Park Haeorum, Kim Minchul, Kim Jaejoong, Kim Sunghwan, Jeong Bumseok
Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Department of Radiology, Kangbuk Samsung Hospital, Seoul, Republic of Korea.
Front Neurosci. 2025 Jul 16;19:1592015. doi: 10.3389/fnins.2025.1592015. eCollection 2025.
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 ( = 0.39, = 0.004) and with error sensitivity for gains and losses (≈0.41-0.44, ≤ 0.005); mPFC Glx showed no such relations. AIC Glx fell during gain learning (-2.21%, = 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.
前岛叶皮质（AIC）整合内感受、认知情感和错误监测信号，并且在焦虑和抑郁中持续表现为过度活跃。越来越多的证据表明，额岛叶区域中谷氨酸+谷氨酰胺（Glx）水平升高与应激反应性有关；然而，尚不清楚AIC中的Glx是否与跨诊断的一般精神病理学因素（G评分）相关，或者是否与学习过程中对预测误差的超重倾向相关。因此，我们将功能磁共振波谱（fMRS）与强化学习建模相结合，以测试：（i）AIC的基线Glx是否能预测从患者健康问卷-9（PHQ-9）、广泛性焦虑障碍量表-7（GAD-7）和状态-特质焦虑问卷-X1（STAI-X1）的双因素分析得出的G评分；（ii）任务诱发的Glx变化是否能追踪基于收益和损失学习期间个体在错误敏感性方面的差异。
56名健康成年人（22±2岁，16名女性）完成问卷，并执行双臂赌博任务（先进行40次损失试验，然后进行40次收益试验），同时在静息状态和每个试验块期间从AIC和内侧前额叶皮质（mPFC）采集单体素半激光光谱。将六种Rescorla-Wagner变体拟合到选择结果中；最佳模型（基于最低的留一法信息准则）包括错误敏感性、决策温度和价值衰减。使用LCModel对Glx（Cramer-Rao下限<20%）进行定量，并通过重复测量方差分析和Bonferroni校正的相关性进行分析；使用Baron-Kenny步骤评估中介效应（α = 0.05）。
AIC的基线Glx与G评分相关（r = 0.39，p = 0.004），并且与收益和损失的错误敏感性相关（≈0.41 - 0.44，p≤0.005）；mPFC的Glx则无此类关系。AIC的Glx在收益学习期间下降（-2.21%，p = 0.034），并且在任务后仍保持较低水平，而mPFC的Glx则无变化。错误敏感性完全介导了AIC - Glx/G评分之间的联系；这些关联特定于Glx，而非其他代谢物。
AIC中较高的兴奋性基调似乎会扩大预测误差权重，进而放大焦虑 - 抑郁的共同维度。奖励学习期间Glx的动态降低表明，急性代谢需求叠加在一种类似特质的基线上，这种基线会使认知产生偏差。因此，通过药理学或神经调节靶向岛叶谷氨酸能功能可能会减轻内化精神病理学基础的适应不良错误处理。
