多变量模式分析在反社会人格障碍中的应用

Application of Multivariate Pattern Analysis in Antisocial Personality Disorder

从静息态功能连接角度使用多变量模式,研究了反社会人格障碍(ASPD)脑网络的异常连接模式。结果表明了多变量模式分析方法成功地对ASPD患者和对照体进行了分类,并且ASPD患者最大的变化是静息态网络、注意网络、视觉认知网络和小脑这些网络之间的失连。该研究使用多模式分析方法成功地提取了反社会人格的异常信息,为反社会人格障碍这种高危人群的综合评估与识别提供了线索和方向。

Due to a very close link between antisocial personality disorder （ASPD） and criminal behavior, understanding the pathophysiology of ASPD is an international imperative. The objective of the present study is to develop a method of multivariate pattern analysis and investigate the altered functional connectivity patterns of ASPD by using rest-state functional magnetic resonance （MRI）. Our results show that multivariate pattern analysis can provides accurate classification between ASPD and control subjects, and the ASPD is motivated from the uncoupling among the default mode network, the attention network, the visual recognition network, and the cerebellar network. Moreover, the method can succeed to extract altered information of ASPD and provide the first evidence for the altered brain’s functional connections in ASPD.