Computational mechanisms underlying multi-step planning deficits in methamphetamine use disorder

Abstract

Current theories suggest individuals with methamphetamine use disorder (iMUDs) have difficulty considering long-term outcomes in decision-making, which could contribute to risk of relapse. Aversive interoceptive states (e.g., stress, withdrawal) are also known to increase this risk. The present study analyzed computational mechanisms of planning in iMUDs, and examined the potential impact of an aversive interoceptive state induction. A group of 40 iMUDs and 49 healthy participants completed two runs of a multi-step planning task, with and without an anxiogenic breathing resistance manipulation. Computational modeling revealed that iMUDs had selective difficulty identifying the best overall plan when this required enduring negative short-term outcomes - a mechanism referred to as aversive pruning. Increases in reported craving before and after the induction also predicted greater aversive pruning in iMUDs. These results highlight aversive pruning deficits as a novel mechanism that could promote poor choice in recovering iMUDs and create vulnerability to relapse.

Fig. 1. Study equipment, task interface, and computational model.

a Equipment used for anxiety induction: silicon mask with adjustable straps and single breathing port; resistors used to create resistance during inhalation and induce anxiety; two-way valve connected to the mask, which ensures that inhalations engage one port while exhalations engage the other; tube connecting two-way valve to resistor. b Graphical interface of the Planning Task. The blue button on the button box (center right) corresponds to transitions with blue arrows and the yellow button corresponds to transitions with yellow arrows. c Computational model of (1) path valuation, (2) the probability of selecting a particular action sequence, and (3) calculation of AP. Note that AP = π, NLL-discounting = 1-γ_G, and LL-discounting = 1-γ_S. d Example decision tree based on an example starting position with point values for transitions and final path points demonstrating aversive pruning. Points for the optimal path and the second-best path (indicated with thicker connecting lines) are shown in green and red, respectively. Colors of connections indicate whether the move was performed using the left (blue) button or the right (yellow) button.

Fig. 2. Anxiety induction efficacy.

Top: Boxplots (median and quartiles) for self-reported anxiety ratings across the resistance sensitivity protocol (scale 0–10). Anxiety for iMUDs (n = 40) was higher than HCs (n = 49; F(1,101) = 14.21, p < 0.001, ${\eta }_p^2=0.12$), and anxiety increased as a function of inspiratory resistance level (F(1,977) = 710.53, p < 0.001, ${\eta }_p^2=0.42$; b = 0.644). Bottom: Boxplots for self-reported anxiety (scale 0–10) and State-Trait Anxiety Inventory (STAI; [35]) State ratings (scale 0-80) from pre- to post-task for runs with and without the breathing resistance. Again, anxiety was generally higher in iMUDs (Fs > 14.39, ps < 0.001) and also increased with resistance level (Fs > 4.50, ps < 0.001). Stars indicate significant differences in post-hoc comparisons between groups at each resistance level (top) or time point (bottom). *p < 0.05,**p < 0.01,***p < 0.001.

Fig. 3. Computational parameters and model-free metrics of behavior.

a Raincloud plots showing distributions for each model parameter by group and resistance condition as well as individual data points connected by thin lines and group means and standard errors depicted by thick lines and confidence ribbons (iMUDs: n = 40; HCs: n = 49). Independent of resistance level, iMUDs had larger AP estimates (F(1,100) = 16.46, p < 0.001, ${\eta }_p^2=0.14$) and larger LL-discounting estimates (F(1,100) = 13.45, p < 0.001, ${\eta }_p^2=0.12$) than HCs. b Means and standard errors for choice accuracy, which differed by group in trials where the optimal path included large losses (OLL trials; F(1,490) = 25.30, p < 0.001, ${\eta }_p^2=0.05$). This was driven by differences at depths 3 and 4. Stars indicate significant effects. LL large loss, NLL no large loss. *p < 0.05, **p < 0.01, ***p < 0.001.

Fig. 4. Correlations between computational measures and other measures of interest.

a Correlations between model parameters and model-free task metrics across groups. b Inter-correlations between model parameters. c Correlations between model parameters and covariates (statistics shown for sex represent t-values from independent samples t-tests, where the negative direction indicates higher values in male participants). All relationships are shown separately for parameters under the no resistance (top) and resistance (bottom) conditions. OLL = trials in which the optimal path contains a large loss, ONLL = trials in which the optimal path does not contain a large loss. RS Reward Sensitivity, NLL_d = ONLL Path Discounting Probability, LL_d = OLL Path Discounting Probability, AP Aversive Pruning. *p < 0.05, **p < 0.01, ***p < 0.001.

Fig. 5. Mediation model indicating that cognitive reflectiveness partially accounted for group differences in aversive pruning.

Graphical depiction of results indicating that lower cognitive reflectiveness levels (CRT scores) partially accounted for greater aversive pruning (AP) in iMUDs compared to HCs (available data: n = 89). Note that the relationship shown between group and CRT accounts for age and sex.