Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China.
Schizophr Bull. 2022 Nov 18;48(6):1354-1362. doi: 10.1093/schbul/sbac094.
Previous studies have reported effects of antipsychotic treatment and illness duration on brain features. This study used a machine learning approach to examine whether these factors in aggregate impacted the utility of MRI features for differentiating individual schizophrenia patients from healthy controls.
This case-control study used patients with never-treated first-episode schizophrenia (FES, n = 179) and long-term ill schizophrenia (LTSZ, n = 30), with follow-up of the FES group after treatment (n = 71), a group of patients who had received long-term antipsychotic treatment (n = 93) and age and sex-matched healthy controls (n = 373) for each patient group. A multiple kernel learning classifier combining both structural and functional brain features was used to discriminate individual patients from controls.
MRI features differentiated untreated FES (0.73) and LTSZ (0.83) patients from healthy controls with moderate accuracy, but accuracy was significantly higher in antipsychotic-treated FES (0.94) and LTSZ (0.98) patients. Treatment was associated with significantly increased accuracy of case identification in both early course and long-term ill patients (both p < .001). Effects of illness duration, examined separately in treated and untreated patients, were less robust.
Our results demonstrate that initiation of antipsychotic treatment alters brain features in ways that further distinguish individual schizophrenia patients from healthy individuals, and have a modest effect of illness duration. Intrinsic illness-related brain alterations in untreated patients, regardless of illness duration, are not sufficiently robust for accurate identification of schizophrenia patients.
先前的研究报告了抗精神病药物治疗和疾病持续时间对大脑特征的影响。本研究采用机器学习方法来检查这些因素综合起来是否会影响 MRI 特征在区分个体精神分裂症患者与健康对照者方面的效用。
本病例对照研究使用了未经治疗的首发精神分裂症（FES，n = 179）和长期患病的精神分裂症（LTSZ，n = 30）患者，对 FES 组进行了治疗后的随访（n = 71），一组接受了长期抗精神病药物治疗的患者（n = 93）和年龄、性别匹配的健康对照组（n = 373）。使用结合结构和功能脑特征的多核学习分类器来区分个体患者和对照者。
MRI 特征以中等准确度区分了未经治疗的 FES（0.73）和 LTSZ（0.83）患者与健康对照者，但接受抗精神病药物治疗的 FES（0.94）和 LTSZ（0.98）患者的准确度明显更高。治疗与早期和长期患病患者的病例识别准确率显著提高（均 p <.001）。在治疗和未治疗的患者中分别检查疾病持续时间的影响，其效果不那么显著。
我们的研究结果表明，抗精神病药物治疗的开始以进一步将个体精神分裂症患者与健康个体区分开来的方式改变了大脑特征，并且对疾病持续时间有适度的影响。未经治疗的患者中与疾病相关的内在大脑改变，无论疾病持续时间如何，都不足以准确识别精神分裂症患者。
