Multivariate voxel-based morphometry successfully differentiates schizophrenia patients from healthy controls
Multivariate voxel-based morphometry successfully differentiates schizophrenia patients from healthy controls
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DOI：
10.1016/j.neuroimage.2006.08.018
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发表时间：
2007-01-01
期刊：
影响因子：
5.7
通讯作者：
Kurachi, Masayoshi
中科院分区：
文献类型：
--
作者：
Kawasaki, Yasuhiro;Suzuki, Michio;Kurachi, Masayoshi
目前可用的实验室程序可能会提供更多的信息，精神病诊断系统更有效的精神障碍分类。为了确定最能区分精神分裂症患者和健康受试者的灰质分布的相关模式，我们采用了多元线性模型和基于体素的形态计量学的判别函数分析技术。进行第一次分析，以获得一个统计模型，该模型对30名男性健康受试者和30名男性精神分裂症患者进行了分类，这些患者根据当前的操作标准进行了诊断。进行第二次分析，通过成功分类由16名男性健康受试者和16名男性精神分裂症患者组成的新队列，前瞻性验证统计模型。如果模式表达的个体轮廓可以与每个受试者的具体诊断相联系，则可以推断灰质分布的结构相关性。结果是，90%的受试者被正确分类的本征图像，刀切法显示远高于机会的准确性。本征图像的模式的特点是正负荷，表明患者的外侧和内侧前额叶区，颞叶，外侧颞区，内侧颞结构，和丘脑的灰质下降，以及负负荷，反映患者的壳核和小脑的灰质增加。当将来自原始队列的特征图像应用于对来自第二队列的数据进行分类时，它正确地分配了超过80%的健康受试者和精神分裂症患者。这些结果表明，特征性分布的灰质改变可能是精神分裂症的诊断价值。(c)2006年爱思唯尔公司All rights reserved.
Currently available laboratory procedures might provide additional information to psychiatric diagnostic systems for more valid classifications of mental disorders. To identify the correlative pattern of gray matter distribution that best discriminates schizophrenia patients from healthy subjects, we applied discriminant function analysis techniques using the multivariate linear model and the voxel-based morphometry. The first analysis was conducted to obtain a statistical model that classified 30 male healthy subjects and 30 male schizophrenia patients diagnosed according to current operational criteria. The second analysis was performed to prospectively validate the statistical model by successfully classifying a new cohort that consisted of 16 male healthy subjects and 16 male schizophrenia patients. Inferences about the structural relevance of the gray matter distribution could be made if the individual profile of pattern expression could be linked to the specific diagnosis of each subject. The result was that 90% of the subjects were correctly classified by the eigenimage, and the Jackknife approach revealed well above chance accuracy. The pattern of the eigenimage was characterized by positive loadings indicating gray matter decline in the patients in the lateral and medial prefrontal regions, insula, lateral temporal regions, medial temporal structures, and thalamus as well as the negative loadings reflecting gray matter increase in the patients in the putamen and cerebellum. When the eigenimage derived from the original cohort was applied to classify data from the second cohort, it correctly assigned more than 80% of the healthy subjects and schizophrenia patients. These findings suggest that the characteristic distribution of gray matter changes may be of diagnostic value for schizophrenia. (c) 2006 Elsevier Inc. All rights reserved.