Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany.
Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia.
Mol Psychiatry. 2023 May;28(5):2008-2017. doi: 10.1038/s41380-023-02069-0. Epub 2023 May 5.
Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups-a 'lower brain volume' subgroup (SG1) and an 'higher striatal volume' subgroup (SG2) with otherwise normal brain structure. In this study, we investigated whether the MRI signatures of these subgroups were also already present at the time of the first-episode of psychosis (FEP) and whether they were related to clinical presentation and clinical remission over 1-, 3-, and 5-years. We included 572 FEP and 424 healthy controls (HC) from 4 sites (Sao Paulo, Santander, London, Melbourne) of the PHENOM consortium. Our prior MRI subgrouping models (671 participants; USA, Germany, and China) were applied to both FEP and HC. Participants were assigned into 1 of 4 categories: subgroup 1 (SG1), subgroup 2 (SG2), no subgroup membership ('None'), and mixed SG1 + SG2 subgroups ('Mixed'). Voxel-wise analyses characterized SG1 and SG2 subgroups. Supervised machine learning analyses characterized baseline and remission signatures related to SG1 and SG2 membership. The two dominant patterns of 'lower brain volume' in SG1 and 'higher striatal volume' (with otherwise normal neuromorphology) in SG2 were identified already at the first episode of psychosis. SG1 had a significantly higher proportion of FEP (32%) vs. HC (19%) than SG2 (FEP, 21%; HC, 23%). Clinical multivariate signatures separated the SG1 and SG2 subgroups (balanced accuracy = 64%; p < 0.0001), with SG2 showing higher education but also greater positive psychosis symptoms at first presentation, and an association with symptom remission at 1-year, 5-year, and when timepoints were combined. Neuromorphological subtypes of schizophrenia are already evident at illness onset, separated by distinct clinical presentations, and differentially associated with subsequent remission. These results suggest that the subgroups may be underlying risk phenotypes that could be targeted in future treatment trials and are critical to consider when interpreting neuroimaging literature.
使用机器学习,我们最近将已建立的精神分裂症的神经解剖学异质性分解为两个体积亚组 - “脑体积较低”亚组(SG1)和“纹状体体积较高”亚组(SG2),而大脑结构正常。在这项研究中,我们调查了这些亚组的 MRI 特征是否在精神病首次发作(FEP)时已经存在,以及它们是否与 1、3 和 5 年内的临床表现和临床缓解相关。我们纳入了来自 PHENOM 联合会的 4 个地点(圣保罗、桑坦德、伦敦、墨尔本)的 572 名 FEP 和 424 名健康对照者(HC)。我们之前的 MRI 亚组模型(671 名参与者;美国、德国和中国)应用于 FEP 和 HC。参与者被分配到以下 4 个类别之一:亚组 1(SG1)、亚组 2(SG2)、无亚组归属(“无”)和混合 SG1+SG2 亚组(“混合”)。体素分析特征描述了 SG1 和 SG2 亚组。监督机器学习分析特征描述了与 SG1 和 SG2 成员资格相关的基线和缓解特征。在精神病首次发作时,已经确定了 SG1 的“脑体积较低”和 SG2 的“纹状体体积较高”(具有正常的神经形态)这两种主要模式。SG1 的 FEP 比例明显高于 HC(32%比 19%),而 SG2 的 FEP 比例为 21%,HC 为 23%。临床多元特征分离了 SG1 和 SG2 亚组(平衡准确率=64%;p<0.0001),SG2 的教育程度较高,但首次出现阳性精神病症状也较高,与 1 年、5 年和合并时的症状缓解相关。精神分裂症的神经形态亚型在疾病发作时已经明显,由不同的临床表现分开,并与随后的缓解程度不同相关。这些结果表明,这些亚组可能是潜在的风险表型,可以在未来的治疗试验中作为目标,在解释神经影像学文献时也至关重要。