Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection
Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection
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DOI：
10.1016/j.neuroimage.2005.08.009
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发表时间：
2005-11-15
期刊：
影响因子：
5.7
通讯作者：
Langleben, DD
中科院分区：
文献类型：
--
作者：
Davatzikos, C;Ruparel, K;Langleben, DD
欺骗过程中的大脑活动模式最近被表征与功能磁共振成像的多学科平均组水平。功能磁共振成像在测谎中的临床价值将取决于个体受试者的测谎能力，而不是群体平均水平。应用于功能性磁共振（fMRI）图像的高维非线性模式分类方法被用来区分与谎言和真相相关的大脑活动的空间模式。在22名参与者进行被迫选择欺骗任务，99%的真实和虚假的反应被正确区分。通过对未参加培训的参与者进行交叉验证评估，预测准确率为88%。研究结果表明，非线性机器学习技术在测谎和fMRI在个体受试者中的其他可能临床应用中具有潜力，并表明准确的临床测试可以基于fMRI对大脑功能的测量。(c)2005年爱思唯尔公司All rights reserved.
Patterns of brain activity during deception have recently been characterized with fMRI on the multi-subject average group level. The clinical value of fMRI in lie detection will be determined by the ability to detect deception in individual subjects, rather than group averages. High-dimensional non-linear pattern classification methods applied to functional magnetic resonance (fMRI) images were used to discriminate between the spatial patterns of brain activity associated with lie and truth. In 22 participants performing a forced-choice deception task, 99% of the true and false responses were discriminated correctly. Predictive accuracy, assessed by cross-validation in participants not included in training, was 88%. The results demonstrate the potential of non-linear machine learning techniques in lie detection and other possible clinical applications of fMRI in individual subjects, and indicate that accurate clinical tests could be based on measurements of brain function with fMRI. (c) 2005 Elsevier Inc. All rights reserved.