Background and Hypothesis Given the heterogeneity and possible disease progression in schizophrenia, identifying the neurobiological subtypes and progression patterns in each patient may lead to novel biomarkers. Here, we adopted data-driven machine-learning techniques to identify the progression patterns of brain morphological changes in schizophrenia and investigate the association with treatment resistance. Study Design In this cross-sectional multicenter study, we included 177 patients with schizophrenia, characterized by treatment response or resistance, with 3D T1-weighted magnetic resonance imaging. Cortical thickness and subcortical volumes calculated by FreeSurfer were converted into z scores using 73 healthy controls data. The Subtype and Stage Inference (SuStaIn) algorithm was used for unsupervised machine-learning analysis. Study Results SuStaIn identified 3 different subtypes: (1) subcortical volume reduction (SC) type (73 patients), in which volume reduction of subcortical structures occurs first and moderate cortical thinning follows, (2) globus pallidus hypertrophy and cortical thinning (GP-CX) type (42 patients), in which globus pallidus hypertrophy initially occurs followed by progressive cortical thinning, and (3) cortical thinning (pure CX) type (39 patients), in which thinning of the insular and lateral temporal lobe cortices primarily happens. The remaining 23 patients were assigned to baseline stage of progression (no change). SuStaIn also found 84 stages of progression, and treatment-resistant schizophrenia showed significantly more progressed stages than treatment-responsive cases (P = .001). The GP-CX type presented earlier stages than the pure CX type (P = .009). Conclusions The brain morphological progressions in schizophrenia can be classified into 3 subtypes, and treatment resistance was associated with more progressed stages, which may suggest a novel biomarker.

中文翻译：

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精神分裂症脑形态学的疾病进展模式：治疗抵抗的更多进展阶段

背景和假设鉴于精神分裂症的异质性和可能的​​疾病进展，识别每个患者的神经生物学亚型和进展模式可能会产生新的生物标志物。在这里，我们采用数据驱动的机器学习技术来识别精神分裂症大脑形态变化的进展模式，并研究与治疗抵抗的关联。研究设计 在这项横断面多中心研究中，我们纳入了 177 名精神分裂症患者，其特征是通过 3D T1 加权磁共振成像对治疗有反应或耐药。使用 73 个健康对照数据，将 FreeSurfer 计算出的皮质厚度和皮质下体积转换为 z 分数。子类型和阶段推理 (SuStaIn) 算法用于无监督机器学习分析。研究结果 SuStaIn 确定了 3 种不同的亚型：(1) 皮质下体积减少 (SC) 型（73 名患者），其中皮质下结构首先发生体积减少，随后出现中度皮质变薄，(2) 苍白球肥大和皮质变薄 (GP- CX）型（42 名患者），其中最初发生苍白球肥大，随后进行性皮质变薄，以及（3）皮质变薄（纯 CX）型（39 名患者），其中主要发生岛叶和外侧颞叶皮质变薄。其余 23 名患者被分配至基线进展阶段（无变化）。SuStaIn 还发现了 84 个进展阶段，难治性精神分裂症的进展阶段明显多于治疗有效病例 (P = .001)。GP-CX 型比纯 CX 型出现更早的阶段 (P = .009)。结论 精神分裂症的脑形态进展可分为 3 个亚型，治疗抵抗与更多的进展阶段相关，这可能暗示了一种新的生物标志物。