Although regional brain deficits have been demonstrated in schizophrenia patients by structural MRI studies, one important question that remains largely unanswered is whether the complex and subtle deficits revealed by MRI could be used as objective biomarkers to discriminate patients from healthy controls individually. To address this question, a total of 326 right-handed participants were recruited, including 163 drug-naïve first-episode schizophrenia (FES) patients and 163 demographically matched healthy controls. High-resolution anatomic data were acquired from all subjects and processed via Freesurfer software to obtain cortical thickness and surface area measurements. Subsequently, the Support Vector Machine (SVM) was used to explore the potential utility for cortical thickness and surface area measurements in the differentiation of individual patients and healthy controls. The accuracy of correct classification of patients and controls was 85.0% (specificity 87.0%, sensitivity 83.0%) for surface area and 81.8% (specificity 85.0%, sensitivity 76.9%) for cortical thickness (p<0.001 after permutation testing). Regions contributing to classification accuracy mainly included the gray matter in default mode, central executive, salience, and visual networks. Current findings, in a sample of never-treated FES patients, suggest that the patterns of illness-related gray matter changes has potential as a biomarker for identifying structural brain alterations in individuals with schizophrenia. Future prospective studies are needed to evaluate the utility of imaging biomarkers for research and potentially for clinical purpose.

中文翻译：

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使用结构 MRI 基于支持向量机对初次用药的精神分裂症患者和健康对照者进行分类

尽管结构性 MRI 研究已经证明精神分裂症患者存在区域性大脑缺陷，但一个重要的问题仍未得到解答，即 MRI 揭示的复杂而微妙的缺陷是否可以用作客观的生物标志物，将患者与健康对照者区分开来。为了解决这个问题，共招募了 326 名惯用右手的参与者，包括 163 名未服用药物的首发精神分裂症 (FES) 患者和 163 名人口统计学匹配的健康对照。从所有受试者获取高分辨率解剖数据并通过 Freesurfer 软件处理以获得皮质厚度和表面积测量值。随后，支持向量机 (SVM) 用于探索皮层厚度和表面积测量在区分个体患者和健康对照方面的潜在效用。患者和对照正确分类的准确率为表面积的 85.0%（特异性 87.0%，敏感性 83.0%）和皮质厚度的 81.8%（特异性 85.0%，敏感性 76.9%）（置换测试后 p<0.001）。有助于分类准确度的区域主要包括默认模式下的灰质、中央执行器、显着性和视觉网络。目前的发现，在从未接受过治疗的 FES 患者样本中，表明与疾病相关的灰质变化模式有可能作为识别精神分裂症患者大脑结构改变的生物标志物。