Anterior insular cortex glutamate-glutamine (Glx) levels predict general psychopathology via heightened error sensitivity
IntroductionThe 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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2025.1592015/full |
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| Summary: | IntroductionThe 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.MethodsFifty-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).ResultsBaseline 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.DiscussionHigher 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. |
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| ISSN: | 1662-453X |