussion and Conclusions

The findings suggest potential differences in the neurobiological mechanisms underlying model-based behavior in IGD and AUD, albeit shared cognitive features observed in computational modeling analysis. As the first neuroimaging study to compare IGD and AUD in terms of the model-based system, this study provides novel insights into distinct decision-making processes in IGD.

Optimizing Individual Targeting of Fronto-Amygdala Network with Transcranial Magnetic Stimulation (TMS): Biophysical, Physiological and Behavioral Variations in People with Methamphetamine Use Disorder

Background

Previous studies in people with substance use disorders (SUDs) have implicated both the frontopolar cortex and amygdala in drug cue reactivity and craving, and amygdala-frontopolar coupling is considered a marker of early relapse risk. Accumulating data highlight that the frontopolar cortex can be considered a promising therapeutic target for transcranial magnetic stimulation (TMS) in SUDs. However, one-size-fits-all approaches to TMS targets resulted in substantial variation in both physiological and behavioral outcomes. Individualized TMS approaches to target cortico-subcortical circuits like amygdala-frontopolar have not yet been investigated in SUDs.

Objective

Here, we (1) defined individualized TMS target location based on functional connectivity of the amygdala-frontopolar circuit while people were exposed to drug-related cues, (2) optimized coil orientation based on maximizing electric field (EF) perpendicular to the individualized target, and (3) harmonized EF strength in targeted brain regions across a population.

Method

MRI data including structural, resting-state, and task-based fMRI data were collected from 60 participants with methamphetamine use disorders (MUDs). Craving scores based on a visual analog scale were collected immediately before and after the MRI session. We analyzed inter-subject variability in the location of TMS targets based on the maximum task-based connectivity between the left medial amygdala (with the highest functional activity among subcortical areas during drug cue exposure) and frontopolar cortex using psychophysiological interaction (PPI) analysis. Computational head models were generated for all participants and EF simulations were calculated for fixed vs. optimized coil location (Fp1/Fp2 vs. individualized maximal PPI location), orientation (AF7/AF8 vs. orientation optimization algorithm), and stimulation intensity (constant vs. adjusted intensity across the population).

Results

Left medial amygdala with the highest (mean ± SD: 0.31±0.29) functional activity during drug cue exposure was selected as the subcortical seed region. Amygdala-to-whole brain PPI analysis showed a significant cluster in the prefrontal cortex (cluster size: 2462 voxels, cluster peak in MNI space: [25 39 35]) that confirms cortico-subcortical connections. The location of the voxel with the most positive amygdala-frontopolar PPI connectivity in each participant was considered as the individualized TMS target (mean ± SD of the MNI coordinates: [12.6 64.23 -0.8] ± [13.64 3.50 11.01]). Individual amygdala-frontopolar PPI connectivity in each participant showed a significant correlation with VAS scores after cue exposure (R=0.27, p=0.03). Averaged EF strength in a sphere with r = 5mm around the individualized target location was significantly higher in the optimized (mean ± SD: 0.99 ± 0.21) compared to the fixed approach (Fp1: 0.56 ± 0.22, Fp2: 0.78 ± 0.25) with large effect sizes (Fp1: p = 1.1e-13, Hedges'g = 1.5, Fp2: p = 1.7e-5, Hedges'g = 1.26). Adjustment factor to have identical 1 V/m EF strength in a 5mm sphere around the individualized targets ranged from 0.72 to 2.3 (mean ± SD: 1.07 ± 0.29).

Conclusion

Our results show that optimizing coil orientation and stimulation intensity based on individualized TMS targets led to stronger electric fields in the targeted brain regions compared to a one-size-fits-all approach. These findings provide valuable insights for refining TMS therapy for SUDs by optimizing the modulation of cortico-subcortical circuits.