Computationally Informed Interventions for Targeting Compulsive Behaviors

Kahnt, Thorsten

doi:10.1016/j.biopsych.2022.08.028

Cited by 5 publications

(3 citation statements)

References 154 publications

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“…Another notable finding is that individuals with AUD exhibited hyperactivation in the prefrontal regions, specifically the OFC and SFG, in the context of model-based behavior. The OFC has been identified as a key regulator of goal-directed, or model-based action planning and execution ( Gremel & Costa, 2013 ; Jones et al., 2012 ; Kahnt, 2023 ; McDannald, Lucantonio, Burke, Niv, & Schoenbaum, 2011 ), encoding the value of stimuli and the relationship between stimuli and their expected outcomes ( Howard, Gottfried, Tobler, & Kahnt, 2015 ; Lopatina et al., 2015 ). Similarly, the SFG is involved in higher cognitive functions and working memory associated with executive processing ( Boisgueheneuc et al., 2006 ).…”

Section: Discussionmentioning

confidence: 99%

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

Kwon,

Choi,

Park

et al. 2024

JBA

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BackgroundAn 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).MethodParticipants 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.ResultsThe 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 ConclusionsThe 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.

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Section: Discussionmentioning

confidence: 99%

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

Kwon,

Choi,

Park

et al. 2024

JBA

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“…Nevertheless, the studies in OFC functional specialization can provide clinical insights into the neuropathology of psychiatric disorders. 6 , 7 , 8 , 9 For instance, the dysfunction of inference-based circuits (i.e., medial OFC, lateral OFC, hippocampus, and amygdala) may result in compulsive behavior 10 and bulimic anorexia nervosa. 11 Therefore, it is a pressing need to comprehensively understand the functional roles of OFC subregions.…”

Section: Introductionmentioning

confidence: 99%

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

Qiu,

Liao

et al. 2024

iScience

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“…The integration of TMS with neuroimaging data, such as fMRI, has advanced our understanding of the functional correlates of TMS by revealing alterations in brain activity/connectivity patterns. This approach helps to utilize between-subject variations in response to stimulation for more precise positioning of the TMS coil over a therapeutic target [31], [32]. Previous TMS studies in SUDs used standard scalp-based targeting methods (e.g., EEG standard system) or neuronavigation systems which ignore the heterogeneity of the employed TMS dosage and functional brain network topography [15].…”

Section: Introductionmentioning

confidence: 99%

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

Soleimani

Conelea

Kuplicki

et al. 2023

Preprint

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Background: Prior research on drug addiction has linked the frontopolar cortex and amygdala coupling to drug cue reactivity/craving. However, one-size-fits-all approaches for transcranial magnetic stimulation (TMS) over frontopolar-amygdala have led to inconsistent results. 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 for maximum electric field (EF) perpendicular to the individualized target, and (3) harmonized EF strength in targeted brain regions across a population. Method: MRI data were collected from 60 participants with methamphetamine use disorders (MUDs). and examined the variability in TMS target location based on task-based connectivity between the frontopolar cortex and amygdala. using psychophysiological interaction (PPI) analysis. EF simulations were calculated for fixed vs. optimized coil location (Fp1/Fp2 vs. individualized maximal PPI), orientation (AF7/AF8 vs. optimization algorithm), and stimulation intensity (constant vs. adjusted intensity across the population). Results: Left medial amygdala with the highest (0.31 +- 0.29) fMRI drug cue reactivity was selected as the subcortical seed region. The location of the voxel with the most positive amygdala-frontopolar PPI connectivity in each participant was considered as the individualized TMS target (MNI coordinates: [12.6,64.23,-0.8] +- [13.64,3.50,11.01]). Individualized frontopolar-amygdala connectivity showed a significant correlation with VAS craving 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 (0.99 +- 0.21V/m) compared to the fixed approach (Fp1:0.56 +- 0.22V/m, Fp2:0.78 +- 0.25V/m) 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 1V/m EF strength in a 5mm sphere around the individualized targets ranged from 0.72-to-2.3 (1.07 +- 0.29). Conclusion: Our results show that optimizing coil orientation and stimulation intensity based on individualized TMS targets led to stronger harmonized electric fields in the targeted brain regions compared to a one-size-fits-all method that hopefully helps to refine future TMS therapy for MUDs.

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Computationally Informed Interventions for Targeting Compulsive Behaviors

Cited by 5 publications

References 154 publications

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

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

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

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

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