Yoon Uicheul, Lee Jong-Min, Im Kiho, Shin Yong-Wook, Cho Baek Hwan, Kim In Young, Kwon Jun Soo, Kim Sun I
Department of Biomedical Engineering, Hanyang University, Sungdong PO Box 55, Seoul 133-605, Korea.
Neuroimage. 2007 Feb 15;34(4):1405-15. doi: 10.1016/j.neuroimage.2006.11.021. Epub 2006 Dec 26.
We proposed pattern classification based on principal components of cortical thickness between schizophrenic patients and healthy controls, which was trained using a leave-one-out cross-validation. The cortical thickness was measured by calculating the Euclidean distance between linked vertices on the inner and outer cortical surfaces. Principal component analysis was applied to each lobe for practical computational issues and stability of principal components. And, discriminative patterns derived at every vertex in the original feature space with respect to support vector machine were analyzed with definitive findings of brain abnormalities in schizophrenia for establishing practical confidence. It was simulated with 50 randomly selected validation set for the generalization and the average accuracy of classification was reported. This study showed that some principal components might be more useful than others for classification, but not necessarily matching the ordering of the variance amounts they explained. In particular, 40-70 principal components rearranged by a simple two-sample t-test which ranked the effectiveness of features were used for the best mean accuracy of simulated classification (frontal: (left(%)|right(%))=91.07|88.80, parietal: 91.40|91.53, temporal: 93.60|91.47, occipital: 88.80|91.60). And, discriminative power appeared more spatially diffused bilaterally in the several regions, especially precentral, postcentral, superior frontal and temporal, cingulate and parahippocampal gyri. Since our results of discriminative patterns derived from classifier were consistent with a previous morphological analysis of schizophrenia, it can be said that the cortical thickness is a reliable feature for pattern classification and the potential benefits of such diagnostic tools are enhanced by our finding.
我们提出了基于精神分裂症患者与健康对照者皮质厚度主成分的模式分类方法，并采用留一法交叉验证进行训练。通过计算皮质内、外表面相连顶点之间的欧几里得距离来测量皮质厚度。由于实际计算问题和主成分的稳定性，对每个脑叶进行主成分分析。此外，针对支持向量机，分析了原始特征空间中每个顶点的判别模式，并结合精神分裂症明确的脑异常发现来建立实际可信度。使用50个随机选择的验证集进行模拟以评估泛化能力，并报告分类的平均准确率。本研究表明，某些主成分在分类中可能比其他主成分更有用，但不一定与它们所解释的方差量的排序相匹配。特别是，通过简单的双样本t检验重新排列的40 - 70个主成分（对特征有效性进行排序）用于模拟分类的最佳平均准确率（额叶：（左（%）|右（%））=91.07|88.80，顶叶：91.40|91.53，颞叶：93.60|91.47，枕叶：88.80|91.60）。而且，判别能力在几个区域双侧的空间分布更广泛，特别是中央前回、中央后回、额上回和颞叶、扣带回和海马旁回。由于我们从分类器得出的判别模式结果与先前对精神分裂症的形态学分析一致，可以说皮质厚度是模式分类的可靠特征，并且我们的发现增强了这种诊断工具的潜在益处。
