Brain Sciences Center (11B), Veterans Affairs Medical Center, One Veterans Drive, Minneapolis, MN 55417, USA.
Neuroinformatics. 2011 Dec;9(4):321-33. doi: 10.1007/s12021-010-9094-6.
Making an accurate diagnosis of schizophrenia and related psychoses early in the course of the disease is important for initiating treatment and counseling patients and families. In this study, we developed classification models for early disease diagnosis using structural MRI (sMRI) and neuropsychological (NP) testing. We used sMRI measurements and NP test results from 28 patients with recent-onset schizophrenia and 47 healthy subjects, drawn from the larger sample of the Mind Clinical Imaging Consortium. We developed diagnostic models based on Linear Discriminant Analysis (LDA) following two approaches; namely, (a) stepwise (STP) LDA on the original measurements, and (b) LDA on variables created through Principal Component Analysis (PCA) and selected using the Humphrey-Ilgen parallel analysis. Error estimation of the modeling algorithms was evaluated by leave-one-out external cross-validation. These analyses were performed on sMRI and NP variables separately and in combination. The following classification accuracy was obtained for different variables and modeling algorithms. sMRI only: (a) STP-LDA: 64.3% sensitivity and 76.6% specificity, (b) PCA-LDA: 67.9% sensitivity and 72.3% specificity. NP only: (a) STP-LDA: 71.4% sensitivity and 80.9% specificity, (b) PCA-LDA: 78.5% sensitivity and 91.5% specificity. Combined sMRI-NP: (a) STP-LDA: 64.3% sensitivity and 83.0% specificity, (b) PCA-LDA: 89.3% sensitivity and 93.6% specificity. (i) Maximal diagnostic accuracy was achieved by combining sMRI and NP variables. (ii) NP variables were more informative than sMRI, indicating that cognitive deficits can be detected earlier than volumetric structural abnormalities. (iii) PCA-LDA yielded more accurate classification than STP-LDA. As these sMRI and NP tests are widely available, they can increase accuracy of early intervention strategies and possibly be used in evaluating treatment response.
早期准确诊断精神分裂症及相关精神病对于启动治疗和为患者及家属提供咨询至关重要。本研究使用结构磁共振成像（sMRI）和神经心理学（NP）测试开发了用于早期疾病诊断的分类模型。我们使用来自 Mind Clinical Imaging Consortium 较大样本的 28 名首发精神分裂症患者和 47 名健康对照者的 sMRI 测量值和 NP 测试结果。我们根据线性判别分析（LDA）开发了基于两种方法的诊断模型；即（a）原始测量值的逐步（STP）LDA，以及（b）通过主成分分析（PCA）创建变量并使用 Humphrey-Ilgen 并行分析选择后进行的 LDA。通过留一法外部交叉验证评估建模算法的误差估计。分别和组合 sMRI 和 NP 变量进行了这些分析。不同变量和建模算法的分类准确性如下。仅 sMRI：（a）STP-LDA：64.3%敏感性和 76.6%特异性，（b）PCA-LDA：67.9%敏感性和 72.3%特异性。仅 NP：（a）STP-LDA：71.4%敏感性和 80.9%特异性，（b）PCA-LDA：78.5%敏感性和 91.5%特异性。sMRI-NP 联合：（a）STP-LDA：64.3%敏感性和 83.0%特异性，（b）PCA-LDA：89.3%敏感性和 93.6%特异性。（i）sMRI 和 NP 变量的联合实现了最大诊断准确性。（ii）NP 变量比 sMRI 更具信息量，表明认知缺陷可以比容积结构异常更早检测到。（iii）PCA-LDA 比 STP-LDA 产生更准确的分类。由于这些 sMRI 和 NP 测试广泛可用，它们可以提高早期干预策略的准确性，并可能用于评估治疗反应。
