Schizophrenia is associated with heterogeneous clinical symptoms and neuroanatomical alterations. In this work, we aim to disentangle the patterns of neuroanatomical alterations underlying a heterogeneous population of patients using a semi-supervised clustering method. We apply this strategy to a cohort of patients with schizophrenia of varying extends of disease duration, and we describe the neuroanatomical, demographic and clinical characteristics of the subtypes discovered. METHODS We analyze the neuroanatomical heterogeneity of 157 patients diagnosed with Schizophrenia, relative to a control population of 169 subjects, using a machine learning method called CHIMERA. CHIMERA clusters the differences between patients and a demographically-matched population of healthy subjects, rather than clustering patients themselves, thereby specifically assessing disease-related neuroanatomical alterations. Voxel-Based Morphometry was conducted to visualize the neuroanatomical patterns associated with each group. The clinical presentation and the demographics of the groups were then investigated. RESULTS Three subgroups were identified. The first two differed substantially, in that one involved predominantly temporal-thalamic-peri-Sylvian regions, whereas the other involved predominantly frontal regions and the thalamus. Both subtypes included primarily male patients. The third pattern was a mix of these two and presented milder neuroanatomic alterations and comprised a comparable number of men and women. VBM and statistical analyses suggest that these groups could correspond to different neuroanatomical dimensions of schizophrenia. CONCLUSION Our analysis suggests that schizophrenia presents distinct neuroanatomical variants. This variability points to the need for a dimensional neuroanatomical approach using data-driven, mathematically principled multivariate pattern analysis methods, and should be taken into account in clinical studies.

中文翻译：

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半监督机器学习方法揭示精神分裂症的神经解剖学异质性

精神分裂症与异质临床症状和神经解剖学改变有关。在这项工作中，我们的目标是使用半监督聚类方法解开异质患者群体背后的神经解剖学改变模式。我们将此策略应用于一组不同病程持续时间的精神分裂症患者，并描述了所发现亚型的神经解剖学、人口统计学和临床​​特征。方法 我们使用称为 CHIMERA 的机器学习方法分析了 157 名诊断为精神分裂症的患者相对于 169 名受试者的对照人群的神经解剖学异质性。CHIMERA 将患者与人口统计学匹配的健康受试者群体之间的差异聚类，而不是将患者本身聚类，从而专门评估与疾病相关的神经解剖学改变。进行基于体素的形态测量以可视化与每组相关的神经解剖学模式。然后调查了各组的临床表现和人口统计学。结果 确定了三个亚组。前两者有很大不同，其中一个主要涉及颞侧丘脑周围区域，而另一个主要涉及额叶区域和丘脑。两种亚型主要包括男性患者。第三种模式是这两种模式的混合，呈现出较温和的神经解剖学改变，包括相当数量的男性和女性。VBM 和统计分析表明，这些组可能对应于精神分裂症的不同神经解剖学维度。结论 我们的分析表明，精神分裂症呈现出不同的神经解剖学变异。这种可变性表明需要使用数据驱动的、基于数学原理的多变量模式分析方法的三维神经解剖学方法，并且应该在临床研究中加以考虑。