Multivariate pattern analysis of functional magnetic resonance imaging data reveals deficits in distributed representations in schizophrenia.
Multivariate pattern analysis of functional magnetic resonance imaging data reveals deficits in distributed representations in schizophrenia.
复制标题
功能磁共振成像数据的多元模式分析揭示了精神分裂症分布式表示的缺陷。
DOI：
10.1016/j.biopsych.2008.07.025
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发表时间：
2008-12-15
影响因子：
10.6
通讯作者：
Carter, Cameron S.
中科院分区：
文献类型：
--
作者：
Yoon, Jong H.;Tamir, Diana;Minzenberg, Michael J.;Ragland, J. Daniel;Ursu, Stefan;Carter, Cameron S.
关键词：
多元模式分析是分析功能磁共振成像数据的一种替代方法，它能够解码分布式神经表征。我们运用这种方法对精神分裂症患者的分布表征障碍假说进行了检验。我们还将该方法的结果与传统的基于glm的单变量分析结果进行了比较。19名精神分裂症患者和15名对照组患者观看了两组刺激——面部、场景、物体和混乱图像的范例。为了验证对刺激的参与，受试者完成了一个1-back匹配任务。训练一个多体素模式分类器来识别一次运行的fMRI数据上的特定类别的活动模式。在剩余的运行中进行了分类测试。体素活动的相关性评估了活动模式随时间的变化。患者完成任务的准确性较低。这一组差异反映在模式分析结果中，与对照组相比，患者的分类准确率分别降低了59%和72%。相比之下，基于glm的单变量测量没有组间差异。在两组中，分类准确性与行为测量显着相关。两组间相关性与分类准确率呈极显著相关。精神分裂症患者对视觉对象的分布式表征受损。这种损伤与任务表现下降相关，表明皮层活动模式完整性下降反映在行为受损上。与单变量结果的比较表明，模式分析在检测神经活动组差异方面具有更高的敏感性，并且降低了驱动这些结果的非特异性因素的可能性。
Multivariate pattern analysis is an alternative method of analyzing fMRI data, which is capable of decoding distributed neural representations. We applied this method to test the hypothesis of the impairment in distributed representations in schizophrenia. We also compared the results of this method with traditional GLM-based univariate analysis. 19 schizophrenia and 15 control subjects viewed two runs of stimuli--exemplars of faces, scenes, objects, and scrambled images. To verify engagement with stimuli, subjects completed a 1-back matching task. A multi-voxel pattern classifier was trained to identify category-specific activity patterns on one run of fMRI data. Classification testing was conducted on the remaining run. Correlation of voxel-wise activity across runs evaluated variance over time in activity patterns. Patients performed the task less accurately. This group difference was reflected in the pattern analysis results with diminished classification accuracy in patients compared to controls, 59% and 72% respectively. In contrast, there was no group difference in GLM-based univariate measures. In both groups, classification accuracy was significantly correlated with behavioral measures. Both groups showed highly significant correlation between inter-run correlations and classification accuracy. Distributed representations of visual objects are impaired in schizophrenia. This impairment is correlated with diminished task performance, suggesting that decreased integrity of cortical activity patterns is reflected in impaired behavior. Comparisons with univariate results suggest greater sensitivity of pattern analysis in detecting group differences in neural activity and reduced likelihood of non-specific factors driving these results.
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