Treatment resistant (TR) psychosis is considered to be a significant cause of disability and functional impairment. Numerous efforts have been made to identify the clinical predictors of TR. However, the exploration of molecular and biological markers is still at an early stage. To understand the TR condition and identify potential molecular and biological markers, we analyzed demographic information, clinical data, structural brain imaging data, and molecular brain imaging data in 7 Tesla magnetic resonance spectroscopy from a first episode psychosis cohort that includes 136 patients. Age, gender, race, smoking status, duration of illness, and antipsychotic dosages were controlled in the analyses. We found that TR patients had a younger age at onset, more hospitalizations, more severe negative symptoms, a reduction in the volumes of the hippocampus (HP) and superior frontal gyrus (SFG), and a reduction in glutathione (GSH) levels in the anterior cingulate cortex (ACC), when compared to non-TR patients. The combination of multiple markers provided a better classification between TR and non-TR patients compared to any individual marker. Our study shows that ACC-GSH, HP and SFG volumes, and age at onset, could potentially be biomarkers for TR diagnosis, while hospitalization and negative symptoms could be used to evaluate the progression of the disease. Multimodal cohorts are essential in obtaining a comprehensive understanding of brain disorders.

治疗抵抗 (TR) 精神病被认为是残疾和功能障碍的重要原因。已经做出许多努力来确定 TR 的临床预测因子。然而，分子和生物标记的探索仍处于早期阶段。为了解 TR 状况并识别潜在的分子和生物标志物，我们分析了包括 136 名患者在内的首发精神病队列的人口统计信息、临床数据、结构性脑成像数据和 7 特斯拉磁共振波谱中的分子脑成像数据。分析中控制了年龄、性别、种族、吸烟状况、病程和抗精神病药剂量。我们发现 TR 患者发病年龄更小，住院次数更多，阴性症状更严重，与非 TR 患者相比，海马体 (HP) 和额上回 (SFG) 的体积减少，前扣带皮层 (ACC) 中的谷胱甘肽 (GSH) 水平降低。与任何单个标记相比，多个标记的组合提供了 TR 和非 TR 患者之间更好的分类。我们的研究表明，ACC-GSH、HP 和 SFG 体积以及发病年龄可能是 TR 诊断的潜在生物标志物，而住院和阴性症状可用于评估疾病的进展。多模式队列对于全面了解脑部疾病至关重要。与任何单个标记相比，多个标记的组合提供了 TR 和非 TR 患者之间更好的分类。我们的研究表明，ACC-GSH、HP 和 SFG 体积以及发病年龄可能是 TR 诊断的潜在生物标志物，而住院和阴性症状可用于评估疾病的进展。多模式队列对于全面了解脑部疾病至关重要。与任何单个标记相比，多个标记的组合提供了 TR 和非 TR 患者之间更好的分类。我们的研究表明，ACC-GSH、HP 和 SFG 体积以及发病年龄可能是 TR 诊断的潜在生物标志物，而住院和阴性症状可用于评估疾病的进展。多模式队列对于全面了解脑部疾病至关重要。