# Anterior insular cortex glutamate-glutamine (Glx) levels predict general psychopathology via heightened error sensitivity

## TL;DR

Higher levels of glutamate-glutamine in the anterior insular cortex are linked to general psychopathology and how people process errors during learning.

## Contribution

This study is the first to show that AIC Glx levels predict a transdiagnostic psychopathology factor through heightened error sensitivity.

## Key findings

- Baseline AIC Glx levels correlated with a general psychopathology score and error sensitivity during learning.
- AIC Glx decreased during reward learning, suggesting acute metabolic demand.
- Error sensitivity fully mediated the link between AIC Glx and psychopathology.

## Abstract

The anterior insular cortex (AIC) integrates interoceptive, cognitive-emotional, and error-monitoring signals, and is consistently hyperactive in anxiety and depression. Converging evidence links elevated glutamate + glutamine (Glx) in fronto-insular regions to stress reactivity; however, it is unknown whether AIC Glx relates to a transdiagnostic general psychopathology factor (G-score) or to the tendency to overweight prediction errors during learning. We therefore combined functional MRS (fMRS) with reinforcement-learning modeling to test whether (i) baseline AIC Glx predicts the G-score derived from bifactor analysis of PHQ-9, GAD-7, and STAI-X1, and (ii) task-evoked Glx changes track individual differences in error sensitivity during gain- and loss-based learning.

Fifty-six healthy adults (22 ± 2 yr, 16 women) completed the questionnaires and performed a two-armed bandit task (40 loss then 40 gain trials) while single-voxel semi-LASER spectra were acquired from AIC and medial prefrontal cortex (mPFC) at rest and during each block. Six Rescorla-Wagner variants were fitted to the choices; the best model (based on the lowest LOOIC) included error sensitivity, decision temperature, and value decay. Glx (CRLB < 20%) was quantified using LCModel and analyzed with repeated-measures ANOVA and Bonferroni-corrected correlations; mediation was assessed using Baron-Kenny steps (α = 0.05).

Baseline AIC Glx correlated with the G-score (r = 0.39, p = 0.004) and with error sensitivity for gains and losses (r≈0.41–0.44, p ≤ 0.005); mPFC Glx showed no such relations. AIC Glx fell during gain learning (−2.21%, p = 0.034) and remained low post-task, whereas mPFC Glx was unchanged. Error sensitivity fully mediated the AIC-Glx/G-score link; associations were specific to Glx, not other metabolites.

Higher excitatory tone in the AIC appears to enlarge prediction-error weighting, which in turn amplifies a shared anxiety-depression dimension. Dynamic Glx reductions during reward learning suggest acute metabolic demand superimposed on a trait-like baseline that biaes cognition. Targeting insular glutamatergic function–pharmacologically or via neuromodulation–may therefore mitigate maladaptive error processing that underlies internalizing psychopathology.