Biomedical Engineering Laboratory, School of Geosciences and Info-Physics, Central South University, Changsha, Hunan, 410083, Peoples Republic of China.
Biomed Eng Online. 2012 Aug 16;11:50. doi: 10.1186/1475-925X-11-50.
Schizophrenia is a severe mental illness associated with the symptoms such as hallucination and delusion. The objective of this study was to investigate the abnormal resting-state functional connectivity patterns of schizophrenic patients which could identify furthest patients from healthy controls.
The whole-brain resting-state fMRI was performed on patients diagnosed with schizophrenia (n = 22) and on age- and gender-matched, healthy control subjects (n = 22). To differentiate schizophrenic individuals from healthy controls, the multivariate classification analysis was employed. The weighted brain regions were got by reconstruction arithmetic to extract highly discriminative functional connectivity information.
The results showed that 93.2% (p < 0.001) of the subjects were correctly classified via the leave-one-out cross-validation method. And most of the altered functional connections identified located within the visual cortical-, default-mode-, and sensorimotor network. Furthermore, in reconstruction arithmetic, the fusiform gyrus exhibited the greatest amount of weight.
This study demonstrates that schizophrenic patients may be successfully differentiated from healthy subjects by using whole-brain resting-state fMRI, and the fusiform gyrus may play an important functional role in the physiological symptoms manifested by schizophrenic patients. The brain region of great weight may be the problematic region of information exchange in schizophrenia. Thus, our result may provide insights into the identification of potentially effective biomarkers for the clinical diagnosis of schizophrenia.
精神分裂症是一种严重的精神疾病，其症状包括幻觉和妄想等。本研究旨在探讨精神分裂症患者的静息态功能连接模式异常，以进一步将患者与健康对照组区分开来。
对 22 名被诊断为精神分裂症的患者和 22 名年龄和性别匹配的健康对照者进行全脑静息态 fMRI 检查。采用多元分类分析将精神分裂症个体与健康对照者区分开来。通过重建算法获得加权脑区，以提取高度有区别的功能连接信息。
通过留一法交叉验证，93.2%（p<0.001）的被试者得到正确分类。大多数识别出的功能连接改变位于视觉皮质、默认模式和感觉运动网络内。此外，在重建算法中，梭状回的权重最大。
本研究表明，使用全脑静息态 fMRI 可以成功地区分精神分裂症患者和健康受试者，梭状回可能在精神分裂症患者表现出的生理症状中发挥重要的功能作用。权重较大的脑区可能是精神分裂症中信息交换出现问题的区域。因此，我们的研究结果可能为识别精神分裂症潜在的有效临床诊断生物标志物提供新的思路。
