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精神分裂症中小世界网络破坏的解剖学见解。

Anatomical insights into disrupted small-world networks in schizophrenia.

机构信息

LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation, the Chinese Academy of Sciences, Beijing, China.

出版信息

Neuroimage. 2012 Jan 16;59(2):1085-93. doi: 10.1016/j.neuroimage.2011.09.035. Epub 2011 Sep 22.

DOI:10.1016/j.neuroimage.2011.09.035
PMID:21963918
Abstract

Schizophrenia is characterized by lowered efficiency in distributed information processing, as indicated by research that identified a disrupted small-world functional network. However, whether the dysconnection manifested by the disrupted small-world functional network is reflected in underlying anatomical disruption in schizophrenia remains unresolved. This study examined the topological properties of human brain anatomical networks derived from diffusion tensor imaging in patients with schizophrenia and in healthy controls. We constructed the weighted brain anatomical network for each of 79 schizophrenia patients and for 96 age and gender matched healthy subjects using diffusion tensor tractography and calculated the topological properties of the networks using a graph theoretical method. The topological properties of the patients' anatomical networks were altered, in that global efficiency decreased but local efficiency remained unchanged. The deleterious effects of schizophrenia on network performance appear to be localized as reduced regional efficiency in hubs such as the frontal associative cortices, the paralimbic/limbic regions and a subcortical structure (the left putamen). Additionally, scores on the Positive and Negative Symptom Scale correlated negatively with efficient network properties in schizophrenia. These findings suggest that complex brain network analysis may potentially be used to detect an imaging biomarker for schizophrenia.

摘要

精神分裂症的特征是分布式信息处理效率降低,这一特征在研究中得到了证实,研究表明其存在功能小世界网络破坏。然而,精神分裂症患者功能小世界网络破坏所表现出的连接中断是否反映在潜在的解剖结构破坏中,这一问题仍未解决。本研究使用扩散张量成像技术,对 79 名精神分裂症患者和 96 名年龄和性别相匹配的健康对照者的大脑解剖网络的拓扑性质进行了研究。我们使用扩散张量追踪技术为每位患者和每位健康对照者构建了加权大脑解剖网络,并使用图论方法计算了网络的拓扑性质。患者解剖网络的拓扑性质发生了改变,全局效率降低,但局部效率保持不变。精神分裂症对网络性能的不良影响似乎是局部的,表现为额联合皮质、边缘/边缘区域和皮质下结构(左侧壳核)等枢纽区域的区域效率降低。此外,阳性和阴性症状量表的评分与精神分裂症患者有效网络特性呈负相关。这些发现表明,复杂脑网络分析可能被用于检测精神分裂症的影像学生物标志物。

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