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Maximum-uncertainty linear discrimination analysis of first-episode schizophrenia subjects

机译：首发精神分裂症患者的最大不确定度线性判别分析

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摘要

Recent techniques of image analysis brought the possibility to recognize subjects based on discriminative image features. We performed a magnetic resonance imaging (MRI)-based classification study to assess its usefulness for outcome prediction of first-episode schizophrenia patients (FES). We included 39 FES patients and 39 healthy controls (HC) and performed the maximum-uncertainty linear discrimination analysis (MLDA) of MRI brain intensity images. The classification accuracy index (CA) was correlated with the Positive and Negative Syndrome Scale (PANSS) and the Global Assessment of Functioning scale (GAF) at 1-year follow-up. The rate of correct classifications of patients with poor and good outcomes was analyzed using chi-square tests. MLDA classification was significantly better than classification by chance. Leave-one-out accuracy was 72%. CA correlated significantly with PANSS and GAF scores at the 1-year follow-up. Moreover, significantly more patients with poor outcome than those with good outcome were classified correctly. MLDA of brain MR intensity features is, therefore, able to correctly classify a significant number of FES patients, and the discriminative features are clinically relevant for clinical presentation 1 year after the first episode of schizophrenia. The accuracy of the current approach is, however, insufficient to be used in clinical practice immediately. Several methodological issues need to be addressed to increase the usefulness of this classification approach.
机译：图像分析的最新技术带来了基于判别性图像特征识别对象的可能性。我们进行了基于磁共振成像（MRI）的分类研究，以评估其对首发精神分裂症患者（FES）的结局预测的有用性。我们纳入了39名FES患者和39名健康对照（HC），并进行了MRI脑强度图像的最大不确定度线性判别分析（MLDA）。在1年的随访中，分类准确度指数（CA）与阳性和阴性综合症量表（PANSS）和整体功能评估量表（GAF）相关。使用卡方检验分析了不良结局和良好结局的正确分类率。 MLDA分类明显优于偶然分类。留一法的准确性为72％。在1年的随访中，CA与PANSS和GAF得分显着相关。此外，正确分类的结果差的患者要多于好结果的患者。因此，脑部MR强度特征的MLDA能够正确分类大量的FES患者，并且该特征与精神分裂症首发1年后的临床表现有关。但是，当前方法的准确性不足以立即用于临床实践。为了提高这种分类方法的实用性，需要解决一些方法学问题。

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