NeuroImage. Clinical · 2026
Fronto-striatal neurometabolite signatures of impulsivity in early abstinence from methamphetamine.
СтатьяНазвание и аннотация: Перевод в очереди
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Dumitrescu Alexandru M, Vest M Frances, Loftis Jennifer M, Murnane Kevin S
Аннотация
BACKGROUND: Methamphetamine use is associated with neurometabolite alterations and increased impulsivity, but relationships between neurometabolites and specific impulsivity dimensions remain unclear, particularly during early abstinence when relapse risk is high and recovery is dynamic.
METHODS: We studied 20 recently abstinent individuals and 21 age-matched controls. Single-voxel MRS was acquired in the bilateral dorsolateral prefrontal cortex (DLPFC), medial prefrontal cortex (mPFC), anterior cingulate cortex (ACC), and caudate (CAUD) to quantify N-acetylaspartate (NAA), myo-inositol (mI), choline compounds (GPC + PCh), and glutamate and glutamine (Glu + Gln) concentrations relative to total creatine (Cr + PCr). Group differences in neurometabolite ratios were tested with linear mixed-effects models, and impulsivity was tested across six Barratt Impulsiveness Scale (BIS-11) first-order factors with mixed-design ANCOVA. Regressions assessed whether neurometabolite concentrations predicted impulsivity and whether relationships differed between groups, adjusting for education, depression, anxiety, and stress.
RESULTS: Recently abstinent individuals scored higher on three BIS-11 factors (Motor, Self-Control, Cognitive Complexity). MRS showed elevated mI/Cr + PCr and GPC + PCh/Cr + PCr in the CAUD and increased GPC + PCh/Cr + PCr in the mPFC, while NAA and Glu + Gln ratios did not differ. Motor impulsivity was predicted only by higher stress. Lower self-control was associated with higher CAUD mI/Cr + PCr in age-matched controls but not in recently abstinent individuals. Cognitive Complexity was predicted by group, with no neurometabolite effects.
CONCLUSIONS: Fronto-striatal glial dysregulation contributes to impulsivity in methamphetamine use disorder. Preservation of NAA despite glial changes suggests a distinct neurochemical profile of early abstinence. Stress and glial dysfunction, potentially involving neuroinflammatory processes, may jointly drive impulsivity, highlighting intervention targets. Integrating MRS with behavioral measures provides insight into relapse vulnerability.
METHODS: We studied 20 recently abstinent individuals and 21 age-matched controls. Single-voxel MRS was acquired in the bilateral dorsolateral prefrontal cortex (DLPFC), medial prefrontal cortex (mPFC), anterior cingulate cortex (ACC), and caudate (CAUD) to quantify N-acetylaspartate (NAA), myo-inositol (mI), choline compounds (GPC + PCh), and glutamate and glutamine (Glu + Gln) concentrations relative to total creatine (Cr + PCr). Group differences in neurometabolite ratios were tested with linear mixed-effects models, and impulsivity was tested across six Barratt Impulsiveness Scale (BIS-11) first-order factors with mixed-design ANCOVA. Regressions assessed whether neurometabolite concentrations predicted impulsivity and whether relationships differed between groups, adjusting for education, depression, anxiety, and stress.
RESULTS: Recently abstinent individuals scored higher on three BIS-11 factors (Motor, Self-Control, Cognitive Complexity). MRS showed elevated mI/Cr + PCr and GPC + PCh/Cr + PCr in the CAUD and increased GPC + PCh/Cr + PCr in the mPFC, while NAA and Glu + Gln ratios did not differ. Motor impulsivity was predicted only by higher stress. Lower self-control was associated with higher CAUD mI/Cr + PCr in age-matched controls but not in recently abstinent individuals. Cognitive Complexity was predicted by group, with no neurometabolite effects.
CONCLUSIONS: Fronto-striatal glial dysregulation contributes to impulsivity in methamphetamine use disorder. Preservation of NAA despite glial changes suggests a distinct neurochemical profile of early abstinence. Stress and glial dysfunction, potentially involving neuroinflammatory processes, may jointly drive impulsivity, highlighting intervention targets. Integrating MRS with behavioral measures provides insight into relapse vulnerability.