The diagnosis of schizophrenia is thought to embrace several distinct subgroups. The manifold entities in a single clinical patient group increase the variance of biological measures, deflate the group-level estimates of causal factors, and mask the presence of treatment effects. However, reliable neurobiological boundaries to differentiate these subgroups remain elusive. Since cortical thinning is a well-established feature in schizophrenia, we investigated if individuals (patients and healthy controls) with similar patterns of regional cortical thickness form naturally occurring morphological subtypes. K-means algorithm clustering was applied to regional cortical thickness values obtained from 256 structural MRI scans (179 patients with schizophrenia and 77 healthy controls [HCs]). GAP statistics revealed three clusters with distinct regional thickness patterns. The specific patterns of cortical thinning, clinical characteristics, and cognitive function of each clustered subgroup were assessed. The three clusters based on thickness patterns comprised of a morphologically impoverished subgroup (25% patients, 1% HCs), an intermediate subgroup (47% patients, 46% HCs), and an intact subgroup (28% patients, 53% HCs). The differences of clinical features among three clusters pertained to age-of-onset, N-back performance, duration exposure to treatment, total burden of positive symptoms, and severity of delusions. Particularly, the morphologically impoverished group had deficits in N-back performance and less severe positive symptom burden. The data-driven neuroimaging approach illustrates the occurrence of morphologically separable subgroups in schizophrenia, with distinct clinical characteristics. We infer that the anatomical heterogeneity of schizophrenia arises from both pathological deviance and physiological variance. We advocate using MRI-guided stratification for clinical trials as well as case-control investigations in schizophrenia.

中文翻译：

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精神分裂症的形态学分析：基于MRI的皮层厚度数据的聚类分析。

精神分裂症的诊断被认为包括几个不同的亚组。单个临床患者组中的多个实体增加了生物学指标的差异，缩小了因果关系组水平的估计值，并掩盖了治疗效果的存在。但是，区分这些亚组的可靠神经生物学界限仍然难以捉摸。由于皮层变薄是精神分裂症的一个公认的特征，因此我们调查了具有相似区域皮层厚度模式的个人（患者和健康对照）是否形成自然发生的形态亚型。K-means算法聚类应用于从256次结构MRI扫描（179例精神分裂症患者和77例健康对照[HCs]）获得的区域皮层厚度值。GAP统计数据揭示了三个具有不同区域厚度模式的簇。评估每个聚类亚组的皮质变薄，临床特征和认知功能的具体模式。基于厚度模式的三个聚类包括一个形态贫乏的亚组（25％的患者，HCs为1％），一个中等亚组（47％的患者，HCs为46％）和一个完整的亚组（28％的患者，HCs为53％）。三组患者之间的临床特征差异涉及发病年龄，N背表现，治疗持续时间，阳性症状总负担和妄想严重程度。尤其是，形态上较贫穷的组的N背表现不足，阳性症状负担较轻。数据驱动的神经影像学方法说明了精神分裂症中形态上可分离的亚组的发生，具有独特的临床特征。我们推断精神分裂症的解剖学异质性是由病理偏差和生理差异引起的。我们主张在精神分裂症的临床试验以及病例对照研究中使用MRI指导的分层。