Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Centro de Medicina Nuclear, 3o andar, LIM-21, Rua Dr. Ovídio Pires de Campos, s/n; Postal code 05403-010, São Paulo, SP, Brazil.
Prog Neuropsychopharmacol Biol Psychiatry. 2013 Jun 3;43:116-25. doi: 10.1016/j.pnpbp.2012.12.005. Epub 2012 Dec 20.
Recent neuroanatomical pattern classification studies have attempted to individually classify cases with psychotic disorders using morphometric MRI data in an automated fashion. However, this approach has not been tested in population-based samples, in which variable patterns of comorbidity and disease course are typically found. We aimed to evaluate the diagnostic accuracy (DA) of the above technique to discriminate between incident cases of first-episode schizophrenia identified in a circumscribed geographical region over a limited period of time, in comparison with next-door healthy controls. Sixty-two cases of first-episode schizophrenia or schizophreniform disorder and 62 age, gender and educationally-matched controls underwent 1.5 T MRI scanning at baseline, and were naturalistically followed-up over 1 year. T1-weighted images were used to train a high-dimensional multivariate classifier, and to generate both spatial maps of the discriminative morphological patterns between groups and ROC curves. The spatial map discriminating first-episode schizophrenia patients from healthy controls revealed a complex pattern of regional volumetric abnormalities in the former group, affecting fronto-temporal-occipital gray and white matter regions bilaterally, including the inferior fronto-occipital fasciculus, as well as the third and lateral ventricles. However, an overall modest DA (73.4%) was observed for the individual discrimination between first-episode schizophrenia patients and controls, and the classifier failed to predict 1-year prognosis (remitting versus non-remitting course) of first-episode schizophrenia (DA=58.3%). In conclusion, using a "real world" sample recruited with epidemiological methods, the application of a neuroanatomical pattern classifier afforded only modest DA to classify first-episode schizophrenia subjects and next-door healthy controls, and poor discriminative power to predict the 1-year prognosis of first-episode schizophrenia.
最近的神经解剖模式分类研究试图使用形态磁共振成像(MRI)数据以自动化方式对精神病患者进行个体分类。然而,这种方法尚未在基于人群的样本中进行测试,在基于人群的样本中通常会发现合并症和疾病病程的可变模式。我们旨在评估上述技术的诊断准确性(DA),以区分在有限时间内在限定地理区域内发现的首发精神分裂症的新发病例,与隔壁的健康对照者相比。 62 例首发精神分裂症或精神分裂症样障碍患者和 62 例年龄,性别和教育程度匹配的对照者在基线时接受了 1.5T MRI 扫描,并在 1 年内进行了自然随访。使用 T1 加权图像来训练高维多元分类器,并生成组间鉴别形态模式的空间图和 ROC 曲线。区分首发精神分裂症患者和健康对照组的空间图显示出前者双侧额颞枕叶灰质和白质区域的复杂区域容积异常模式,包括下额枕束以及第三脑室和侧脑室。然而,个体对首发精神分裂症患者和对照组的鉴别观察到的整体适度 DA(73.4%),并且分类器无法预测首发精神分裂症的 1 年预后(缓解与非缓解病程)(DA=58.3%)。总之,使用采用流行病学方法招募的“真实世界”样本,神经解剖模式分类器的应用仅适度 DA 对首发精神分裂症患者和隔壁健康对照组进行分类,并且对首发精神分裂症的 1 年预后的预测能力较差。